WO2020107039A2

WO2020107039A2 - Methods and apparatus for communications with finite blocklength coded modulation

Info

Publication number: WO2020107039A2
Application number: PCT/US2019/068019
Authority: WO
Inventors: Chen Song; Guosen Yue
Original assignee: Futurewei Technologies, Inc.
Priority date: 2019-12-20
Filing date: 2019-12-20
Publication date: 2020-05-28
Also published as: WO2020107039A3

Abstract

A method includes obtaining a capacity gap and a dispersion scaling factor of the channel, the capacity gap being a difference between a theoretical capacity of the channel and a corrected capacity of the channel, and the dispersion scaling factor being a square root of a ratio of a corrected dispersion of the channel to a theoretical dispersion of the channel; estimating a channel capacity and a channel dispersion for the channel in accordance with the capacity gap, the dispersion scaling factor, a channel quality of the channel, and a modulation level of the channel; and determining a performance characteristic of the channel in accordance with the channel capacity of the channel, the channel dispersion of the channel, and a blocklength of a code used for transmissions over the channel.

Description

Methods and Apparatus for Communications With Finite Blocklength Coded Modulation

TECHNICAL FIELD

The present disclosure relates generally to methods and apparatus for digital communications, and, in particular embodiments, to methods and apparatus for communications with finite blocklength coded modulation.

BACKGROUND

In many wireless communications systems, the characterization of the wireless communication channel, e.g., link adaptation and symbol rate of the modulation and coding scheme (MCS) matching, plays an important role in determining how to transmit information reliably and effectively. Link adaptation is the adaptation of the modulation scheme and the symbol rate of the MCS in accordance with the quality of the radio link (i.e., the communications channel), while symbol rate of the MCS matching is the matching of the symbol rate of the MCS and the data rate used in the communication to the quality of the radio link. In the last few decades, wireless communications systems, including the current Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) compliant communications systems, use channel capacity to determine link adaptation, for example.

In a traditional wireless communications system where the codewords arising from channel coding are long (e.g., codewords in a communications system using a low- density parity check (LDPC) code or a turbo code are on the order of KU or to^ bits long), performing link adaptation and symbol rate of the MCS matching in accordance with the channel capacity is a reasonable practice.

However, there is an emerging need for short packet communication in Fifth Generation (5G) and later wireless communications. As an example, short packets are expected to be the majority of message traffic in ultra-reliable low latency communication (URLLC) and massive machine type communication in 5G. When short packets are used, predicting the symbol rate of the MCS merely on channel capacity is problematic because the channel capacity cannot provide an accurate estimate of real communications system

performance.

Furthermore, packets are also required to be delivered with a much lower error rate (e.g., block error rate (BLER)) in 5G. As an example, with URLLC, the BLER is expected to be as low as to A However, due to the low latency requirements of URLLC, on the order of 0.5 milli-seconds, traditional techniques, such as hybrid automatic repeat request (HARQ), are not applicable. Therefore, accurately predicting the symbol rate of the MCS of the wireless communication channel in order to meet the error rate and the latency requirement is crucial in the performance of 5G communications systems.

Therefore, there is a need for methods and apparatus for communications with finite blocklength coded modulation.

SUMMARY

According to a first aspect, a method implemented by a communicating device communicating over a channel is provided. The method comprising: obtaining, by the communicating device, a capacity gap and a dispersion scaling factor of the channel, the capacity gap being a difference between a theoretical capacity of the channel and a corrected capacity of the channel with a practical code with an infinite blocklength being used for transmissions over the channel, and the dispersion scaling factor being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel; estimating, by the communicating device, a channel capacity and a channel dispersion for the channel in accordance with the capacity gap, the dispersion scaling factor, a channel quality of the channel, and a modulation level of the channel;

determining, by the communicating device, a performance characteristic of the channel in accordance with the channel capacity of the channel, the channel dispersion of the channel, and a blocklength of a code used for transmissions over the channel; and communicating, by the communicating device, with another communicating device, in accordance with the performance characteristic of the channel.

In a first implementation form of the method according to the first aspect as such, the performance characteristic of the channel being a symbol rate of the channel, and the performance characteristic being further determined in accordance with an error rate of the channel.

In a second implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, determining the symbol rate of the channel comprising combining the channel capacity of the channel and the channel dispersion of the channel.

In a third implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, the performance characteristic of the channel being an error rate of the channel, and the performance characteristic being further determined in accordance with a symbol rate of the channel.

In a fourth implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, determining the error rate of the channel comprising combining the channel capacity of the channel and the channel dispersion of the channel.

In a fifth implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, obtaining the capacity gap and the dispersion scaling factor comprising: selecting, by the communicating device, the capacity gap from a plurality of capacity gaps, the selecting being in accordance with the channel quality of the channel; and selecting, by the communicating device, the dispersion scaling factor from a plurality of dispersion scaling factors, the selecting being in accordance with the channel quality of the channel.

In a sixth implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, further comprising receiving, by the communicating device, from a different device, the plurality of capacity gaps and the plurality of dispersion scaling factors.

In a seventh implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, further comprising: calculating, by the communicating device, the plurality of capacity gaps; and calculating, by the communicating device, the plurality of dispersion scaling factors.

In an eighth implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, obtaining the capacity gap and the dispersion scaling factor comprising receiving, by the communicating device, the capacity gap and the dispersion scaling factor from a different device.

In a ninth implementation form of the method according to the first aspect as such or any preceding implementation form of the first aspect, obtaining the capacity gap and the dispersion scaling factor comprising calculating, by the communicating device, the capacity gap and the dispersion scaling factor.

According to a second aspect, a method implemented by a communicating device communicating over a plurality of parallel channels is provided. The method comprising: obtaining, by the communicating device, capacity gaps and dispersion scaling factors for the plurality of parallel channels, a capacity gap of a channel being a difference between a theoretical capacity of the channel and a corrected capacity of the channel when a practical code with an infinite blocklength is used for transmissions over the channel, and a dispersion scaling factor of the channel being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel; estimating, by the communicating device, channel capacities and channel dispersions for the plurality of parallel channels in accordance with the capacity gaps, the dispersion scaling factors, channel qualities for the plurality of parallel channels, and modulation levels for the plurality of parallel channels; obtaining, by the communicating device, weighting factors for the plurality of parallel channels in accordance with a blocklength of a code used for transmissions over the plurality of parallel channels; determining, by the communicating device, performance characteristics for the plurality of parallel channels in accordance with the channel capacities, the channel dispersions, the weighting factors, and the blocklength of the code used for transmissions over the plurality of parallel channels; and communicating, by the communicating device, with another communicating device, in accordance with the performance characteristics of the plurality of parallel channels.

In a first implementation form of the method according to the second aspect as such, the performance characteristics being symbol rates, and the performance characteristics being further determined in accordance with error rates of the plurality of parallel channels.

In a second implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, the performance characteristics being error rates, and the performance characteristics being further determined in accordance with symbol rates of the plurality of parallel channels.

In a third implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, determining the error rates comprising iteratively determining the error rates until a convergence threshold is met or a predetermined number of iterations is met.

In a fourth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, determining the performance characteristics comprising applying the weighting factors to the channel capacities, and applying the weighting factors to the channel dispersions. In a fifth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, determining the performance characteristics comprising determining the performance characteristics in accordance with a combination of weighted channel capacities and weighted channel dispersions.

In a sixth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, the performance

characteristics being error rates of the plurality of parallel channels, and determining the performance characteristics comprising: determining, by the communicating device, the error rates in accordance with a combined symbol rate of the plurality of parallel channels, with the weighting factors set to p_k

updating, by the communicating device, the combined symbol rate, the weighting factors, the channel capacities, and the channel dispersions; updating, by the communicating device, the error rates in accordance with the channel capacities, and the channel dispersions; and repeating, by the communicating device, the updating the symbol rates, the weighting factors, the channel capacities, and the channel dispersions, and the updating the error rates until at least one of a convergence threshold is met or a predetermined number of iterations is reached.

In a seventh implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, determining the weighting factors comprising evaluating w_k = ^k^^Rk where fQ is a pre-specified function, R_k is

- j P jf iPj)

the symbol rate for a Ar-th channel, and /¾ =

In an eighth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, the pre-specified function/0 comprising f(R_k) = R ^a, where a is a parameter that is adjusted for the code.

In a ninth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, obtaining the weighting factors comprising calculating the weighting factors.

In a tenth implementation form of the method according to the second aspect as such or any preceding implementation form of the second aspect, obtaining the weighting factors comprising: transmitting, by the communicating device, to a different device, the channel capacities and the channel dispersions; and receiving, by the communicating device, from the different device, the weighting factors.

According to a third aspect, a communicating device communicating over a channel is provided. The communicating device comprising: a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory storage, wherein the one or more processors execute the instructions to: obtain a capacity gap and a dispersion scaling factor of the channel, the capacity gap being a difference between a theoretical capacity of the channel and a corrected capacity of the channel with a practical code with an infinite blocklength being used for transmissions over the channel, and the dispersion scaling factor being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel; estimate a channel capacity and a channel dispersion for the channel in accordance with the capacity gap, the dispersion scaling factor, a channel quality of the channel, and a modulation level of the channel; determine a performance characteristic of the channel in accordance with the channel capacity of the channel, the channel dispersion of the channel, and a blocklength of a code used for transmissions over the channel; and communicate, with another communicating device, in accordance with the performance characteristic of the channel.

In a first implementation form of the communicating device according to the third aspect as such, the performance characteristic of the channel being a symbol rate of the channel, and the performance characteristic being further determined in accordance with an error rate of the channel.

In a second implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to combine the channel capacity of the channel and the channel dispersion of the channel.

In a third implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the

performance characteristic of the channel being an error rate of the channel, and the performance characteristic being further determined in accordance with a symbol rate of the channel. In a fourth implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to combine the channel capacity of the channel and the channel dispersion of the channel.

In a fifth implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to select the capacity gap from a plurality of capacity gaps, the selecting being in accordance with the channel quality of the channel, and select the dispersion scaling factor from a plurality of dispersion scaling factors, the selecting being in accordance with the channel quality of the channel.

In a sixth implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to receive, from a different device, the plurality of capacity gaps and the plurality of dispersion scaling factors.

In a seventh implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to calculate the plurality of capacity gaps; and calculate the plurality of dispersion scaling factors.

In an eighth implementation form of the communicating device according to the third aspect as such or any preceding implementation form of the third aspect, the one or more processors further executing the instructions to receive the capacity gap and the dispersion scaling factor from a different device.

According to a fourth aspect, a communicating device communicating over a plurality of parallel channels is provided. The communicating device comprising: a non-transitoiy memory storage comprising instructions; and one or more processors in communication with the memory storage, wherein the one or more processors execute the instructions to: obtain capacity gaps and dispersion scaling factors for the plurality of parallel channels, a capacity gap of a channel being a difference between a theoretical capacity of the channel and a corrected capacity of the channel when a practical code with an infinite blocklength is used for transmissions over the channel, and a dispersion scaling factor of the channel being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel; estimate channel capacities and channel dispersions for the plurality of parallel channels in accordance with the capacity gaps, the dispersion scaling factors, channel qualities for the plurality of parallel channels, and modulation levels for the plurality of parallel channels; obtain weighting factors for the plurality of parallel channels in accordance with a blocklength of a code used for transmissions over the plurality of parallel channels; determine performance characteristics for the plurality of parallel channels in accordance with the channel capacities, the channel dispersions, the weighting factors, and the blocklength of the code used for transmissions over the plurality of parallel channels; and communicate, with another communicating device, in accordance with the performance characteristics of the plurality of parallel channels.

In a first implementation form of the communicating device according to the fourth aspect as such, the performance characteristics being symbol rates, and the performance characteristics being further determined in accordance with error rates of the plurality of parallel channels.

In a second implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the performance characteristics being error rates, and the performance characteristics being further determined in accordance with symbol rates of the plurality of parallel channels.

In a third implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to iteratively determine the error rates until a convergence threshold is met or a predetermined number of iterations is met.

In a fourth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to applying the weighting factors to the channel capacities, and applying the weighting factors to the channel dispersions.

In a fifth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to determine the performance characteristics in accordance with a combination of weighted channel capacities and weighted channel dispersions. In a sixth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the performance characteristics being error rates of the plurality of parallel channels, and the one or more processors further executing the instructions to determine the error rates in accordance with a combined symbol rate of the plurality of parallel channels, with the weighting factors set to p_k =

update the combined symbol rate, the weighting factors, the channel capacities, and the channel dispersions; update the error rates in accordance with the channel capacities, and the channel dispersions; and repeat the updating the symbol rates, the weighting factors, the channel capacities, and the channel dispersions, and the updating the error rates until at least one of a convergence threshold is met or a predetermined number of iterations is reached.

In a seventh implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to evaluate w_k = fQ

is a pre-specified function, R_k is the symbol rate for a k- th channel, and /¾ =

In an eighth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the pre specified function/Q comprising f(R_k) = R ^a, where a is a parameter that is adjusted for the code.

In a ninth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to calculate the weighting factors.

In a tenth implementation form of the communicating device according to the fourth aspect as such or any preceding implementation form of the fourth aspect, the one or more processors further executing the instructions to transmit, to a different device, the channel capacities and the channel dispersions; and receive, from the different device, the weighting factors.

An advantage of a preferred embodiment is that a computationally tractable technique for characterizing a wireless communication channel with finite blocklength coded modulation is provided. The relatively low computational requirements enable the dynamic characterization of the wireless communication channel to meet changing conditions and performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

Figure t illustrates an example communications system;

Figure 2 illustrates a channel characteristics interconnection unit;

Figure 3 illustrates an example link adaptation unit according to example embodiments presented herein;

Figure 4 illustrates a diagram of a communications system with parallel AWGN channels according to example embodiments presented herein;

Figure 5 illustrates a flow diagram of example operations of a multistep process for characterizing the symbol rate of the MCS of a wireless communications channel according to example embodiments presented herein;

Figure 6 illustrates a detailed view of an example link adaptation unit that characterizes the symbol rate of the MCS of wireless communications channel in accordance with input characteristics of the wireless communications channel according to example embodiments presented herein;

Figure 7 illustrates an example reliability optimization unit according to example embodiments presented herein;

Figure 8 illustrates a flow diagram of example operations of a multistep process for characterizing the error rate of a wireless communications channel according to example embodiments presented herein;

Figure 9 illustrates a detailed view of an example reliability optimization unit that characterizes the error rate of wireless communications channel in accordance with input characteristics of the wireless communications channel according to example embodiments presented herein;

Figure to illustrates an example communication system according to example embodiments presented herein; Figures nA and nB illustrate example devices that may implement the methods and teachings according to this disclosure; and

Figure 12 is a block diagram of a computing system that may be used for implementing the devices and methods disclosed herein.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The structure and use of disclosed embodiments are discussed in detail below. It should be appreciated, however, that the present disclosure provides many applicable concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed are merely illustrative of specific structure and use of embodiments, and do not limit the scope of the disclosure.

Figure t illustrates an example communications system too. Communications system too includes an access node 105 with coverage area 106. Access node 105 serves user equipments (UEs), such as UEs 110, and 112. Access node 105 provides connectivity between the UEs and a backhaul network 120. In a first operating mode,

communications to and from a UE passes through access node 105. In a second operating mode, communications to and from a UE do not pass through access node 105, however, access node 105 typically allocates resources used by the UE to communicate when specific conditions are met. Access nodes may also be commonly referred to as Node Bs, evolved Node Bs (eNBs), next generation (NG) Node Bs (gNBs), master eNBs (MeNBs), secondary eNBs (SeNBs), master gNBs (MgNBs), secondary gNBs (SgNBs), network controllers, control nodes, base stations, access points, transmission points (TPs), transmission-reception points (TRPs), cells, carriers, macro cells, femtocells, pico cells, and so on, while UEs may also be commonly referred to as mobile stations, mobiles, terminals, users, subscribers, stations, and the like. Access nodes may provide wireless access in accordance with one or more wireless communication protocols, e.g., the Third Generation Partnership Project (3GPP) long term evolution (LTE), LTE advanced (LTE- A), Fifth Generation (5G), 5G LTE, 5G NR, High Speed Packet Access (HSPA), the IEEE 802.11 family of standards, such as 802.na/b/g/n/ac/ad/ax/ay/be, etc. While it is understood that communications systems may employ multiple access nodes capable of communicating with a number of UEs, only one access node and two UEs are illustrated for simplicity.

Multiple use cases are to be supported in 5G. They include enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra- reliable low latency communications (URLLC). An intended goal for eMBB is to wirelessly deliver gigabytes of information per second, while mMTC supports smart cities and URLLC supports applications such as self-driving automobiles and mission critical applications. These use cases and others also support applications such as industrial automation, augmented reality, work and play in the cloud, 3D video, ultra-high definition displays, smart homes or buildings, voice applications, and so forth. The wide range of services offered in 5G have corresponding different requirements on the quality of service (QoS), e.g., throughput (e.g., data rate), reliability (e.g., block error rate (BLER)), and latency, with different transmission characteristics (such as, blocklength, modulation, coding schemes, and so on).

Shorter blocklength codes may be used in 5G to achieve: for URLLC - latency on the order of to milli-seconds and 0.5 milli-seconds physical (PHY) layer latency, different levels of reliability (e.g., BLER) for different applications, different packet sizes (e.g., to bytes to hundreds of bytes in size), and no retransmission or limited number of retransmissions; for mMTC - short packets, low latency, and media access control (MAC); and for eMBB - different layer mapping for multiple input multiple output (MIMO) operation.

Therefore, the characterization of the wireless communications channel is critical to the performance of 5G, in particular, URLLC, mMTC, and eMBB. As discussed herein, the characterization of the wireless communications channel may involve performing link adaptation for the wireless communications channel, symbol rate of the modulation and coding scheme (MCS) matching for the wireless communications channel, error rate determination for the wireless communications channel, or a combination thereof.

Existing attempts to interconnect characteristics of the wireless communications channel (throughput (e.g., symbol rate of the MCS used for the communications channel), error rate (e.g., BLER), and blocklength) have been limited to utilizing an infinite blocklength assumption to determine either the throughput or the error rate, and then applying the result to a finite value blocklength. These solutions involve brute force simulations, which makes implementing the solutions in real-time or computationally limited devices, such as UEs or access nodes, impractical.

The interconnecting of the characteristics of the wireless communications channels means that given a subset of known characteristics, it is possible to determine some of the other characteristics. As an illustrative example, if the symbol rate of the MCS, blocklength, and channel quality are known characteristics, it is possible to determine the error rate of the wireless communications channel. As another illustrative example, if the error rate, blocklength, and channel quality are known characteristics, it is possible to determine the symbol rate of the MCS of the wireless communications channel.

Figure 2 illustrates a channel characteristics interconnection unit 200. Channel characteristics interconnection unit 200 has, as input, one or more channel

characteristics, and as output, one or more channel characteristics. Examples of channel characteristics include, but are not limited to, symbol rate of the MCS, blocklength, channel quality, and error rate.

Although the assumption of infinite blocklength codes can be useful, the application of results derived for wireless communications channels with infinite blocklength codes to those with finite blocklength codes has yielded inaccurate results. Furthermore, MIMO and orthogonal frequency division multiplexed (OFDM) operation requires the combining of parallel channels into one codeword with a single coded modulation format. Additionally, parallel channels require the support of sub-channels with arbitrary combinations of signal to noise ratios (SNRs) and blocklengths. When coded modulation is used, channel quality disparity in parallel channels has a biased impact on receiver performance with some codes. Hence, supporting parallel channels further complicates the task and makes the interconnecting of the characteristics of the wireless

communications channel intractable.

According to an example embodiment, methods and apparatus for characterizing wireless communications channels are provided. The example embodiments are not computationally intensive, allowing for real-time implementations or implementations in computationally limited devices. The example embodiments are operable with arbitrary combinations of channel characteristics, such as channel quality, symbol rate of the MCS, error rate, and blocklength. Furthermore, the example embodiments are applicable to a wide range of coding and modulation schemes, including the tuning of some parameters but not overhauling the basic structure. Although the example embodiments presented herein focus on wireless communications channels, the example embodiments are also operable with wired communications channels. Therefore, the discussion of wireless communications channels should not be construed as limiting the scope of the example embodiments.

According to an example embodiment, methods and apparatus for characterizing the symbol rate of the MCS of a wireless communications channel in accordance with other characteristics of the wireless communications channel is provided. The other characteristics include error rate, blocklength, and channel quality, for example. In an embodiment, a model of the wireless communications channel with characteristics is provided. The model may be tuned based on simulation data. The simulation data may be derived from relatively simple, non-computationally intensive simulations, thereby enabling real-time implementation of the methods and apparatus. In addition to being performed in real-time, the example embodiments presented herein are also applicable in offline applications, where the simulation data is determined in offline simulations and used to tune the model (which may be performed in real-time or offline). If the model is tuned offline, the results are stored for subsequent use.

Figure 3 illustrates an example link adaptation unit 300. Link adaptation unit 300 determines the symbol rate of the MCS of a wireless communications channel in accordance with input characteristics of the wireless communications channel, including error rate, blocklength, and channel quality. The error rate of the wireless

communications channel maybe a BLER, frame error rate (FER), bit error rate (BER), packet error rate (PER), and so on. The blocklength is the blocklength of the coded symbols used to encode transmissions, and can range from short (for example, on the order of tens or hundreds of bits long) to long (for example, on the order of hundreds or thousands of bits long) depending on the code used. The channel quality is an indicator of the quality of the wireless communications channel. Examples of the channel quality indicator include SNR, signal plus interference to noise ratio (SINR), channel quality indicator (CQI), reference signal received quality (RSRQ), and so on. Although link adaptation unit 300 is shown as a single unit, some of the operations performed by link adaptation unit 300 may be performed at other devices or units, and the results provided back to link adaptation unit 300.

Although the discussion focuses on a wireless communications channel, the example embodiments presented herein are operable with parallel wireless communications channels. Therefore, the discussion of a wireless communications channel should not be construed as being limiting to the scope of the example embodiments.

In an embodiment, a model of the symbol rate of the MCS of the wireless

communications channel is expressible as

where W is the channel quality; CAWGN(£2) is the channel capacity as a function of W for an additive white Gaussian noise (AWGN) channel or parallel AWGN channels; DO(W) is the channel capacity gap in the infinite blocklength regime for a practical code as a function of W; VWVGN(G) is the channel dispersion as a function of W for an AWGN channel or parallel AWGN channels; g(W) is the channel dispersion scaling factor in the finite blocklength regime for a practical code as a function of W; n is the blocklength of the code; Q^~1Q is the inverse of the QQ function, which is the tail distribution function of the standard normal distribution and is defined as Q (x) =

and e is the error rate. W is a scalar for a single AWGN channel or a vector for parallel AWGN channels. In the case of parallel AWGN channels, each term in equation (l) is a vector with entries being the result of element-wise operations. A practical code is an actual code with a finite blocklength used in a transmission, as opposed to a theoretical infinite blocklength code. A practical code may be any finite length error correction code, such as a turbo code, a polar code, a low-density parity check (LDPC) code, etc.

The capacity gap (DO(W)) is the difference between the theoretical channel capacity and the maximum rate achievable by a practical code with infinite blocklength. The capacity gap is a correction that captures the sub-optimality of the code. The channel dispersion scaling factor (g(W)) is the square root of a ratio of the channel dispersion achievable by a practical code with finite blocklength to the theoretical channel dispersion. The channel dispersion scaling factor is a correction that captures the sub-optimality of the code. In general, for a capacity achieving code, the channel dispersion scaling factor should be g(W) > 1.

In a situation where parallel AWGN channels are being considered instead of a single AWGN channel, the channel capacity and the channel dispersion may be expressed as a linear combination of individual AWGN channels. Figure 4 illustrates a diagram of a communications system 400 with parallel AWGN channels. As shown in Figure 4, communications system 400 includes parallel AWGN channels 410 that comprise K AWGN channels, e.g., AWGN 415, AWGN₂ 417, and AWGN_K 419 (amongst others). Parallel AWGN channels 410 may be decomposed into the K AWGN channels. Each of the K AWGN channels is represented as having a potentially different channel quality, e.g., SNR 416, SNR₂ 418, and SNR_K 420 (amongst others). When parallel AWGN channels are being considered, the combined corrected channel capacity, the channel dispersion, and the final combined rate may be expressed as

(2)

(3) and

where w_k is a weighting factor for AWGN channel k, C (fl ) is the corrected channel capacity for the Ar-th AWGN channel, V (Ci_k) is the corrected channel dispersion for the k- th AWGN channel, C_comb is the combined corrected channel capacity, V_comb is the combined corrected channel dispersion, and R_COm_b is the final combined rate. The corrected channel capacity C (fi_k) and the corrected channel dispersion V (fl_k) include the corrections provided by the capacity gap (Dΰ(W_/0) and the channel dispersion scaling factor (y(il_/c)). A detailed discussion is provided below. The weighting factor for channel capacity and the channel dispersion may be different for any given AWGN channel.

In an embodiment, a multistep process is used to characterize the symbol rate of the MCS of a wireless communications channel. In an embodiment, the model of the symbol rate of the MCS of the wireless communications channel (Equation (i)) is used to characterize the symbol rate of the MCS of the wireless communications channel in accordance with other characteristics of the wireless communications channel (e.g., error rate, blocklength, and channel quality). The model is applicable to situations where the wireless communications channel is an AWGN channel or parallel AWGN channels.

Figure 5 illustrates a flow diagram of example operations 500 of a multistep process for characterizing the symbol rate of the MCS of a wireless communications channel.

Operations 500 may be indicative of operations occurring in the characterization of the symbol rate of the MCS of a wireless communications channel. Operations 500 may be implemented in either end of a communicating devices pair that is communicating over the wireless communications channel, for example. Alternatively, operations 500 may be implemented in a device that is not one of the two communicating devices

communicating over the wireless communications channel. As an example, portions of operations 500 may be implemented in a unit or device that are not co-located with one of the two communicating devices, and results of operations 500 (not including the actual communications) are sent to one or both of the two communicating devices, and utilized by the communicating devices. The results may be stored at the communicating devices, in a memory, for example. Alternatively, operations 500 maybe implemented for characterizing the symbol rate of the MCS of a wired communications channel.

Operations 500 begin with the device obtaining a channel capacity gap and a channel dispersion scaling factor (block 505). The device obtains the channel capacity gap (DO(W)) and the channel dispersion scaling factor (t(W)), each for a variety of W and error rates. In other words, a plurality of channel capacity gaps and a plurality of channel dispersion scaling factors are obtained by the device. As an example, a channel capacity gap and a channel dispersion scaling are obtained for different combinations of W and error rates, resulting in the plurality of channel capacity gaps and the plurality of channel dispersion scaling factors. In a situation where there is a single W and error rate, a single channel capacity gap and a single channel dispersion scaling factor are obtained by the device.

As discussed previously, the gap and the scaling factor are measures of a difference in performance achieved by an ideal code and a practical code in both infinite (i.e., very long) and finite blocklength regimes. A detailed discussion of an example of how the device obtains the channel capacity gap and the channel dispersion scaling factor is provided below. The channel capacity gap and the channel dispersion scaling factor may be obtained a priori and stored in the communications device for subsequent use. The channel capacity gap and the channel dispersion scaling factor may be determined (or calculated) by a communications device actually participating in the communications or received from a different device or unit not actually communicating over the wireless communication channel.

In the situation where the channel capacity gap and the channel dispersion scaling factor are determined by a different device, the values may be shared with the communications devices participating in the communications, for example. As an example, the different device or unit, not co-located with either of the communications devices participating in the communications, may determine (or calculate) the channel capacity gap and the channel dispersion scaling factor, and send the values to either or both of the communications devices participating in the communications. The values may be stored in a memory, for example, for subsequent use. The different device or unit may determine or update the channel capacity gap and the channel dispersion scaling factor at scheduled intervals, upon receipt of an instruction to do so, or when a performance metric meets a specified threshold. Whenever the channel capacity gap or the channel dispersion scaling factor changes, the different device or unit may update either or both of the communications devices participating in the communications.

As an illustrative example, the determination of the channel capacity gap may be as follows:

- Select a sufficiently large blocklength n_max as an approximation for the infinite blocklength of the code;

- For each symbol rate of the MCS R₀ of interest, determine a corresponding W₀ that yields an error rate that meets an error rate threshold (e.g., error rate = o.t). Simulation may be used, for example;

- Determine a theoretical channel capacity CAWGN( _O) according to the following equation:

where Z is a random variable that follows a zero-mean complex Gaussian distribution with unit variance, and (. ) are normalized constellation points of an m-aiy phase shift keying (m-PSK) or quadrature amplitude modulation (m-QAM) input with average power W; and

- Determine the channel capacity gap as AC (W₀) = C_AWCN(Ci₀) - R₀, and determine a smooth curve Dΰ(W) using interpolation, e.g., linear interpolation.

As an illustrative example, the determination of the channel dispersion scaling factor may be as follows:

- Select a set of blocklengths S = [n_L, ... , n_r and a set of error rates 8 =

{ P_el , ... , P_er2} as reference sets. The respective sets can be small, e.g., less than to entries each;

- For each error rate P_e in the set of error rates 8, and for each symbol rate of the MCS (R ) of interest, determine a corresponding W^ that results in an error rate substantially equal to P_e. Simulation may be used, for example;

- Determine a theoretical channel capacity C civin^) according to equation (4), as well as a theoretical channel dispersion

according to the following equation:

- Find a ythat minimizes the expression below:

to determine y^), and determine a smooth curve y(fl) using interpolation, e.g., linear interpolation.

The device performs subchannel quality estimation (block 507). Subchannel quality estimation involves the device estimating the channel capacity C and the channel dispersion V. The subchannel quality estimation may be performed for finite and infinite blocklength codes. The channel capacity C and the channel dispersion V may be estimated for the channel quality of the wireless communications channel. In the situation where parallel AWGN channels are considered, the channel capacity C and the channel dispersion V are estimated for the channel qualities of each of the parallel AWGN channels, resulting in a plurality of channel capacities and a plurality of channel dispersions. As an example, if there are four parallel AWGN channels, then there may be up to four different channel capacities and four channel dispersions, one for each of the four parallel AWGN channels. The corrected channel capacity C and the corrected channel dispersion V are results of the theoretical channel capacity, channel dispersion, and the channel capacity gap and channel dispersion scaling factor. In an embodiment, the corrected channel capacity C and the corrected channel dispersion V are estimated as

£(W_[) = C_AWGN(ili )— AC (Hi) (7) and

v(a = r²(n_i)v_AlVGN(a_i). (8)

The device optionally obtains weighting factors w_k (block 509). In the situation where parallel AWGN channels are considered, the device determines (or calculates) the weighting factors used to weigh a linear combination of the different AWGN channels of the parallel AWGN channels, such as shown in equations (2) and (3). The weighting factors may be a function of the blocklength of the code, as well as the error rate. The weighting factors may be determined or calculated in accordance with expression:

where p_k

fQ is a pre-specified function, and R_k

In an embodiment, /(¾) = R_k ^a, where a ³ 0 is a tunable parameter.

In an embodiment, the different device or unit that determined the channel capacity gap and the channel dispersion scaling factor may also determine or calculate the weighting factors w_k and sends the weighting factors w_k to either or both of the devices participating in the communications. In an embodiment, yet another device or unit determines or calculates the weighting factors w_k.

The device performs rate combining to determine the symbol rate of the MCS (block 511). The symbol rate of the MCS, as determined through rate combining, is a characterization of the wireless communications channel in accordance with other characteristics of the wireless communications channel, e.g., channel quality, block length, and error rate. As an illustrative example, rate combining may be performed by linearly combining the channel capacity (e.g., equation (2)) and the channel dispersion (e.g., equation (3)) and then the model (equation (1)) is used to determine the symbol rate of the MCS. Other techniques may be used to perform rate combining. In a situation where a single AWGN channel is considered, the device predicts a symbol rate of the MCS in accordance with a given blocklength and channel quality. As an example, the device uses the model (equation (1)) to predict the symbol rate of the MCS. In a situation where parallel AWGN channels are considered, the device predicts a symbol rate of the MCS for an arbitrary combination of blocklengths (n₁ , ... , n_K) with a total length n = å ₌₁ n_k and corresponding channel qualities (W₁ ... , l_K) using the model (equation (i)). Additional correction may be used depending on the code used.

The device communicates in accordance with the symbol rate of the MCS and other characteristics of the wireless communications channel, such as error rate, blocklength, channel quality, and so forth (block 513).

Figure 6 illustrates a detailed view of an example link adaptation unit 600 that characterizes the symbol rate of the MCS of wireless communications channel in accordance with input characteristics of the wireless communications channel, including error rate, blocklength, and channel quality. Link adaptation unit 600 may implement the multistep process for characterizing the symbol rate of the MCS of a wireless communications channel shown in Figure 5, for example. Although link adaptation unit 600 is described as being a single unit, some of the operations performed by link adaptation unit 600 may be performed at other devices or units, with the results being communicated to link adaptation unit 600. In such a situation, link adaptation unit 600 may store the results, received from the other devices or units, in a memory, for example, for subsequent use.

Link adaptation unit 600 has, as inputs, one or more channel quality values for the wireless communications channel, a modulation format used on the wireless

communications channel, one or more blocklength values for the code used on the wireless communications channel, and one or more error rates. Link adaptation unit 600 has, as output, a symbol rate of the MCS for the wireless communications channel, determined in accordance with the various values of the inputs of link adaptation unit 600.

Link adaptation unit 600 includes a channel capacity gap unit 605 and a channel dispersion scaling factor unit 607. In an embodiment, channel capacity gap unit 605 and channel dispersion scaling factor unit 607 are configured to determine the channel capacity gap and the channel dispersion scaling factor, as described in Figure 5, for example. In another embodiment, channel capacity gap unit 605 and channel dispersion scaling factor unit 607 are configured to retrieve the channel capacity gap and the channel dispersion scaling factor for a particular channel quality value from a memory, for example. In such an embodiment, the channel capacity gap and the channel dispersion scaling factor may have been determined by some other device or unit and communicated to link adaptation unit 600, where channel capacity gap unit 605 and channel dispersion scaling factor unit 607 can retrieve the channel capacity gap and the channel dispersion scaling factor as needed.

Link adaptation unit 600 also includes a subchannel quality estimation unit 609.

Subchannel quality estimation unit 609 is configured to determine estimates of the channel capacity (C) and channel dispersion (V) for the wireless communications channel for the channel quality of the wireless communications channel. Subchannel quality estimation unit 609 determines estimates for the channel capacity and the channel dispersion as described in Figure 5, using equations (7) and (8), for example. Subchannel quality estimation unit 609 obtains the channel capacity gap and the channel dispersion scaling factor from channel capacity gap unit 605 and channel dispersion scaling factor unit 607. Subchannel quality estimation unit 609 includes units for determining the theoretical channel capacity, the theoretical channel dispersion, the corrected channel capacity, and the corrected channel dispersion.

The channel capacity and the channel dispersion are provided to a weighting factor determining unit 611, which, when parallel AWGN channels are considered, generates the weighting factors used in the linear combination of the channel capacities and channel dispersions for each AWGN channel making up the parallel AWGN channels.

The weighting factors may be determined using equation (9), for example. If a single AWGN channel is considered, weighting factor determining unit 611 maybe inactive.

In an embodiment, the weighting factors are determined by a different device or unit. In such an embodiment, the channel capacity and the channel dispersion are provided (e.g., communicated) to the different device and unit. The different device or unit determines the weighting factors using equation (9), for example, and communicates the results back to link adaptation unit 600.

A rate combining unit 613 determines the symbol rate of the MCS of the wireless communications channel. In a situation where a single AWGN channel is considered, the device predicts a symbol rate of the MCS in accordance with a given blocklength and channel quality. As an example, the device uses the model (equation (1)) to predict the symbol rate of the MCS. In a situation where parallel AWGN channels are considered, the device predicts a symbol rate of the MCS for an arbitrary combination of blocklengths (n , ... , n_K) with a total length n = å ₌₁ n_k and corresponding channel qualities

(W_1; ... , Gί_k) using the model (equation (1)).

Figure 7 illustrates an example reliability optimization unit 700. Reliability optimization unit 700 determines an error rate, such as BLER, BER, FER, PER, and so on, in accordance with input characteristics of the wireless communications channel, including symbol rate of the MCS , blocklengths, and channel quality. The symbol rate of the MCS is related to the throughput of the wireless communications channel, specifying the amount of information that can be carried over the wireless communications channel. The blocklength is the blocklength of the coded symbols used to encode transmissions, and can range from short (for example, on the order of tens or hundreds of bits long) to long (for example, on the order of hundreds or thousands of bits long) depending on the code used. The channel quality is an indicator of the quality of the wireless

communications channel. Examples of the channel quality indicators include SNR, SINR, CQI, RSRQ, and so on. Although the discussion focusses on a wireless communications channel, the example embodiments presented herein are operable with other

communications channels, such as digital subscriber lines (DSL) and optical

communication channels. Therefore, the discussion of a wireless communications channel should not be construed as being limiting to the scope of the example embodiments.

Although reliability optimization unit 700 is shown as a single unit, some of the operations performed by reliability optimization unit 700 may be performed at other devices or units, and the results provided back to reliability optimization unit 700.

In an embodiment, the model of the symbol rate of the MCS of the wireless

communications channel presented in equation (1) is re-expressed to determine the error rate, as shown below

where Q() is the Q function, and R is the symbol rate of the MCS of the wireless communications channel.

In the situation where a single AWGN channel is considered, the error rate is

reformulated as follows

In the situation where parallel AWGN channels are considered, the linear combination of the channel capacity and the channel dispersion must be considered. Recall that the channel capacity and the channel dispersion are expressible as

and

where w is a weighting factor, and C (W_έ) and V (W_έ) are obtained from equations (7) and (8), respectively. In an embodiment, because R_k depends on the error rate, R_k may be evaluated iteratively. As an example, an initial error rate e₀ may be determined using an uncorrected weighting factor w_k = _Pk as e₀ = then R_k is determined as follows

k_R ^~a

with each iteration, update w_k = _{å jr p} ^k _RTa, the channel capacity and channel dispersion as weighted sums, and the error rate

In an embodiment, a is chosen as a function of the standard deviation among the R_k s.

Figure 8 illustrates a flow diagram of example operations 800 of a multistep process for characterizing the error rate of a wireless communications channel. Operations 800 may be indicative of operations occurring in the characterization of the error rate of a wireless communications channel. Operations 800 may be implemented in either end of a communicating devices pair that is communicating over the wireless communications channel, for example. Alternatively, operations 800 may be implemented in a device that is not one of the two communicating devices communicating over the wireless communications channel. As an example, portions of operations 800 may be

implemented in a unit or device that are not co-located with one of the two

communicating devices, and results of operations 800 (not including the actual communications) are sent to one or both of the two communicating devices, and utilized by the communicating devices. The results may be stored at the communicating devices, in a memory, for example. Alternatively, operations 800 may be implemented in a system-level simulation to provide data usable in tuning characteristics of a

communications channel. Alternatively, operations 800 may be implemented for characterization of the error rate of a wired communications channel.

Operations 800 begin with the device obtaining a channel capacity gap and a channel dispersion scaling factor (block 805). The device obtains the channel capacity gap (D(ϋ(W)) and the channel dispersion scaling factor (g(W)), each for a variety of W and error rates. As an example, the device determines or calculates the channel capacity gap and the channel dispersion scaling factor as described in the discussion of block 505 of Figure 5. Alternatively, another device or unit determines or calculates the channel capacity gap and the channel dispersion scaling factor and provides the results to the device. The results may be stored in a memory, for example. The device performs subchannel quality estimation (block 807). Subchannel quality estimation involves the device estimating the channel capacity C and the channel dispersion V. As an example, the device determines the channel capacity and the channel dispersion as described in the discussion of block 507 of Figure 5.

The device optionally obtains weighting factors w_k (block 809). In the situation where parallel AWGN channels are considered, the device determines the weighting factors used to weigh a linear combination of the different AWGN channels of the parallel AWGN channels, such as shown in equations (2) and (3). The weighting factors may be a function of the blocklength of the code, as well as the error rate. In an embodiment, the different device or unit that determined the channel capacity gap and the channel dispersion scaling factor may also determine or calculate the weighting factors w_k and sends the weighting factors w_k to either or both of the devices participating in the communications. In an embodiment, yet another device or unit determines or calculates the weighting factors w_k.

The device determines the error rate (block 811). In a situation where a single AWGN channel is considered, the error rate is determined in accordance with equation (12). In a situation where parallel AWGN channels are considered, an iterative process (such as one described above) is utilized to determine the error rate. The device performs a check to determine if the error rate has converged (block 813). As an example, the error rate has converged if a difference between two consecutive iterations of the error rate changes by less than a convergence threshold. The convergence threshold may be specified in a technical standard, or set by an operator of the communications system, for example. If the error rate has not converged, the device returns to block 811 to perform another iteration of determining the error rate. If the error rate has converged, the device communicates using the symbol rate of the MCS and other characteristics of the wireless communications channel, such as error rate, blocklength, channel quality, and so forth (block 815).

The error rate may also be determined using an iterative process.

1) An initial error rate is estimated from the symbol rate of the MCS with the weighting factors w_k set to p_k (using equation (to), for example);

2) Update the symbol rate of the MCS, the weighting factors w_k for each channel of the parallel AWGN channels, the combined channel capacity, and the combined channel dispersion;

3) Update the error rate in accordance with the combined channel capacity and the combined channel dispersion (again, using equation (to), for example); and

4) Repeat 2) and 3) until the error rate has converged (consecutive error rates differ by less than a specified threshold, for example) or a predetermined number of iteration is reached.

Figure 9 illustrates a detailed view of an example reliability optimization unit 900 that characterizes the error rate of wireless communications channel in accordance with input characteristics of the wireless communications channel, including symbol rate of the MCS, blocklength, and channel quality. Reliability optimization unit 900 may implement the multistep process for characterizing the error rate of a wireless communications channel shown in Figure 8, for example. Although reliability optimization unit 900 is described as being a single unit, some of the operations performed by reliability optimization unit 900 may be performed at other devices or units, with the results being communicated to reliability optimization unit 900. In such a situation, reliability optimization unit 900 may store the results, received from the other devices or units, in a memory, for example, for subsequent use.

Reliability optimization unit 900 has, as inputs, one or more channel quality values for the wireless communications channel, a modulation and coding scheme used on the wireless communications channel, and one or more blocklength values for the code used on the wireless communications channel. Reliability optimization unit 900 has, as output, an error rate for the wireless communications channel, determined in accordance with the various values of the inputs of reliability optimization unit 900.

Reliability optimization unit 900 includes a channel capacity gap unit 905 and a channel dispersion scaling factor unit 907. In an embodiment, channel capacity gap unit 905 and channel dispersion scaling factor unit 907 are configured to determine the channel capacity gap and the channel dispersion scaling factor, as described in Figure 8, for example. In another embodiment, channel capacity gap unit 905 and channel dispersion scaling factor unit 907 are configured to retrieve the channel capacity gap and the channel dispersion scaling factor for a particular channel quality value from a memory, for example. In such an embodiment, the channel capacity gap and the channel dispersion scaling factor may have been determined by some other device or unit and communicated to reliability optimization unit 900, where channel capacity gap unit 905 and channel dispersion scaling factor unit 907 can retrieve the channel capacity gap and the channel dispersion scaling factor as needed.

Reliability optimization unit 900 also includes a subchannel quality estimation unit 909. Subchannel quality estimation unit 909 is configured to determine estimates of the channel capacity (C) and channel dispersion (V) for the wireless communications channel for the channel quality of the wireless communications channel. Subchannel quality estimation unit 909 determines estimates for the channel capacity and the channel dispersion as described in Figure 8, using equations (7) and (8), for example. Subchannel quality estimation unit 909 obtains the channel capacity gap and the channel dispersion scaling factor from channel capacity gap unit 905 and channel dispersion scaling factor unit 907. Subchannel quality estimation unit 909 includes units for determining the theoretical channel capacity, the theoretical channel dispersion, the corrected channel capacity, and the corrected channel dispersion.

Reliability optimization unit 900 also includes a rate unit 911. Rate unit 911 is configured to determine the symbol rate of the MCS from the modulation and coding scheme provided as an input to reliability optimization unit 900.

The channel capacity and the channel dispersion are provided to a weighting factor determining unit 913, which, when parallel AWGN channels are considered, generates the weighting factors used in the linear combination of the channel capacities and channel dispersions for each AWGN channel making up the parallel AWGN channels. If a single AWGN channel is considered, weighting factor determining unit 911 maybe inactive.

In an embodiment, the weighting factors are determined by a different device or unit. In such an embodiment, the channel capacity and the channel dispersion are provided (e.g., communicated) to the different device and unit. The different device or unit determines the weighting factors using equation (9), for example, and communicates the results back to reliability optimization unit 900.

An error rate determining unit 915 determines the error rate of the wireless

communications channel. In a situation where a single AWGN channel is considered, the device determines the error rate in accordance with a given blocklength and channel quality. As an example, the device uses equation (11) to determine the error rate. In a situation where parallel AWGN channels are considered, the device determines the error rate for an arbitrary combination of blocklengths (n_L, ... , n_K) with a total length n = å_fc=i ⁿk and corresponding channel qualities (W₁ ... , Ci_K) using the iterative process discussed above. Reliability optimization unit 900 iteratively determines the error rate until the error rate converges, e.g., consecutive iteratively determined error rates are less than a convergence threshold.

Figure to illustrates an example communication system 1000. In general, the system 1000 enables multiple wireless or wired users to transmit and receive data and other content. The system 1000 may implement one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), or non-orthogonal multiple access (NOMA).

In this example, the communication system 1000 includes electronic devices (ED) 1010a- 1010c, radio access networks (RANs) I020a-t020b, a core network 1030, a public switched telephone network (PSTN) 1040, the Internet 1050, and other networks 1060. While certain numbers of these components or elements are shown in Figure to, any number of these components or elements may be included in the system 1000.

The EDs loioa-iotoc are configured to operate or communicate in the system 1000. For example, the EDs loioa-iotoc are configured to transmit or receive via wireless or wired communication channels. Each ED loioa-iotoc represents any suitable end user device and may include such devices (or may be referred to) as a user equipment or device (UE), wireless transmit or receive unit (WTRU), mobile station, fixed or mobile subscriber unit, cellular telephone, personal digital assistant (PDA), smartphone, laptop, computer, touchpad, wireless sensor, or consumer electronics device.

The RANs I020a-t020b here include base stations I070a-t070b, respectively. Each base station I070a-t070b is configured to wirelessly interface with one or more of the EDs loioa-iotoc to enable access to the core network 1030, the PSTN 1040, the Internet 1050, or the other networks 1060. For example, the base stations I070a-t070b may include (or be) one or more of several well-known devices, such as a base transceiver station (BTS), a Node-B (NodeB), an evolved NodeB (eNodeB), a Next Generation (NG) NodeB (gNB), a Home NodeB, a Home eNodeB, a site controller, an access point (AP), or a wireless router. The EDs loioa-iotoc are configured to interface and communicate with the Internet 1050 and may access the core network 1030, the PSTN 1040, or the other networks 1060.

In the embodiment shown in Figure to, the base station 1070a forms part of the RAN 1020a, which may include other base stations, elements, or devices. Also, the base station 1070b forms part of the RAN 1020b, which may include other base stations, elements, or devices. Each base station I070a-i070b operates to transmit or receive wireless signals within a particular geographic region or area, sometimes referred to as a“cell.” In some embodiments, multiple-input multiple-output (MIMO) technology maybe employed having multiple transceivers for each cell.

The base stations I070a-i070b communicate with one or more of the EDs loioa-ioioc over one or more air interfaces 1090 using wireless communication links. The air interfaces 1090 may utilize any suitable radio access technology.

It is contemplated that the system 1000 may use multiple channel access functionality, including such schemes as described above. In particular embodiments, the base stations and EDs implement 5G New Radio (NR), LTE, LTE-A, or LTE-B. Of course, other multiple access schemes and wireless protocols may be utilized.

The RANs I020a-i020b are in communication with the core network 1030 to provide the EDs loioa-ioioc with voice, data, application, Voice over Internet Protocol (VoIP), or other services. Understandably, the RANs I020a-i020b or the core network 1030 may be in direct or indirect communication with one or more other RANs (not shown). The core network 1030 may also serve as a gateway access for other networks (such as the PSTN 1040, the Internet 1050, and the other networks 1060). In addition, some or all of the EDs loioa-ioioc may include functionality for communicating with different wireless networks over different wireless links using different wireless technologies or protocols. Instead of wireless communication (or in addition thereto), the EDs may communicate via wired communication channels to a service provider or switch (not shown), and to the Internet 1050.

Although Figure 10 illustrates one example of a communication system, various changes may be made to Figure 10. For example, the communication system 1000 could include any number of EDs, base stations, networks, or other components in any suitable configuration.

Figures 11A and 11B illustrate example devices that may implement the methods and teachings according to this disclosure. In particular, Figure 11A illustrates an example ED 1110, and Figure 11B illustrates an example base station 1170. These components could be used in the system 1000 or in any other suitable system.

As shown in Figure 11A, the ED 1110 includes at least one processing unit 1100. The processing unit 1100 implements various processing operations of the ED 1110. For example, the processing unit tioo could perform signal coding, data processing, power control, input/output processing, or any other functionality enabling the ED mo to operate in the system tooo. The processing unit tioo also supports the methods and teachings described in more detail above. Each processing unit tioo includes any suitable processing or computing device configured to perform one or more operations. Each processing unit tioo could, for example, include a microprocessor, microcontroller, digital signal processor, field programmable gate array, or application specific integrated circuit.

The ED mo also includes at least one transceiver 1102. The transceiver 1102 is configured to modulate data or other content for transmission by at least one antenna or NIC (Network Interface Controller) 1104. The transceiver 1102 is also configured to demodulate data or other content received by the at least one antenna 1104. Each transceiver 1102 includes any suitable structure for generating signals for wireless or wired transmission or processing signals received wirelessly or by wire. Each antenna 1104 includes any suitable structure for transmitting or receiving wireless or wired signals. One or multiple transceivers 1102 could be used in the ED mo, and one or multiple antennas 1104 could be used in the ED mo. Although shown as a single functional unit, a transceiver 1102 could also be implemented using at least one transmitter and at least one separate receiver.

The ED mo further includes one or more input/output devices 1106 or interfaces (such as a wired interface to the Internet 1050). The input/output devices 1106 facilitate interaction with a user or other devices (network communications) in the network. Each input/output device 1106 includes any suitable structure for providing information to or receiving information from a user, such as a speaker, microphone, keypad, keyboard, display, or touch screen, including network interface communications.

In addition, the ED mo includes at least one memory 1108. The memory 1108 stores instructions and data used, generated, or collected by the ED mo. For example, the memory 1108 could store software or firmware instructions executed by the processing unit(s) 1100 and data used to reduce or eliminate interference in incoming signals. Each memory 1108 includes any suitable volatile or non-volatile storage and retrieval device(s). Any suitable type of memory may be used, such as random access memory (RAM), read only memory (ROM), hard disk, optical disc, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, and the like.

As shown in Figure 11B, the base station 1170 includes at least one processing unit 1150, at least one transceiver 1152, which includes functionality for a transmitter and a receiver, one or more antennas 1156, at least one memory 1158, and one or more input/output devices or interfaces 1166. A scheduler, which would be understood by one skilled in the art, is coupled to the processing unit 1150. The scheduler could be included within or operated separately from the base station 1170. The processing unit 1150 implements various processing operations of the base station 1170, such as signal coding, data processing, power control, input/output processing, or any other functionality. The processing unit 1150 can also support the methods and teachings described in more detail above. Each processing unit 1150 includes any suitable processing or computing device configured to perform one or more operations. Each processing unit 1150 could, for example, include a microprocessor, microcontroller, digital signal processor, field programmable gate array, or application specific integrated circuit.

Each transceiver 1152 includes any suitable structure for generating signals for wireless or wired transmission to one or more EDs or other devices. Each transceiver 1152 further includes any suitable structure for processing signals received wirelessly or by wire from one or more EDs or other devices. Although shown combined as a transceiver 1152, a transmitter and a receiver could be separate components. Each antenna 1156 includes any suitable structure for transmitting or receiving wireless or wired signals. While a common antenna 1156 is shown here as being coupled to the transceiver 1152, one or more antennas 1156 could be coupled to the transceiver(s) 1152, allowing separate antennas 1156 to be coupled to the transmitter and the receiver if equipped as separate components. Each memory 1158 includes any suitable volatile or non-volatile storage and retrieval device(s). Each input/output device 1166 facilitates interaction with a user or other devices (network communications) in the network. Each input/output device 1166 includes any suitable structure for providing information to or receiving/providing information from a user, including network interface communications.

Figure 12 is a block diagram of a computing system 1200 that may be used for implementing the devices and methods disclosed herein. For example, the computing system can be any entity of UE, access network (AN), mobility management (MM), session management (SM), user plane gateway (UPGW), or access stratum (AS). Specific devices may utilize all of the components shown or only a subset of the components, and levels of integration may vary from device to device. Furthermore, a device may contain multiple instances of a component, such as multiple processing units, processors, memories, transmitters, receivers, etc. The computing system 1200 includes a processing unit 1202. The processing unit includes a central processing unit (CPU) 1214, memory 1208, and may further include a mass storage device 1204, a video adapter 1210, and an I/O interface 1212 connected to a bus 1220. The bus 1220 may be one or more of any type of several bus architectures including a memory bus or memory controller, a peripheral bus, or a video bus. The CPU 1214 may comprise any type of electronic data processor. The memory 1208 may comprise any type of non-transitory system memory such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), read-only memory (ROM), or a combination thereof. In an embodiment, the memory 1208 may include ROM for use at boot-up, and DRAM for program and data storage for use while executing programs.

The mass storage 1204 may comprise any type of non-transitory storage device configured to store data, programs, and other information and to make the data, programs, and other information accessible via the bus 1220. The mass storage 1204 may comprise, for example, one or more of a solid state drive, hard disk drive, a magnetic disk drive, or an optical disk drive.

The video adapter 1210 and the I/O interface 1212 provide interfaces to couple external input and output devices to the processing unit 1202. As illustrated, examples of input and output devices include a display 1218 coupled to the video adapter 1210 and a mouse, keyboard, or printer 1216 coupled to the I/O interface 1212. Other devices may be coupled to the processing unit 1202, and additional or fewer interface cards may be utilized. For example, a serial interface such as Universal Serial Bus (USB) (not shown) may be used to provide an interface for an external device.

The processing unit 1202 also includes one or more network interfaces 1206, which may comprise wired links, such as an Ethernet cable, or wireless links to access nodes or different networks. The network interfaces 1206 allow the processing unit 1202 to communicate with remote units via the networks. For example, the network interfaces 1206 may provide wireless communication via one or more transmitters/transmit antennas and one or more receivers/ receive antennas. In an embodiment, the processing unit 1202 is coupled to a local-area network 1222 or a wide-area network for data processing and communications with remote devices, such as other processing units, the Internet, or remote storage facilities.

It should be appreciated that one or more steps of the embodiment methods provided herein may be performed by corresponding units or modules. For example, a signal may be transmitted by a transmitting unit or a transmitting module. A signal may be received by a receiving unit or a receiving module. A signal may be processed by a processing unit or a processing module. Other steps may be performed by a determining unit or module, or a selecting unit or module. The respective units or modules may be hardware, software, or a combination thereof. For instance, one or more of the units or modules may be an integrated circuit, such as field programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs).

Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope of the disclosure as defined by the appended claims.

Claims

WHAT IS CLAIMED IS:

1. A method implemented by a communicating device communicating over a channel, the method comprising:

obtaining, by the communicating device, a capacity gap and a dispersion scaling factor of the channel, the capacity gap being a difference between a theoretical capacity of the channel and a corrected capacity of the channel with a practical code with an infinite blocklength being used for transmissions over the channel, and the dispersion scaling factor being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel;

estimating, by the communicating device, a channel capacity and a channel dispersion for the channel in accordance with the capacity gap, the dispersion scaling factor, a channel quality of the channel, and a modulation level of the channel;

2. The method of claim t, the performance characteristic of the channel being a symbol rate of the channel, and the performance characteristic being further determined in accordance with an error rate of the channel.

3. The method of claim 2, determining the symbol rate of the channel comprising combining the channel capacity of the channel and the channel dispersion of the channel.

4. The method of claim t, the performance characteristic of the channel being an error rate of the channel, and the performance characteristic being further determined in accordance with a symbol rate of the channel.

5. The method of claim 4, determining the error rate of the channel comprising combining the channel capacity of the channel and the channel dispersion of the channel.

6. The method of any one of claims 1-5, obtaining the capacity gap and the dispersion scaling factor comprising:

selecting, by the communicating device, the capacity gap from a plurality of capacity gaps, the selecting being in accordance with the channel quality of the channel; and selecting, by the communicating device, the dispersion scaling factor from a plurality of dispersion scaling factors, the selecting being in accordance with the channel quality of the channel.

7. The method of claim 6, further comprising receiving, by the communicating device, from a different device, the plurality of capacity gaps and the plurality of dispersion scaling factors.

8. The method of claim 6, further comprising:

calculating, by the communicating device, the plurality of capacity gaps; and calculating, by the communicating device, the plurality of dispersion scaling factors.

9. The method of any one of claims 1-6, obtaining the capacity gap and the dispersion scaling factor comprising receiving, by the communicating device, the capacity gap and the dispersion scaling factor from a different device.

10. The method of any one of claims 1-6, obtaining the capacity gap and the dispersion scaling factor comprising calculating, by the communicating device, the capacity gap and the dispersion scaling factor.

11. A method implemented by a communicating device communicating over a plurality of parallel channels, the method comprising:

obtaining, by the communicating device, capacity gaps and dispersion scaling factors for the plurality of parallel channels, a capacity gap of a channel being a difference between a theoretical capacity of the channel and a corrected capacity of the channel when a practical code with an infinite blocklength is used for transmissions over the channel, and a dispersion scaling factor of the channel being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel;

estimating, by the communicating device, channel capacities and channel dispersions for the plurality of parallel channels in accordance with the capacity gaps, the dispersion scaling factors, channel qualities for the plurality of parallel channels, and modulation levels for the plurality of parallel channels;

obtaining, by the communicating device, weighting factors for the plurality of parallel channels in accordance with a blocklength of a code used for transmissions over the plurality of parallel channels;

determining, by the communicating device, performance characteristics for the plurality of parallel channels in accordance with the channel capacities, the channel dispersions, the weighting factors, and the blocklength of the code used for transmissions over the plurality of parallel channels; and

communicating, by the communicating device, with another communicating device, in accordance with the performance characteristics of the plurality of parallel channels.

12. The method of claim it, the performance characteristics being symbol rates, and the performance characteristics being further determined in accordance with error rates of the plurality of parallel channels.

13. The method of claim 11, the performance characteristics being error rates, and the performance characteristics being further determined in accordance with symbol rates of the plurality of parallel channels.

14. The method of claim 13, determining the error rates comprising iteratively determining the error rates until a convergence threshold is met or a predetermined number of iterations is met.

15. The method of any one of claims 12 or 13, determining the performance characteristics comprising applying the weighting factors to the channel capacities, and applying the weighting factors to the channel dispersions.

16. The method of claim 14, determining the performance characteristics comprising determining the performance characteristics in accordance with a combination of weighted channel capacities and weighted channel dispersions.

17. The method of claim 13, the performance characteristics being error rates of the plurality of parallel channels, and determining the performance characteristics comprising:

determining, by the communicating device, the error rates in accordance with a combined symbol rate of the plurality of parallel channels, with the weighting factors set ^t0 p* = ?;

updating, by the communicating device, the combined symbol rate, the weighting factors, the channel capacities, and the channel dispersions;

updating, by the communicating device, the error rates in accordance with the channel capacities, and the channel dispersions; and

repeating, by the communicating device, the updating the symbol rates, the weighting factors, the channel capacities, and the channel dispersions, and the updating the error rates until at least one of a convergence threshold is met or a predetermined number of iterations is reached.

18. The method of any one of claims 11-17, determining the weighting factors comprising evaluating fied function, R_k is the symbol rate for a Ar-th channel, and /¾ =

19. The method of claim 18, the pre-specified function/Q comprising /(¾) = ^R _k ^a _> where a is a parameter that is adjusted for the code.

20. The method of any one of claim 11-19, obtaining the weighting factors comprising calculating the weighting factors.

21. The method of any one of claims 11-19, obtaining the weighting factors comprising:

transmitting, by the communicating device, to a different device, the channel capacities and the channel dispersions; and

receiving, by the communicating device, from the different device, the weighting factors.

22. A communicating device communicating over a channel, the communicating device comprising:

a non-transitory memory storage comprising instructions; and

one or more processors in communication with the memory storage, wherein the one or more processors execute the instructions to:

obtain a capacity gap and a dispersion scaling factor of the channel, the capacity gap being a difference between a theoretical capacity of the channel and a corrected capacity of the channel with a practical code with an infinite blocklength being used for transmissions over the channel, and the dispersion scaling factor being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel;

estimate a channel capacity and a channel dispersion for the channel in accordance with the capacity gap, the dispersion scaling factor, a channel quality of the channel, and a modulation level of the channel;

determine a performance characteristic of the channel in accordance with the channel capacity of the channel, the channel dispersion of the channel, and a blocklength of a code used for transmissions over the channel; and

communicate, with another communicating device, in accordance with the performance characteristic of the channel.

23. The communicating device of claim 22, the performance characteristic of the channel being a symbol rate of the channel, and the performance characteristic being further determined in accordance with an error rate of the channel.

24. The communicating device of claim 23, the one or more processors further executing the instructions to combine the channel capacity of the channel and the channel dispersion of the channel.

25. The communicating device of claim 22, the performance characteristic of the channel being an error rate of the channel, and the performance characteristic being further determined in accordance with a symbol rate of the channel.

26. The communicating device of claim 25, the one or more processors further executing the instructions to combine the channel capacity of the channel and the channel dispersion of the channel.

27. The communicating device of any one of claims 22-26, the one or more processors further executing the instructions to select the capacity gap from a plurality of capacity gaps, the selecting being in accordance with the channel quality of the channel, and select the dispersion scaling factor from a plurality of dispersion scaling factors, the selecting being in accordance with the channel quality of the channel.

28. The communicating device of claim 27, the one or more processors further executing the instructions to receive, from a different device, the plurality of capacity gaps and the plurality of dispersion scaling factors.

29. The communicating device of claim 27, the one or more processors further executing the instructions to calculate the plurality of capacity gaps; and calculate the plurality of dispersion scaling factors.

30. The communicating device of any one of claims 22-27, the one or more processors further executing the instructions to receive the capacity gap and the dispersion scaling factor from a different device.

31. A communicating device communicating over a plurality of parallel channels, the communicating device comprising:

a non-transitory memory storage comprising instructions; and

obtain capacity gaps and dispersion scaling factors for the plurality of parallel channels, a capacity gap of a channel being a difference between a theoretical capacity of the channel and a corrected capacity of the channel when a practical code with an infinite blocklength is used for transmissions over the channel, and a dispersion scaling factor of the channel being a square root of a ratio of a corrected dispersion of the channel for a practical code with a finite blocklength used for transmissions over the channel to a theoretical dispersion of the channel;

estimate channel capacities and channel dispersions for the plurality of parallel channels in accordance with the capacity gaps, the dispersion scaling factors, channel qualities for the plurality of parallel channels, and modulation levels for the plurality of parallel channels;

obtain weighting factors for the plurality of parallel channels in accordance with a blocklength of a code used for transmissions over the plurality of parallel channels;

determine performance characteristics for the plurality of parallel channels in accordance with the channel capacities, the channel dispersions, the weighting factors, and the blocklength of the code used for transmissions over the plurality of parallel channels; and

communicate, with another communicating device, in accordance with the performance characteristics of the plurality of parallel channels.

32. The communicating device of claim 31, the performance characteristics being symbol rates, and the performance characteristics being further determined in accordance with error rates of the plurality of parallel channels.

33. The communicating device of claim 31, the performance characteristics being error rates, and the performance characteristics being further determined in accordance with symbol rates of the plurality of parallel channels.

34. The communicating device of claim 33, the one or more processors further executing the instructions to iteratively determine the error rates until a convergence threshold is met or a predetermined number of iterations is met.

35. The communicating device of any one of claims 32 or 33, the one or more processors further executing the instructions to applying the weighting factors to the channel capacities, and applying the weighting factors to the channel dispersions.

36. The communicating device of claim 34, the one or more processors further executing the instructions to determine the performance characteristics in accordance with a combination of weighted channel capacities and weighted channel dispersions.

37. The communicating device of claim 33, the performance characteristics being error rates of the plurality of parallel channels, and the one or more processors further executing the instructions to determine the error rates in accordance with a combined symbol rate of the plurality of parallel channels, with the weighting factors set to p_k

38. The communicating device of any one of claims 31-37, the one or more processors further executing the instructions to evaluate w_k = where / 0 is a pre

specified function, R_k is the symbol rate for a Ar-th channel, and /_¾ =

39. The communicating device of claim 38, the pre-specified function/0 comprising f(R_k) = R_k ^a, where a is a parameter that is adjusted for the code.

40. The communicating device of any one of claims 31-39, the one or more processors further executing the instructions to calculate the weighting factors.

41. The communicating device of any one of claims 31-39, the one or more processors further executing the instructions to transmit, to a different device, the channel capacities and the channel dispersions; and receive, from the different device, the weighting factors.