WO2023218427A1

WO2023218427A1 - System and method for channel estimation in spectrum sharing with massive multiple input-multiple output (mimo) operation

Info

Publication number: WO2023218427A1
Application number: PCT/IB2023/054948
Authority: WO
Inventors: Zahra POURGHAREHKHAN; Shahram SHAHBAZPANAHI; Majid Bavand; Gary Boudreau; Israfil Bahceci; Ali AFANA
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2022-05-13
Filing date: 2023-05-12
Publication date: 2023-11-16

Abstract

A method, system and apparatus for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO) are disclosed. According to one aspect, a method in a network node includes estimating channel coefficients of at least one primary user (PU) during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase. The method also includes allocating a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

Description

SYSTEM AND METHOD FOR CHANNEL ESTIMATION IN SPECTRUM SHARING WITH MASSIVE MULTIPLE INPUT-MULTIPLE OUTPUT (MIMO) OPERATION

FIELD

The present disclosure relates to wireless communications, and in particular, to systems and methods for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO).

BACKGROUND

The Third Generation Partnership Project (3 GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs. Sixth Generation (6G) wireless communication systems are also being considered.

Underlay spectrum sharing cognitive radio (USSCR) addresses the issue of spectrum under-utilization. In USSCRs, a secondary network (SN) is allowed to concurrently access the spectrum licensed to a primary network (PN). As the PN has higher priority for using the spectrum, a challenge for realizing an USSCR is to ensure that the SN avoids harmful interference experienced by the PN. Also, minimal required cooperation between the PN and SN is preferred.

Massive multiple-input-multiple-output (MIMO) technology, where the base stations of a network are equipped with a large number of antennas, provides high spatial multiplexing capabilities and spectral efficiency by employing beamforming technology. Nevertheless, the performance of massive MIMO schemes hinges on the accuracy of channel state information (CSI) acquisition. The pilot contamination (PC) phenomenon, as a consequence of sharing pilot sequences between a PN and an SN, has been recognized as a limiting factor in USSCRs that hinders acquisition of reliable CSI.

Despite the effect on system performance, the problem of channel estimation has been rarely studied in the literature of USSCRs. In one reference, it is assumed that the secondary base station (SBS) transmits pilot symbols towards both the primary user devices (PUs) and the secondary user devices (SUs), and then, the PUs and the SUs send the estimated channels back to the SBS. This method imposes a huge burden on the PN. Moreover, no training design or scheduling for the PN and the SN is provided to avoid significant pilot contamination (PC) at both networks. An orthogonal pilot sharing scheme between the PN and the SN during the training phase has been proposed, where the SUs are permitted to use only those pilot sequences for channel estimation that are not used by the PUs. This scheme significantly degrades the spectral efficiency of the SN, as the number of the SUs served by the SBS is limited to the number of inactive PUs. A pilot sharing strategy has been proposed where both the SUs and the PUs simultaneously transmit their pilots towards their corresponding base stations. In this scheme, the training length of the PN depends on the number of the SUs. Also, this scheme either suffers from harmful PC in networks with a large number of nodes or requires a considerably long training phase, thereby leading to spectral efficiency reduction for both networks, particularly in crowded networks.

The channel estimation techniques in USSCRs mostly concentrate on the channel estimates of the SUs at the SBS without prioritizing protection of the training performance of the PN from PC caused by the SUs at the primary base station (PBS). Either the spectral efficiency of the SN significantly degrades, or the PN has to change its training phase duration as the number of the SUs is changed. Additionally, the problem of channel estimation of PUs at the SBS is not investigated in the existing USSCR schemes such that PUs are not meant to collaborate with the SBS. However, PU channel estimates at the SBS may be used to protect PN in the data transmission phase. Also, involving PUs in the channel estimation process of the SN imposes burdens on the PN, degrading its spectral efficiency and increasing its consumed energy.

SUMMARY

Some embodiments advantageously provide network nodes and methods for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO).

Some embodiments address the problem of channel estimation in a multi-user massive MIMO secondary network aiming to access the same licensed spectrum of a multi-user massive MIMO primary network using the underlay spectrum sharing approach. The channels of the single-antenna primary users (PUs) and those of the secondary users (SUs) at the secondary base station (SBS) may be estimated by exploiting a learning phase. In the learning phase, the secondary network (SN) is silent and listens to the primary network (PN) in order to extract the required information about the PU channels. To do so, the SN training phase is designed with the priority of mitigating pilot contamination at the PN, under the restriction that the PN is not meant to change its training phase length in the presence of the SN. The disclosed estimator of the PU channels is based on the PU data in addition to their pilots. In other words, to estimate the PU channels, the signals received at the SBS during both (uplink) UL and training intervals of the PN may be used. The disclosed estimator outperforms the conventional pilot-based estimators. To estimate the SU channels at the SBS, two seemingly different pilot-based approaches are disclosed and it is shown that they result in the same estimator. These approaches rely on the PU channels already estimated at the SBS during the learning phase. Some embodiments enable the SN to control the interference that it causes at the PN at the cost of a slight performance degradation in terms of quality of the SU channel estimates at the SBS.

In some embodiments, a solution is provided to the problem of channel estimation at the SBS in underlay spectrum sharing cognitive radios (USSCRs). Unlike known techniques, a disclosed SN training phase is designed with the priority of restraining pilot contamination (PC) at the PN, in some embodiments. Employing a learning phase, where the SN is not transmitting and listens to the PN, the PU channels are estimated at the SBS. Using a disclosed transmission frame structure of the SN along with the estimated PU channels, the interference imposed on the SBS from the PUs during the SN training phase may be reduced, and then the SU channels may be estimated. The estimated PU channels may also be employed for protecting the PUs from the SN during the SN data transmission phase and vice versa. Some embodiments provide reliable channel state information (CSI) at the SBS and allow the PN to operate its training independently from the SN presence. In fact, the design parameters of the PN training phase are not required to change due to the SN existence, in some embodiments.

In some embodiments, the problem of channel estimation at the SBS in USSCRs is solved with a low complexity solution that does not require prior knowledge about the channels. In some embodiments, an SN training phase is implemented with the priority of mitigating PC at the PN. This avoids prohibitive performance degradation of the PN in the presence of the SN in terms of spectral efficiency. Using a data-based method, an estimator for the PU channels is disclosed that outperforms the conventional pilot-based estimator, as shown in FIG. 1.

By using the estimated PU channels, the channels of the SUs are estimated such that the interference imposed on the SBS by the PUs during the SN training phase may be eliminated or estimated. Therefore, highly reliable CSI may be achieved at the SBS, as shown in FIG. 2, leading to improvement of not only the SN spectral efficiency, but also the spectral efficiency of the PN.

Moreover, the estimated PU channels at the SBS may be used to protect the PUs from the signals transmitted by the SBS and vice versa.

In addition, in some embodiments, the PN has no collaboration with the SN for acquiring reliable CSI at the SBS except for sharing the pilot sequences. This approach achieves data rates desired in 5G and 6G communications as well as green communication since it reduces the total transmit power in the network.

According to one aspect, a network node in a secondary network configured to communicate with a plurality of wireless devices, WDs, is provided. The network includes processing circuitry configured to estimate channel coefficients of at least one WD being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase. The processing circuitry is also configured to allocate a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

According to this aspect, in some embodiments, estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals. In some embodiments, the at least partial cancellation of interference is based at least in part on an estimate of PUs’ channels. In some embodiments, estimating the channel coefficients is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the SU channels being channels between the network node and WDs being secondary users of the secondary network. In some embodiments, cancellation of interferences is based at least in part on a correlation of the received samples with a secondary user, SU, pilot signal, the secondary user being a user of the secondary network. In some embodiments, estimating the channel coefficients includes subtracting a PU pilot signal from the received samples. In some embodiments, an amount of overlap is based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, the power allocated to the secondary users of the secondary network is selected based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, estimating the channel coefficients includes concatenating SU channels and PU channels during the secondary network training phase as a matrix. In some embodiments, estimating the channel coefficients includes determining a pseudoinverse of PN channels. In some embodiments, estimating the channel coefficients includes subtracting PU pilots from a vector of samples received during the secondary network training phase to produce a resultant matrix and multiplying the resultant matrix by a pseudoinverse of the resultant matrix.

According to another aspect, a method implemented in a network node in a secondary network, the network node configured to communicate with a plurality of wireless devices, WDs, is provided. The method includes estimating channel coefficients of at least one WD being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase. The method also includes allocating a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

According to this aspect, in some embodiments, estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals. In some embodiments, the at least partial cancellation of interference is based at least in part on an estimate of PU channels. In some embodiments, estimating the channel coefficients is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the channels being channels between the network node and WDs being secondary users of the secondary network. In some embodiments, cancellation of interferences is based at least in part on a correlation of the received samples with a secondary user, SU, pilot signal, the secondary user being a user of the secondary network. In some embodiments, estimating the channel coefficients includes subtracting a PU pilot signal from the received samples. In some embodiments, an amount of overlap is based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, the power allocated to the secondary users of the secondary network is selected based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, estimating the channel coefficients includes concatenating SU channels and PU channels during the secondary network training phase as a matrix. In some embodiments, estimating the channel coefficients includes determining a pseudoinverse of PN pilots. In some embodiments, estimating the channel coefficients includes subtracting PU pilots from a vector of samples received during the secondary network training phase to produce a resultant matrix and multiplying the resultant matrix by a pseudoinverse of the resultant matrix.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

FIG. 1 is a graph of a comparison between a data-based estimator and an estimator according to some embodiments disclosed herein;

FIG. 2 is a comparison between a known pilot sequence design with an estimator according to some embodiments disclosed herein;

FIG. 3 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure;

FIG. 4 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure;

FIG. 9 is a flowchart of an example process in a network node for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO);

FIG. 10 is a communication system according to principles set forth herein; and FIG. 11 is a transmission frame structure for a PN and an SN.

DETAILED DESCRIPTION

Before describing in detail example embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to channel estimation in spectrum sharing with massive multiple input multiple output (MIMO). Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein may be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node.

In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The WD herein may be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD). The WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc. Examples of WDs as used herein also include primary users (PUs) and secondary users (SUs).

Also, in some embodiments the generic term “radio network node” is used. It may be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH). Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

Note further, that functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, may be distributed among several physical devices.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Some embodiments provide channel estimation in spectrum sharing with massive multiple input multiple output (MIMO).

Returning now to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 3 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first wireless device (WD) 22a (e.g., a PU 22a) located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22b (e.g., a SU 22b) in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16. Note that a WD 22 that acts as a secondary user device may be referred to herein as an SU 22, and a WD 22 that acts as a primary user device may be referred to herein as a PU 22. Note also that the network nodes 16a and 16b may be in different networks and may be served by different cores 14. Thus, the PUs 22a and SUs 22b may be in the same network or may be served by network nodes in different networks that may overlap and may also be collocated. In other words, although FIG. 3 shows a single network, implementations are nor limited to such.

Also, it is contemplated that a WD 22 may be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a WD 22 may have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, WD 22 may be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.

The communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm. The host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30. The intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network. The intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).

The communication system of FIG. 3 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24. The connectivity may be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries. The OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications. For example, a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.

A network node 16 is configured to include an estimator unit 32 which is configured to estimate channel coefficients of at least one primary user (PU) during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase.

Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 4. In a communication system 10, a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10. The host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities. The processing circuitry 42 may include a processor 44 and memory 46. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24. Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein. The host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with the host computer 24.

The software 48 may be executable by the processing circuitry 42. The software 48 includes a host application 50. The host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the remote user, the host application 50 may provide user data which is transmitted using the OTT connection 52. The “user data” may be data and information described herein as implementing the described functionality. In one embodiment, the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.

The communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. The hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.

In the embodiment shown, the hardware 58 of the network node 16 further includes processing circuitry 68. The processing circuitry 68 may include a processor 70 and a memory 72. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 74 may be executable by the processing circuitry 68. The processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16. Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein. The memory 72 is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16. For example, processing circuitry 68 of the network node 16 may include an estimator unit 32 which is configured to estimate channel coefficients of at least one primary user (PU) during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase.

The communication system 10 further includes the WD 22 already referred to. The WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.

The hardware 80 of the WD 22 further includes processing circuitry 84. The processing circuitry 84 may include a processor 86 and memory 88. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22. The software 90 may be executable by the processing circuitry 84. The software 90 may include a client application 92. The client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24. In the host computer 24, an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the user, the client application 92 may receive request data from the host application 50 and provide user data in response to the request data. The OTT connection 52 may transfer both the request data and the user data. The client application 92 may interact with the user to generate the user data that it provides.

The processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22. The processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein. The WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.

In some embodiments, the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 4 and independently, the surrounding network topology may be that of FIG. 3.

In FIG. 4, the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).

The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.

In some embodiments, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 52 between the host computer 24 and WD 22, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.

Thus, in some embodiments, the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the WD 22, and/or preparing/terminating/ maintaining/supporting/ending in receipt of a transmission from the WD 22.

In some embodiments, the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16. In some embodiments, the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the network node 16, and/or preparing/ terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.

Although FIGS. 3 and 4 show various “units” such as estimator 32 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 3 and 4, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 4. In a first step of the method, the host computer 24 provides user data (Block S100). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block S102). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104). In an optional third step, the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block S106). In an optional fourth step, the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block S108).

FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 3, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 3 and 4. In a first step of the method, the host computer 24 provides user data (Block S 110). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12). The transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step, the WD 22 receives the user data carried in the transmission (Block S 114).

FIG. 7 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 3, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 3 and 4. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block S 116). In an optional substep of the first step, the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block S 118). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124). In a fourth step of the method, the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).

FIG. 8 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 3, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 3 and 4. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block S130). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block S132).

FIG. 9 is a flowchart of an example process in a network node 16 for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO). One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the estimator 32), processor 70, radio interface 62 and/or communication interface 60. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to estimate channel coefficients of at least one WD 22 being a primary user, PU 22, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase (Block S134). The process also includes allocating a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap (Block S136).

In some embodiments, estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals. In some embodiments, the at least partial cancellation of interference is based at least in part on an estimate of PU channels. In some embodiments, estimating the channel coefficients is based at least in part on a joint estimation of secondary user, SU 22, channels and PU data, the channels being channels between the network node and WDs 22 being secondary users (SUs) 22, of the secondary network. In some embodiments, cancellation of interferences is based at least in part on a correlation of the received samples with a secondary user, SU, pilot signal, the secondary user being a user of the secondary network. In some embodiments, estimating the channel coefficients includes subtracting a PU pilot signal from the received samples. In some embodiments, an amount of overlap is based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, the power allocated to the secondary users of the secondary network is selected based at least in part on a tradeoff between interference and channel identifiability. In some embodiments, estimating the channel coefficients includes concatenating SU channels and PU channels during the secondary network training phase as a matrix. In some embodiments, estimating the channel coefficients includes determining a pseudoinverse of PN pilots. In some embodiments, estimating the channel coefficients includes subtracting PU pilots from a vector of samples received during the secondary network training phase to produce a resultant matrix and multiplying the resultant matrix by a pseudoinverse of the resultant matrix.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for channel estimation in spectrum sharing with massive multiple input multiple output (MIMO).

An example communications network is illustrated in FIG. 10. Assumptions and features of the system may include the following:

• The PN may be implemented using NN 16a as described herein, and the SN may be implemented using NN 16b as described herein. Note that a NN 16 may be configured as either a primary base station 16a or a secondary base station 16b or both. Both the PBS 16a and the PBS 16b may operate in the same RF bandwidth, simultaneously:

• The PN includes: o A PBS 16a with massive number of antennas denoted as M_p; o N_p single-antenna PUs 22a (M_p » N_p) (this could be extended to multiple antennas);

• The SN includes: o An SBS 16b with massive number of antennas denoted as M_s; o N_s single-antenna SUs 22b (M_s » N_s) (this could be extended to multiple antennas);

• Both the PBS 16a and SBS 16b employ transmit and receive beamformers to serve their own users; and/or

• The PBS 16a provides the SBS 16b with the information of the PUs’ pilots.

Communication frame structure

In order to cause the least disturbance possible to the PN, the SN may be configured to obtain a reliable CSI of both the PUs 22a and SUs 22b. The training phase of the SN may be implemented to control the PC at the PN.

FIG. 11 shows a communication super-frame of F time frames for data exchange in the PN. During each super- frame, the channels among the base stations and users in both networks are assumed reciprocal and fixed over both time and frequency. Each PN time frame includes three sub-frames, namely training phase (TP), UL phase, and downlink (DL) phase. Assume that the SN is aware of the PN communication time frame structure, and thus, aligns its time frames with those of the PN. At the SBS 16b, the PN super-frame is divided into two groups of frames, namely the learning phase frames (LPFs), and spectrum sharing frames (SSFs). The number of EPFs and SSFs are denoted by F, and F_t, respectively. During the LPFs, all nodes in the SN remain quiet and listen to the PN with the aim of estimating the SBS-PU channel coefficients. Each SN SSF includes three phases, namely a training phase, an UL phase, and a DL phase. During the SSF phase, the SN operates in one of the following two modes: in the first mode, which takes the first F_o time frames after the LPF, the SN performs UL training using training intervals which partially overlap with the training phase of the PN by L_o symbols. In the second mode, which takes the remaining time frames of the PN super-frame, the SN performs UL training using training intervals which do not overlap with the training phase of the PN. Note that if F_o = 0, the training phase of the SN will not interfere with the training phase of the PN in any time frame. However, choosing F_o = 0 results in channel nonidentifiability in the SN. During the time-interval in the training phase of the PN where the SN is not transmitting in the learning phase (LP), the SN uses the signals emitted by the PN nodes. There are also signals emitted by the PN nodes during the LPFs for learning more about the PN nodes. Those SSFs where training in the SN overlaps with the training in the PN are referred to as overlapping frames (OFs), while the remaining SSFs are called non-overlapping frames (NOFs). TP_OV and TP_nv refer to the portion of the SN training period which overlaps and does not overlap, respectively, with the PN training period. Denoting the duration of training in both networks in each frame as L_t, the length of the SN training period in the OFs that does not overlap with the PN training is denoted as L2 ⁼ Lf- L_o.

General method assumptions

Assume that the SN has prior knowledge of the PU training symbols. This information may be provided by the PBS 16a. Also assume that the SN has no information about the statistical characteristics of the channels. The knowledge of the PU training symbols may be used along with the training symbols of the SUs 22 to estimate the SBS- PU and the SBS-SU channel coefficients at the SBS 16b. These channel estimates may then be used to design receive and transmit beamformers at the SN that reduce interference to the PN nodes. Further assume that the SN is aware of the PN communication time frame structure, and thus, may align its time frames with those of the PN. Also assume that the energy of the SUs 22 at the training phase of each frame is fixed. In some embodiments, a first estimate of the SBS-PU channel coefficients may be made using the signals received at the SBS 16b during the learning intervals. Then, using the so- estimated SBS-PU channel coefficients, three approaches to estimate the SBS-SU channel coefficients at the SBS 16b are disclosed.

SBS-PU channel estimation

In order to estimate the channels coefficients of the PUs at the SBS 16b, the subspace of the columns of the SBS-PU channel matrix may be used. To estimate this subspace, the SBS 16b may calculate the sample covariance matrix (SCM) of its received samples when the SN is not transmitting. The SCM may be obtained using the samples received at the SBS 16b during all the LPFs and LPs. The received samples in these time durations include the PUs’ data in addition to their pilots. To constitute the columns of the subspace of the SBS-PU channel matrix, consider the first N_p principal eigenvectors of the SCM. Then, the SBS-PU channel matrix may be estimated using the samples received at the SBS 16b when the PN is in the training phase and the SN is not transmitting. To do so, the SBS 16b may employ a least squares (LS) estimator. Indeed, one example estimator for the SBS-PU channels has two main steps as summarized in Algorithm 1.

Algorithm 1: Estimating SBS-PU channel matrix

1) Collect samples received at the training and UL intervals during the LPFs and LPs:

1-1) Calculate the SCM;

1-2) Consider the first N_p principal eigenvectors of the SCM;

2) Collect samples received at the PN training phases during the LPFs and LPs:

2-1) Multiply the resultant matrix of Step 2 by the Hermitian of resultant matrix of Step 1-2 from left;

2-2) Multiply the resultant matrix by the resultant matrix of Step 1-2 from left;

2-3) Multiply the resultant matrix by the pseudoinverse of PN pilots from right; and/or

2-4) Divide the resultant matrix by square root of the PU power.

One disclosed estimator is a minimum-variance unbiased estimator (MVUE) of the SBS-PU channel matrix when the subspace of the columns of the SBS-PU channel matrix is available and the noise of receiver is Gaussian. Among all possible unbiased estimators, this estimator has the minimum variance and does not require prior knowledge about the channels.

SBS-SU channel estimation

In some embodiments, in order to protect the PN training performance, the SN training phase overlaps with the PN UL interval in all the SSFs. However, the SN training phase may also overlap a portion of the PN training phase in the overlapping frames (OFs) in order to obtain the MVUE estimator of the SBS-SU channels. If the training phases of the PN and the SN do not overlap in SSFs, the problem of estimating the SBS-SU channels at the SBS 16b suffers from non-identifiability. By allowing an overlap between two networks training phases, the SUs’ pilots cause interference on the PBS 16a in its training phase, and consequently, may degrade its channel estimation performance. In order to reduce the degradation to the PN training phase, a lower power may be allocated for the SU symbols in the TP_OV as compared to TP_nv. In order to control the SUs’ power in TPov and TP_nv, the coefficients denoted by oq and a₂ ^arc used, respectively. In other words, in some embodiments, it is assumed that oq < a₂ ^to protect the PBS 16a training as much as possible. With a relatively small value for oq, the SUs cause insignificant interference to the PBS 16a. For the pilots at the NOFs, different symbols may have equal powers, that is oq = oc₂. Assume that the energy of the SUs at the training phase of each frame is fixed.

The signals received at the SBS 16b during the SN training phase may include both the PUs’ pilots and PUs’ UE data as well as the SU pilots. In light of the fact that the SBS 16b has already estimated the PUs’ channels and is aware of the PUs’ pilots, the received samples including the PUs’ pilots may be subtracted from the total samples received at the SBS 16b in the SN training phases. Therefore, the residual signals may only include the PUs’ UL data in addition to the SU pilots corrupted by the receiver noise. Thus, the SBS 16b may have interference caused by the PUs’ UL data during the SN training phase. Three different approaches are disclosed to address this interference, namely: a joint channel and interference estimator (JCIE) approach, an interference cancellation estimator (ICE) approach and a conventional least squares (LS) approach.

Approach 1: JCIE

In the first approach, during the SN training phase, the SBS-SU channel matrix and the PUs’ UL data (which overlap in TP_nv with the training phase of the SBS 16b) are treated as unknowns to the SBS 16b. Joint estimation of the SUs’ channels and the PUs’ data using the LS estimator may then be performed, followed by extraction of the estimation of the desired parameters that include the SBS-SU channel coefficients. Algorithm 2 is summarized as follows:

Algorithm 2: Estimating SBS-SU channel matrix using JCIE

1) Set cq < a₂ in the OFs and cq = cq in the NOFs;

2) Collect the received samples at the SN training phases;

3) Subtract the PU pilots from the resultant vector of Step 2;

4) Concatenate the SUs’ pilots and PUs’ channels during all the SN training phases as a matrix;

5) Multiply the resultant vector of Step 3 by the pseudoinverse of resultant matrix of Step 4 from left; and/or

6) Extract the first M_SN_S elements of the resultant vector of Step 5.

Approach 2: ICE

In this approach, the problem of the SBS-SU channel estimation at the SBS 16b is solved by canceling the interference caused by the PUs to the SBS 16b instead of estimating this interference. To do so, knowledge of the PUs’ channels estimated at the SBS 16b in the learning phase is used. A matrix is found having columns that are orthogonal and that span the null space of the matrix. This includes the PUs’ channels during all the SN training phases except the TP_OV. This matrix is referred to as the cokemel of the PUs’ channels. Algorithm 3 is summarized as follows: Algorithm 3: Estimating SBS-SU channel matrix using ICE

1) Set cq < a₂ in the OFs and cq = cq in the NOFs;

2) Collect the received samples at the SN training phases;

3) Subtract the PUs’ pilots from the resultant vector of Step 2;

4) Find the cokernel of the matrix including the PUs’ channels at the SN training phases;

5) Multiply the resultant vector of Step 3 by the Hermitian of resultant matrix of Step 4 from left;

6) Multiply the Hermitian of cokemel of the PUs’ channels by the SUs’ pilots during all the SN training phases; and/or

7) Multiply the resultant vector of Step 5 by the pseudoinverse of resultant matrix of Step 6 from left.

Equivalency of JCIE and ICE:

Employing JCIE or ICE may result in the same estimator for the SU channel coefficients at the SBS 16b. JCIE and ICE are MVUE for Gaussian noise of a receiver.

Approach 3: Conventional LS As another solution, the unknown interference may be treated as noise. Thus, the signals received at the SBS 16b may include the SU training signals and the additive noise. Therefore, the conventional LS in which the SBS-SU channels are estimated by correlating the SBS’s received samples with the SUs’ pilots may be employed. This estimator may suffer from poor performance as the power of the PU data is considerable, and thus, the signal-to-noise ratio may be low.

Some embodiments may include one or more of the following:

Embodiment Al. A network node in a secondary network configured to communicate with a plurality of wireless devices, WDs, the network node configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to: estimate channel coefficients of at least one WD being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is quiet, the secondary network training phase partially overlapping a primary network training phase; and allocate a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

Embodiment A2. The network node of Embodiment Al, wherein estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals.

Embodiment A3. The network node of Embodiment A2, wherein cancellation of interference is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the SU channels being channels between the network node and WDs being secondary users of the secondary network.

Embodiment A4. The network node of any of Embodiments A2 and A3, wherein cancellation of interference is based at least in part on an estimate of PUs’ channels.

Embodiment A5. The network node of any of Embodiments A2-A4, wherein cancellation of interferences is based at least in part on a correlation of the received samples with a secondary user, SU, pilot signal, the secondary user being a user of the secondary network.

Embodiment A6. The network node of any of Embodiments A1-A5, wherein estimating the channel coefficients includes subtracting a PU pilot signal from the received samples.

Embodiment A7. The network node of any of Embodiments A1-A6, wherein an amount of overlap is based at least in part on a tradeoff between interference and channel identifiability.

Embodiment Bl. A method implemented in a network node in a secondary network, the network node configured to communicate with a plurality of wireless devices, WDs, the method comprising: estimating channel coefficients of at least one WD being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is quiet, the secondary network training phase partially overlapping a primary network training phase; and allocating a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

Embodiment B2. The method of Embodiment B l, wherein estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals.

Embodiment B3. The method of Embodiment B2, wherein cancellation of interference is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the channels being channels between the network node and WDs being secondary users of the secondary network.

Embodiment B4. The method of any of Embodiments B2 and B3, wherein cancellation of interference is based at least in part on an estimate of PU channels.

Embodiment B5. The method of any of Embodiments B2-B4, wherein cancellation of interferences is based at least in part on a correlation of the received samples with a secondary user, SU, pilot signal, the secondary user being a user of the secondary network.

Embodiment B6. The method of any of Embodiments B 1-B5, wherein estimating the channel coefficients includes subtracting a PU pilot signal from the received samples.

Embodiment B7. The method of any of Embodiments B 1-B6, wherein an amount of overlap is based at least in part on a tradeoff between interference and channel identifiability.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that may be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable memory or storage medium that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments may be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

Abbreviations that may be used in the preceding description include:

CSI Channel state information

DL Downlink

ICE Interference cancellation estimator

JCIE Joint channel and interference estimator

LPF Learning phase frame

LP Learning phase

LS Least square

MIMO Multiple input multiple output

MVUE Minimum-variance unbiased estimator

NOF Non-overlapping frame

OF Overlapping frame

PBS Primary base station

PC Pilot contamination

PN Primary network

PU Primary user

SBS Secondary base station SCM Sample covariance matrix

SN Secondary network

SSF Spectrum sharing frame

SU Secondary user TP Training phase

UL Uplink

USSCR Underlay spectrum sharing cognitive radio

WD Wireless Device It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

What is claimed is:

1. A network node (16) in a secondary network configured to communicate with a plurality of wireless devices, WDs (22), the network node (16) comprising processing circuitry configured to: estimate channel coefficients of at least one WD (22) being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase; and allocate a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

2. The network node (16) of Claim 1, wherein estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals.

3. The network node (16) of Claim 2, wherein the at least partial cancellation of interference is based at least in part on an estimate of PUs’ channels.

4. The network node (16) of Claim 1, wherein estimating the channel coefficients is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the SU channels being channels between the network node (16) and WDs (22) being secondary users of the secondary network.

5. The network node (16) of any of Claims 2-4, wherein the cancellation of interference is based at least in part on a correlation of the received samples with a pilot signal of a secondary user, SU, the secondary user being a user of the secondary network.

6. The network node (16) of any of Claims 1-5, wherein estimating the channel coefficients includes subtracting a PU pilot signal from the received samples.

7. The network node (16) of any of Claims 1-6, wherein an amount of overlap is based at least in part on a first tradeoff between interference and channel identifiability.

8. The network node (16) of any of Claims 1-7, wherein the power allocated to the secondary users of the secondary network is selected based at least in part on a second tradeoff between interference and channel identifiability.

9. The network node (16) of any of Claims 1-8, wherein estimating the channel coefficients includes concatenating SU channels and PU channels during the secondary network training phase as a matrix.

10. The network node (16) of any of Claims 1-9, wherein estimating the channel coefficients includes determining a pseudoinverse of PN pilots.

11. The network node (16) of any of Claims 1-10, wherein estimating the channel coefficients includes subtracting PU pilots from a vector of samples received during the secondary network training phase to produce a resultant matrix and multiplying the resultant matrix by a pseudoinverse of the resultant matrix.

12. A method implemented in a network node (16) in a secondary network, the network node (16) configured to communicate with a plurality of wireless devices, WDs (22), the method comprising: estimating (S134) channel coefficients of at least one WD (22) being a primary user, PU, of a primary network, the estimation being performed during a secondary network training phase based at least in part on samples received when the secondary network is not transmitting, the secondary network training phase partially overlapping a primary network training phase; and allocating (S136) a power to secondary users of the secondary network during the overlap that is lower than a power allocated to the secondary users during a time when there is no overlap.

13. The method of Claim 12, wherein estimating the channel coefficients includes at least partial cancellation of interference from PU uplink data signals.

14. The method of Claim 13, wherein the at least partial cancellation of interference is based at least in part on an estimate of PU channels.

15. The method of Claim 12, wherein estimating the channel coefficients is based at least in part on a joint estimation of secondary user, SU, channels and PU data, the channels being channels between the network node (16) and WDs (22) being secondary users of the secondary network.

16. The method of any of Claims 13-15, wherein the cancellation of interference is based at least in part on a correlation of the received samples with a pilot signal of a secondary user, SU, the secondary user being a user of the secondary network.

17. The method of any of Claims 12-16, wherein estimating the channel coefficients includes subtracting a PU pilot signal from the received samples.

18. The method of any of Claims 12-17, wherein an amount of overlap is based at least in part on a first tradeoff between interference and channel identifiability.

19. The method of any of Claims 12-18, wherein the power allocated to the secondary users of the secondary network is selected based at least in part on a second tradeoff between interference and channel identifiability.

20. The method of any of Claims 12-19, wherein estimating the channel coefficients includes concatenating SU channels and PU channels during the secondary network training phase as a matrix.

21. The method of Claim any of Claims 12-20, wherein estimating the channel coefficients includes determining a pseudoinverse of PN pilots.

22. The method of any of Claims 12-21, wherein estimating the channel coefficients includes subtracting PU pilots from a vector of samples received during the secondary network training phase to produce a resultant matrix and multiplying the resultant matrix by a pseudoinverse of the resultant matrix.