WO2019170875A1

WO2019170875A1 - Reception of signals from multiple sources

Info

Publication number: WO2019170875A1
Application number: PCT/EP2019/055876
Authority: WO
Inventors: Ajit Reddy
Original assignee: Nokia Solutions And Networks Oy
Priority date: 2018-03-09
Filing date: 2019-03-08
Publication date: 2019-09-12

Abstract

A solution for reception of signals from multiple sources is disclosed. The apparatus in a radio access network comprises a set of antenna element (212A –212D), an RF front end (214A–214D) and an analog to digital front end (216A –216D) connected to each antenna element, at least one digital vector signal processor (220) connected to the digital front ends. The apparatus receives as an input a transmission comprising signals of different access types and modulation formats from multiple sources, samples and scales the samples in vector form.

Description

RECEPTION OF SIGNALS FROM MULTIPLE SOURCES

Technical Field

The exemplary and non-limiting embodiments relate to communication systems and detection and decoding of signals from multiple sources.

Background

Wireless telecommunication systems are under constant development. In addition to traditional cellular networks where each user terminal is connected to the infrastructure such as a base station, various other network types have also been proposed. An ad hoc wireless mesh access network is a collection of both mobile and immobile communication devices that wish to communicate but have no fixed infrastructure available and have no pre- determined organization of available links. Individual communication devices or nodes are responsible for dynamically discovering, which other nodes they can directly communicate with. In some scenarios there are individual nodes that are capable of gateway (GW) discovery and have a power supply and they may be configured to connect to the Internet, thus providing support to the mobile nodes in an ad hoc manner. In the case of some scenarios these nodes are immobile. These networks may thus comprise an immobile backbone of access point (AP) setup in a planned or unplanned manner. These networks are expected to provide a high reliability and quality of service (QoS).

In the design of most wireless communication systems interference issues must be taken into account. This applies especially to networks where there is no centralized control such as ad hoc wireless mesh access networks. In addition the density of the networks and the use of multiple antennas have an effect ion interference. Therefore, a suitable architecture for the receiver needs to be defined which are suitable for receiving of multi-source signals of different formats.

Brief description

The following presents a simplified summary of the invention in or- der to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to a more detailed description that is presented later.

According to an aspect of the present invention, there is provided a method of claim 1.

According to an aspect of the present invention, there is provided an apparatus of claim 6.

According to an aspect of the present invention, there is provided a computer program comprising instructions as claimed in claim 11.

Brief description of drawings

One or more examples of implementations are set forth in more detail in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.

Figure 1 illustrates a general architecture of an exemplary system; Figures 2A and 2B illustrate examples of an apparatus;

Figure 3 illustrates an example of a radio frequency front end;

Figure 4 illustrates an example of an analog to digital front end; and

Figure 5 is a flowchart illustrating an embodiment.

Detailed description of some embodiments

In the following, different exemplifying embodiments will be described using, as an example of an access architecture to which the embodiments may be applied, a radio access architecture based on long term evolution advanced (LTE Advanced, LTE-A) or new radio (NR, 5G), without restricting the embodiments to such an architecture, however. It is obvious for a person skilled in the art that the embodiments may also be applied to other kinds of communications networks having suitable means by adjusting parameters and procedures appropriately. Some examples of other systems are the universal mobile telecommunications system (UMTS) radio access network (UTRAN or E-UTRAN), long term evolution (LTE, the same as E-UTRA), wireless local area network (WLAN or WiFi), worldwide interoperability for microwave access (WiMAX), Bluetooth®, personal communications services (PCS), ZigBee®, wideband code division multiple access (WCDMA), systems using ultra- wideband (UWB) technology, sensor networks, mobile ad-hoc networks (MANETs) and Internet Protocol multimedia subsystems (IMS) or any combination thereof.

Figure 1 depicts an example of a simplified system architecture only showing some elements and functional entities, all being logical units, whose implementation may differ from what is shown. The connections shown in Fig. 1 are logical connections; the actual physical connections may be different. It is apparent to a person skilled in the art that the system typically comprises also other functions and structures than those shown in Fig. 1.

The embodiments are not, however, restricted to the system given as an example but a person skilled in the art may apply the solution to other communication systems provided with necessary properties.

Fig. 1 shows mobile nodes or user terminals 100 and 102 configured to be in a wireless connection on one or more communication channels in an ad hoc network. The mobile nodes may be in connection with immobile nodes 104, 106, 108, 110 which are configured to act as access points. The immobile nodes may in turn be connected via a gateway 112, 114 to a communication network 116, 118 such as Internet, for example, thus providing backhauling connectivity. The connection between the access point and the gateway may be through a high-speed connection which could be wired or wireless. The connections may also utilize multiple hops between nodes.

The mobile node (also called UE, user equipment, user terminal, terminal device, etc.) illustrates one type of an apparatus to which resources on the air interface are allocated and assigned.

The user device typically refers to a portable computing device that includes wireless mobile communication devices operating with or without a subscriber identification module (SIM), including, but not limited to, the following types of devices: a mobile station (mobile phone), smartphone, personal digital assistant (PDA), device using a wireless modem (alarm or measurement device, etc.), laptop and/or touch screen computer, tablet, game console, notebook, and multimedia device. It should be appreciated that a user device may also be a nearly exclusive uplink only device, of which an example is a camera or video camera loading images or video clips to a network. A user device may also be a device having capability to operate in Internet of Things (loT) network which is a scenario in which objects are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. An example of such a device is a sensor or actuator. The user device may also utilise cloud. In some applications, a user device may comprise a small portable device with radio parts (such as a watch, earphones or eyeglasses) and the computation is carried out in the cloud. The user device may be configured to perform one or more of user equipment functionalities. The user device may also be called a subscriber unit, remote terminal, or access terminal just to mention but a few names or apparatuses.

Various techniques described herein may also be applied to a cyber- physical system (CPS) (a system of collaborating computational elements controlling physical entities). CPS may enable the implementation and exploitation of massive amounts of interconnected ICT devices (sensors, actuators, processors microcontrollers, etc.) embedded in physical objects at different locations. Mobile cyber physical systems, in which the physical system in question has inherent mobility, are a subcategory of cyber-physical systems. Examples of mobile physical systems include mobile robotics and electronics transported by humans or animals.

Additionally, although the apparatuses have been depicted as single entities, different units, processors and/or memory units (not all shown in Fig. 1 ) may be implemented.

In recent times, wireless ad hoc mesh networks have become attractive because they are expected to partially substitute wired network infrastructure thus providing low cost efficient solution for wireless networking in areas where the wired networks are not deployed. Their popularity comes from the fact that they are self-organized and self-configurable and easily adaptable to different traffic requirements and network changes. In these networks the assumption is made that there are nodes which connect to the internet through the discovery of a gateway, have ample supply of power and thus may serve as access points. These nodes may be immobile. However, an access point may also be connected to the gateway by means of unmanned aerial vehicle (UAV), unmanned surface vehicle (USV) or the access point can be the unmanned ground vehicle (UGV) as well. The UGV and the USV can be mobile or stationary.

Techniques with multiple antenna elements in transmission and/or reception are highly beneficial to the wireless mesh network and they may be used in the form of beam antennas, adaptive antennas and multiple-input multiple-output (MIMO) coding, for example. Multiple antennas can provide power, diversity and multiplexing gain and therefore increase the transmission range and reduce the transmitting power, mitigate interference, increase channel reliability and data throughput. Given the different types of propagation environment that are expected in these networks, multiple antenna techniques are expected to boost throughput performance and reduce interference and delay thus improving overall end to end performance.

Due to spatial channel reuse in these networks the access point, when receiving signals from different mobile nodes, may suffer from interference from the other nodes. This may make the wireless communication interference limited rather than noise limited. In multiple antenna systems, the network capacity is affected not only by the signal to interference ratio but also by the interference power that greatly reduces the data rates in these communication links. The interference in this case is spatially colored rather than being spatially white since it emanates unequally from different directions. These interference needs to be characterized for the optimum design of the receiver architectures and algorithms.

Multiple antennas in the form of directional antennas / adaptive antennas may be used to optimize power transmission and reception. A directional antenna generates multiple fixed beam patterns in the direction of interest essentially providing sectoring. An adaptive antenna array generates the beam structure based on a certain optimization criteria, such as maximizing the array gain towards the signal of interest and thus suppressing the interfering signals.

In mesh networks, multi-hop support with self-backhaul introduces interference which is known and must be taken care of. Interference affects resource allocation and becomes worse with large number of antenna elements. Adaptive beamforming alone is not the solution and this degrades the capacity and affects the system throughput. This is not only a problem in 4G and 5G networks but also future networks which involve multichannel structures.

Another scenario is beyond 5G where waveforms and carrier densities are not clearly defined apriori and a receiver needs to sense and adapt in real time for different waveforms which is not a trivial problem.

A solution for a wideband multi-carrier and/or multi-channel receiver is proposed where the receiver is able to simultaneously down-converts a set of RF channels residing in a single sampled data stream. The received signals may have been originated from multiple sources with different access types and modulations. In wideband multi-carrier receivers, a single receiver processes all the spectrum bands of interest, and the filtering of each component carrier signal is usually done in the digital domain, after analog-to digital conversion. Since the wideband receivers have minimum RF processing and filtering they have relatively simple RF design and potentially can be implemented on silicon as a single chip RF 1C. Flowever, they require wideband analog-to-digital converter (RF ADC) having very high-speed, high resolution and high dynamic range, which limits application of the wideband receiver architecture in modern wireless communication systems

The proposed wideband receiver architecture is capable of successful detection and deconvolution of multiple signal sources. The approach does not have a priori information regarding the signal and uses the arbitrary array geometry and applicability in any propagation environment. The method proposed has the property of being globally convergent. The adaptive method is sufficiently fast to track channel variations caused by moving transmitters while at the same time having a low complexity from the computational point of view.

Fig. 2A and 2B illustrate an embodiment. It should be understood that the apparatus is depicted herein as an example illustrating some embodiments. It is apparent to a person skilled in the art that the apparatus may also comprise other functions and/or structures and not all described functions and structures are required. Although the apparatus has been depicted as one entity, different modules and memory may be implemented in one or more physical or logical entities. For example, the apparatus may be realized using cloud computing or distributed computing with several physical entities located in different places but connected with each other.

Fig. 2A illustrates a simplified example of an apparatus 200 of a radio access network in which embodiments of the invention may be applied. In some embodiments, the apparatus may be a base station, an access point, a user terminal or a remote radio head, for example.

The apparatus of the example includes a control circuitry 202 configured to control at least part of the operation of the apparatus.

The apparatus may comprise a memory 204 for storing data. Furthermore the memory may store software or applications 206 executable by the control circuitry 202. The memory may be integrated in the control circuitry.

The control circuitry 202 is configured to execute one or more applications. The applications may be stored in the memory 204.

The apparatus may further comprise radio interface 208 operationally connected to the control circuitry 202. The radio interface may be connected to an antenna or a set of antenna elements 210.

The apparatus may further comprise one or more interfaces 212 operationally connected to the control circuitry 202. The interface may connect the apparatus to other apparatuses of the radio access system. The interface may also be a user interface.

In an embodiment, the applications 206 stored in the memory 204 executable by the control circuitry 202 may cause the apparatus to perform the steps described below.

Fig. 2B illustrates an example of a multiple channel wideband receiver of an embodiment. The multiple antenna receiver consists of multiple receiver paths 212A, 212B, 212C, 212D corresponding to the number of antenna elements. Each receiver path has a RF Front End (RFE) 214A, 214B, 214C, 214D and Analog to Digital Front End (ADFE) 216A, 216B, 216C, 216D operatively connected to a Synthesizer and Clock Circuitry 218. The multiple receiver paths are configured to be capable of handling multiple radio technologies and different modulations under the control of a Digital Vector Signal Processor (DVSEP) 220, which also is configured to handle the requirements of the digital signal processing for decorrelation, time and frame alignment and the decoding of the vector signals. The network control element (NCE) 222 configured to handle backend digital processing and backend operations related to the network.

In an embodiment, the Radio Frequency Front Ends and Analog to

Digital Front Ends may be located in the transceiver and the Digital Vector Signal Processor in the processor of Fig. 2A, but other realizations are possible as well, as one skilled in the art is well aware.

In an embodiment, Digital Vector Signal Processor 220 controls 224 the operation of the Radio Frequency Front Ends and Analog to Digital Front Ends.

In an embodiment, each of the receiver path may be a highly- integrated radio frequency (RF) receiver capable of being configured for a wide range of applications. It may integrate all RF, mixed signal, and digital blocks necessary to provide all receiver functions. The receiver may also provide self- calibration for DC offset and quadrature error correction (QEC) as well as automatic gain control (AGC) under varying temperatures and input signal conditions.

The Radio Frequency Front Ends and Analog to Digital Front Ends may be configured to receive RF signals and convert them to digital data usable by Digital Vector Signal Processor (DVSP). Each receiver may be a direct conversion system that contains a programmable attenuator stage, followed by matched in-phase(l) and quadrature (Q) mixers, and band shaping filters that down convert received signals to baseband for digitization.

Fig. 3 illustrates an example of a Radio Frequency Front End. The frond comprises a Low Noise Amplifier (LNA) 300, a band-pass filter (BPF) 302, a local oscillator (LO) 304, a mixer 306 configured to mix the signal from the band-pass filter with the local oscillator signal. The front end further comprises gain amplifiers (GA) 308, 310 and second band-pass filters (BPF) 312, 314. The signals 316, 318 from the second band-pass filters is fed to the Analog to Digital Front End.

Fig. 4 illustrates an example of an Analog to Digital Front End. In an embodiment, gain control of the received signal is achieved by following a programmed gain index map that distributes attenuation among the blocks for optimal performance for each level. This is achieved by enabling the internal automatic gain control (AGC) allowing the DVSP 208 to make gain adjustments as needed. Additionally, each channel may contain independent Received Signal Strength Indicator (RSSI) measurement capability, DC offset tracking, and circuitries necessary for self-calibration. The Analog to Digital Front End comprises Analog to digital converters (ADC) 400, 402 and adjustable sample rates that produce data streams from the received signals. The signals are processed further by decimation filters and programmable low pass FIR filter (LPF) 404, 406 with additional decimation settings (DEC) 408, 410 as illustrated in the example of Fig. 4. The sample rate of each digital filter block may be adjustable by changing decimation factors to produce the desired output data rate.

Let us assume k mobile devices communicating with an immobile node (access point) with m element antenna where k £ m. Each of the mobile devices transmits a continuous time waveform and let Sk(t), be the discrete time signal of the continuous signal. Let xi(t), X2{t) . .. x_n{t) be the received signal at each of the antenna element.

The immobile node receiver may be of the structure illustrated in the examples of Fig. 2A and 2B. It may be noted that although in this example it is assumed that the receiver is the immobile node, the proposed solution may be utilised also in other nodes.

Figure 5 is a flowchart illustrating an embodiment.

In an embodiment, a receiver controller or processor is configured to receive 500 as an input a transmission comprising signals from multiple sources and sampling the transmission into a vector form. The transmission may be a multisource vector signal x, which is sampled 502 and buffered into a memory where the length of the vector x is less than or equal to the number of antenna array elements m. The memory size is denoted with n.

The received vector sampled signal data x may be scaled both in the analog and digital domain in order to control the gain such that the received signal vectors are at the desired signal level. In an embodiment, the above steps may be executed by Analog to Digital Front End of the receiver.

In an embodiment, the following steps are performed in the controller

202 using the memory 204 or in the Digital Vector Signal Processor (DVSEP) 220.

For each vector sample x, a mean value mp for the vector is determined 504 as mp = E{x] Then an offset vector x_a is determined 506 by subtracting the mean value from the vector signal: x_a = x - mp.

In an embodiment, each vector x_a is stored as a column in a sample matrix X_a, where the size of X_a is m x n, where m is the number of rows and n the number of columns.

In an embodiment, a correlation matrix RX_a is determined by multiplying the sample matrix X_a with the Flermitian conjugate X_a* of the sample matrix: RX_a = E{X_{a *}X_a*}.

The correlation matrix RX_ais decomposed into a rotation matrix Q and a scaling matrix D:

[D, Q] = msig_decomp (RX_a)

where msig_decomp is a decomposition function. Any known decomposition function may be used.

In an embodiment, the sample matrix X_a is rotated by the rotation matrix Q:

Xm = mat_rotate (Q, X_a)

where mat_rotate is a rotating function. Any known rotating function may be used. The rotated matrix X_m is scaled and the signal data is sphered multiplying the rotated matrix by a square root of inverted scaling matrix D ^1/2:

Z = mat_sqrt (matjnverse (D)) * X_m

where mat_sqrt is a square root and matjnverse an inversion function..

In an embodiment, a matrix M is determined by a summing function n

M = ^ k(Z_έ, Z/, Z_¾, z ) where 1 < i,j £ n

k,l= 1

where K(z_i, Z , Z_fe, Z_i ^*) is a function that computes the fourth order cumulant with the scaled rotated matrix Z as the input. Z^ZJ Z^Z_j ^* are vectors and /, j, k, I are integer variables and 1 £ i, j, k, I £ n where n is a complex dimensional random vector and further ZJ and Z\ are Hermitian conjugates of Zy and Zi respectively.

In an embodiment, the matrix M is decomposed to obtain L and E, where L is a matrix of characteristic values and equals diag( i, l₂ ... l n), where diag( ) represents the diagonal of a matrix and li, l₂ ... lh are characteristic values of matrix M. E is a characteristic matrix consisting of characteristic vectors.

A matrix MM is then determined as MM = F(L, E), where F( ) is an function which reshapes the characteristic matrix.

Using matrix MM a matrix F is computed by maximizing a criterion

0(E, C) and by updating the matrix MM, where

v

0(E, C) ^ V \diag(E^HC_rE) \² and updating MM where E is the characteristic matrix and E^H is Hermitian transpose of matrix E, and C_r is a matrix with index r.

While MM is updated F is also updated using Givens rotation, and updating is done by rotation of the matrices

C = {C_r | 1 < r <p}

which are a set of p matrices of size n x n. Cr are arbitrary square matrices of arbitrary number p where r and p are integer variables

In an embodiment, a channel stream matrix S is determined by multiplying the scaled rotated matrix Z and F^H : S =F^H Z where F^H is a Hermitian transpose of matrix F., The channel stream matrix S is stored in a buffer memory, where S of size m x n and S is of the form

Each of the vectors si, S2 ... s_m is further processed by digital gain compensation.

In an embodiment, time and frame alignment of the vectors */, S2 ...

S_m is performed and the access type and modulation used in the transmission of each vector are determined. Examples of possible access types are time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), code division multiple access (CDMA), and phase division multiple access (PDMA). Examples of possible signal modulation are phase shift keying (PSK), frequency shift keying (FSK), amplitude shift keying (ASK), quadrature amplitude modulation (QAM), continuous phase modulation (CPM), frequency modulation (FM), amplitude modulation (AM), amplitude and phase-shift keying (APSK) to name a few.

In determining the access types and signal modulation the respective data streams may be extracted and processed to determine the access type and signal modulation. For example, using a detection process it may be determined that a signal is an LTE signal. The signal is then

determined to see if has a single or two carriers (5 MFIz, 10MFIz, 15MFIz, 20MFIz) and this information leads to determining the sampling rate and the fast Fourier transform (FFT) sizes used. If the access type and the modulation of a signal cannot be determined it may be classified as an unknown signal.

In an embodiment, the vectors are then decoded based on the respective access types and signal modulation to obtain decoded data, and the decoded data is assembled into respective data units containing control and user information for network communication.

Thus in the proposed method the signal vectors are estimated without knowing the array structure with respect to the array deformation and distortion of the received wave front. Identification of the signals is based on the properties of the signal. The proposed solution is computationally efficient technique and cane be implemented in digital vector signal processors, for example.

The steps and related functions described in the above and attached figures are in no absolute chronological order, and some of the steps may be performed simultaneously or in an order differing from the given one. Other functions can also be executed between the steps or within the steps. Some of the steps can also be left out or replaced with a corresponding step.

The apparatuses or controllers able to perform the above-described steps may be implemented as an electronic digital computer, or a circuitry which may comprise a working memory (RAM), a central processing unit (CPU), and a system clock. The CPU may comprise a set of registers, an arithmetic logic unit, and a controller. The controller or the circuitry is controlled by a sequence of program instructions transferred to the CPU from the RAM. The controller may contain a number of microinstructions for basic operations. The implementation of microinstructions may vary depending on the CPU design. The program instructions may be coded by a programming language, which may be a high-level programming language, such as C, Java, etc., or a low-level programming language, such as a machine language, or an assembler. The electronic digital computer may also have an operating system, which may provide system services to a computer program written with the program instructions.

As used in this application, the term‘circuitry’ refers to all of the following: (a) hardware-only circuit implementations, such as implementations in only analog and/or digital circuitry, and (b) combinations of circuits and software (and/or firmware), such as (as applicable): (i) a combination of processor(s) or (ii) portions of processor(s)/software including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus to perform various functions, and (c) circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present.

This definition of ‘circuitry’ applies to all uses of this term in this application. As a further example, as used in this application, the term‘circuitry’ would also cover an implementation of merely a processor (or multiple processors) or a portion of a processor and its (or their) accompanying software and/or firmware. The term ‘circuitry’ would also cover, for example and if applicable to the particular element, a baseband integrated circuit or applications processor integrated circuit for a mobile phone or a similar integrated circuit in a server, a cellular network device, or another network device.

An embodiment provides a computer program embodied on a distribution medium, comprising program instructions which, when loaded into an electronic apparatus, are configured to control the apparatus to execute the embodiments described above.

The computer program may be in source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, which may be any entity or device capable of carrying the program. Such carriers include a non-transitory computer readable medium, record medium, computer memory, read-only memory, and a software distribution package, for example. Depending on the processing power needed, the computer program may be executed in a single electronic digital computer or it may be distributed amongst a number of computers.

The apparatus or parts of the apparatus may also be implemented as one or more integrated circuits, such as application-specific integrated circuits ASIC, field-programmable gate array FPGA, electronically erasable programmable read-only memory EEPROM. Other hardware embodiments are also feasible, such as a circuit built of separate logic components. A hybrid of these different implementations is also feasible. When selecting the method of implementation, a person skilled in the art will consider the requirements set for the size and power consumption of the apparatus, the necessary proc It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The invention and its embodiments are not limited to the examples described above but may vary within the scope of the claims.

Claims

1. A method in an apparatus in a radio access network, comprising: receiving as an input a transmission comprising signals of different access types and modulation formats from multiple sources;

vector sampling the transmission into a vector form and digitizing the samples and scaling the vector form signal to a given signal level jointly in analog and digital domain;

determining a mean value for the vector form signal;

determining an offset vector by obtaining the difference between the mean value and the vector signal.

2. The method of claim 1 , further comprising:

collecting a set of offset vectors as columns in a sample matrix, where n is the number of columns and m the number of rows in the sample matrix;

determining a correlation matrix by multiplying the sample matrix with the Hermitian transpose of the sample matrix;

decomposing the correlation matrix into a rotation matrix and a scaling matrix;

rotating the sample matrix by the rotation matrix;

scaling the rotated matrix by multiplying the rotated matrix by a square root of inverted scaling matrix to obtain scaled rotated matrix Z;

determining a matrix M by a summing function

n

M = ^ k(Z_έ, Z/, Z_¾, z ) where 1 < i,j £ n

k,l= 1

where i,j, k and / are indices between the limits of 1 to n, in the scaled rotated matrix Z, k is a function determining the fourth order cumulant of the matrix Z, Z_it ZJ, Z_k, Z_i are vectors,

decomposing the matrix M to obtain L and E, where L is a matrix of characteristic values and equals diag( i, l₂ ... l n), where diag( ) is a function representing diagonal values of a matrix and li, l₂ ... l n are characteristic values of matrix M and E is a characteristic matrix consisting of characteristic vectors;

determining a matrix MM = F(L, E), where F( ) is an function which reshapes the characteristic matrix;

computing a matrix F is by maximizing a criterion 0(E, C) and by updating the matrix MM utilizing Givens rotation , where

where E is the characteristic matrix and E^H is Hermitian transpose of matrix E, C_r is a matrix with index r;

updating F utilizing Givens rotation by rotation of the matrices C = {C_r 1 1 < r <p) be a set of p matrices of size n x n;

where C_r are arbitrary square matrices of arbitrary number, p and r are integer variables;

determining a channel stream matrix S by multiplying the scaled rotated matrix and F^H which is a Hermitian transpose of matrix F, and storing S in a buffer memory, where S of size m x n and S is of the form

performing digital gain compensation for each of the vectors */, S2 ... Sni

performing time and frame alignment of the vectors */, S2 ... s_m and determining the access type and modulation used in the transmission of each vector;

decoding the vectors based on the respective access types and signal modulation to obtain decoded data, and

assembling the decoded data into respective data units containing control and user information for network communication.

3. The method of claim 1 or 2, further comprising:

receiving the transmission utilizing a given number of antenna elements, and vector sampling the transmission into a vector form where the length of the vector is less than or equal to the number of antenna elements.

4. The method of claim 3, further comprising

processing the signal received by each antenna element with a radio frequency front end and an analog to digital front end connected to each antenna element.

5. The method of any preceding claim, further comprising: scaling the vector form signal to a given signal level before and after digitizing the samples.

6. An apparatus in a radio access network comprising

an analog to digital front end;

at least one digital vector signal processor connected to the digital front end; and

at least one memory including computer program code;

the analog to digital front end being configured to receive as an input a transmission comprising signals of different access types and modulation formats from multiple sources, sample by the analog to digital front end the transmission into a vector form and digitize the samples and scaling the vector form signal to a given signal level jointly in analog and digital domain;

the at least one memory and the computer program code configured to, with the at least one digital vector signal processor, cause the apparatus at least to perform:

determine a mean value for the vector form signal;

7. The apparatus of claim 6, further configured to:

collect a set of offset vectors as columns in a sample matrix, where n is the number of columns and m the number of rows in the sample matrix;

determine a correlation matrix by multiplying the sample matrix with the Hermitian transpose of the sample matrix;

decompose the correlation matrix into a rotation matrix and a scaling matrix;

rotate the sample matrix by the rotation matrix;

scale the rotated matrix by multiplying the rotated matrix by a square root of inverted scaling matrix to obtain scaled rotated matrix Z;

determine a matrix M by a summing function

n

M = ^ k(Z_έ,Z/,Z_¾,z ) where 1 < i,j £ n

k,l= 1

where i,j, and / are indices between the limits of 1 to n, in the scaled rotated matrix Z, k is a function determining the fourth order cumulant of the matrix Z, Z_i ZJ, Z_k, Z\ are vectors, decompose the matrix M to obtain L and E, where L is a matrix of characteristic values and equals diagfii, l₂ ... lh), where diag( ) is a function representing diagonal values of a matrix and li, l₂ ... lh are characteristic values of matrix M and E is a characteristic matrix consisting of characteristic vectors;

compute a matrix F is by maximizing a criterion 0(E, C) and by updating the matrix MM utilizing Givens rotation , where

p

0(E, C) ^ V \diag(E^HC_rE) \² and updating MM where E is the characteristic matrix and E^H is Hermitian transpose of matrix E, C_r is a matrix with index r;

update F utilizing Givens rotation by rotation of the matrices C = {C_r 1 1 < r <p) be a set of p matrices of size n x n;

determine a channel stream matrix S by multiplying the scaled rotated matrix and F^H which is a Hermitian transpose of matrix F, and storing S in a buffer memory, where S of size m x n and S is of the form

perform digital gain compensation for each of the vectors */, S2 ... s_m perform time and frame alignment of the vectors */, S2 ... s_m and determining the access type and modulation used in the transmission of each vector;

decode the vectors based on the respective access types and signal modulation to obtain decoded data, and

assemble the decoded data into respective data units containing control and user information for network communication.

8. The apparatus of claim 6 or 7, further comprising

a given number of antenna elements configured to receive the transmission,

a radio frequency front end and an analog to digital front end connected to each antenna element, the analog to digital front end configured to vector sample the transmission received by the antenna element into a vector form where the length of the vector is less than or equal to the total number of antenna elements.

9. The apparatus of any preceding claim 6 to 8, further configured to: scale the vector form signal to a given signal level before and after digitizing the samples.

10. The apparatus of any preceding claim 6 to 9, wherein the apparatus is an access point in a radio access network.

11. The apparatus of any preceding claim 6 to 9, wherein the apparatus is a user terminal of a radio access network.

12. A computer program comprising instructions for causing an apparatus to perform at least the following:

receive as an input a transmission comprising signals of different access types and modulation formats from multiple sources;

vector sample the transmission into a vector form and digitize the samples and scale the vector form signal to a given signal level jointly in analog and digital domain;

determine a mean value for the vector form signal;

determine an offset vector by obtaining the difference between the mean value and the vector signal.