ESTIMATION METHOD OF DISCRETE DIGITAL SIGNALS IN NOISY OVERLOADED WIRELESS COMMUNICATION SYSTEMS WITH CSI ERRORS

Abstract

A computer-implemented reconstruction method of discrete digital signals in noisy overloaded wireless communication systems with CSI Errors that is characterized by a channel matrix of complex coefficients, the method including receiving the signal from channel by a signal detector, estimation of the CSI error parameter τ is done at the receiver, estimation noise power is done by a noise power estimator, forwarding the detected signal and the CSI error parameter τ and noise power estimation to a decoder that estimates the transmitted symbol, wherein the estimation of the decoder produces a symbol that could probably have been transmitted it is forwarded to a de-mapper, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol to a microprocessor for further processing.

Claims

1. A computer-implemented reconstruction method of discrete digital signals in noisy overloaded wireless communication systems with CSI errors that is characterized by a channel matrix of complex coefficients, the method including Receiving the signal from channel by a signal detector Estimation of the CSI error parameter τ is done at the receiver Estimation noise power is done by a noise power estimator Forwarding the detected signal and the CSI error parameter τ and noise power estimation to a decoder that estimates the transmitted symbol (s), wherein the estimation of the decoder produces a symbol that could probably have been transmitted it is forwarded to a de-mapper, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol to a microprocessor for further processing.

2. The method of claim 1, wherein channel correlation is assumed to channel estimation error and the CSI error parameter τ is incorporated within the covariance matrix capturing this correlation.

3. The method of claim 1, wherein minimizing the effect of channel estimation error is done by compensating of the noise increase and the channel correlation within the objective function used by decoder.

4. The method of any one of claim 1, wherein the minimizing the effect of channel estimation error to the minimization formulation via a first function, a second function, a third function used to estimate the transmit signal (s) $\underset{x\in {ℂ}^{N_t}}{\min }{\left(\stackrel{\_}{y}-\hat{H}x\right)}^H{.Math.}_{\tilde{n}}^{\mathrm{corr}-1}\left(\stackrel{\_}{y}-\hat{H}x\right)+\frac{{\sigma }_n^2}{1-{\tau }^2}{.Math.x.Math.}_2^2+\lambda \stackrel{.Math.𝒞.Math.}{\underset{i=1}{.Math.}}{.Math.x-c_{i}1.Math.}_0$

5. The method of claim 4, wherein the fractional programming algorithm is targeted to find a value of the third function that is lower than the global minimum of the first function.

6. The method of claim 4, wherein the first function is a Euclidian distance function centred around the received signal's vector including the channel correlation effect.

7. The method of claim 4, wherein the second function is the product of the estimated noise power and transmit signal power scaled by the channel estimation error parameter.

8. The method of claim 4, wherein the third function is a function based on or tightly approximating the l.sub.0-norm.

9. A receiver of a communication system having a processor, volatile and/or non-volatile memory, at least one interface adapted to receive a signal in an communication channel, wherein the non-volatile memory stores computer program instructions which, when executed by the microprocessor, configure the receiver to implement reconstruction of discrete digital signals in noisy overloaded wireless communication systems with CSI errors that is characterized by a channel matrix of complex coefficients, by performing operations including: Receiving the signal from channel by a signal detector Estimation of the CSI error parameter τ is done at the receiver Estimation noise power is done by a noise power estimator Forwarding the detected signal and the CSI error parameter τ and noise power estimation to a decoder that estimates the transmitted symbol (s), wherein the estimation of the decoder produces a symbol that could probably have been transmitted it is forwarded to a de-mapper, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol to a microprocessor for further processing.

10. (canceled)

11. (canceled)

12. The receiver of claim 9, wherein channel correlation is assumed to channel estimation error and the CSI error parameter τ is incorporated within the covariance matrix capturing this correlation.

13. The receiver of claim 9, wherein minimizing the effect of channel estimation error is done by compensating of the noise increase and the channel correlation within the objective function used by decoder.

14. The receiver of any one of claim 9, wherein the minimizing the effect of channel estimation error to the minimization formulation via a first function, a second function, a third function used to estimate the transmit signal (s) $\underset{x\in {ℂ}^{N_t}}{\min }{\left(\stackrel{\_}{y}-\hat{H}x\right)}^H{.Math.}_{\tilde{n}}^{\mathrm{corr}-1}\left(\stackrel{\_}{y}-\hat{H}x\right)+\frac{{\sigma }_n^2}{1-{\tau }^2}{.Math.x.Math.}_2^2+\lambda \stackrel{.Math.𝒞.Math.}{\underset{i=1}{.Math.}}{.Math.x-c_{i}1.Math.}_0$

15. The receiver of claim 14, wherein the fractional programming algorithm is targeted to find a value of the third function that is lower than the global minimum of the first function.

16. The receiver of claim 14, wherein the first function is a Euclidian distance function centred around the received signal's vector including the channel correlation effect.

17. The receiver of claim 14, wherein the second function is the product of the estimated noise power and transmit signal power scaled by the channel estimation error parameter.

18. The receiver of claim 14, wherein the third function is a function based on or tightly approximating the l.sub.0-norm.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Flow charts depicting disclosed methods comprise “processing blocks” or “steps” that may represent computer software instructions or groups of instructions. Alternatively, the processing blocks or steps may represent steps performed by functionally equivalent circuits, such as a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a graphics processing unit (GPU) or a computer processing unit (CPU) programmed with software instructions to perform disclosed methods. It will be appreciated by those of ordinary skill in the art that unless otherwise indicated herein, a particular sequence of steps described is illustrative only and can be varied. Unless otherwise stated, the steps described herein are unordered, meaning that the steps can be performed in any convenient or desirable order.

[0083] The invention will be further explained with reference to the drawings in which:

[0084] FIG. 1 shows a simplified schematic representation of orthogonal multiple access

[0085] a. to a shared medium,

[0086] FIG. 2 shows a simplified schematic representation of non-orthogonal multiple

[0087] b. access to a shared medium,

[0088] FIG. 3 shows an exemplary generalized block diagram of a transmitter and a

[0089] c. receiver that communicate over a noisy communication channel,

[0090] FIG. 4 Performance comparison: proposed solution and prior-art schemes.

DETAILED DESCRIPTION

[0091] FIGS. 1 and 2 have been discussed further above and are not revisited here.

[0092] FIG. 3 shows an exemplary generalized block diagram of a transmitter T and a receiver R that communicate over a communication channel 208. Transmitter T may include, inter alia, a source 202 of digital data that is to be transmitted. Source 202 provides the bits of the digital data to an encoder 204, which forwards the data bits encoded into symbols to a modulator 206. Modulator 206 transmits the modulated data into the communication channel 208, e.g. via one or more antennas or any other kind of signal emitter (not shown). The modulation may for example be a Quadrature Amplitude Modulation (QAM), in which symbols to be transmitted are represented by an amplitude and a phase of a transmitted signal.

[0093] Channel 208 may be a wireless channel. However, the generalized block diagram is valid for any type of channel, wired or wireless. In the context of the present invention the medium is a shared medium, i.e., multiple transmitters and receivers access the same medium and, more particularly, the channel is shared by multiple transmitters and receivers.

[0094] Receiver R receives the signal through communication channel 208, e.g. via one or more antennas or any other kind of signal receiver (not shown). Communication channel 208 may have introduced noise to the transmitted signal, and amplitude and phase of the signal may have been distorted by the channel. The distortion may be compensated for by an equalizer provided in the receiver (not shown) that is controlled based upon channel characteristics that may be obtained, e.g., through analysing pilot symbols with known properties transmitted over the communication channel. Likewise, noise may be reduced or removed by a filter in the receiver (not shown).

[0095] A signal detector 212 receives the signal from channel and 210 tries to estimate the CSI error parameter τ from a series of received signals that accumulate over previous transmissions. Signal detector 212 forwards the estimated signal to a decoder 214 that decodes the estimated signal into an estimated symbol. If the decoding produces a symbol that could probably have been transmitted it is forwarded to a de-mapper 216, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol, e.g., to a microprocessor 218 for further processing.

[0096] A signal detector 210 receives the signal from the channel and tries to estimate, from the received signal, which signal had been transmitted into the channel. Signal detector 210 forwards the estimated signal to a decoder 212 that decodes the estimated signal into an estimated symbol. If the decoding produces a symbol that could probably have been transmitted it is forwarded to a de-mapper 214, which outputs the bit estimates corresponding to the estimated transmit signal and the corresponding estimated symbol, e.g., to a microprocessor 216 for further processing. Otherwise, if the decoding does not produce a symbol that is likely to have been transmitted, the unsuccessful attempt to decode the estimated signal into a probable symbol is fed back to the signal detector for repeating the signal estimation with different parameters. The processing of the data in the modulator of the transmitter and of the demodulator in the receiver are complementary to each other.

[0097] While the transmitter T and receiver R of FIG. 3 appear generally known, the receiver R, and more particularly the signal detector 210 and decoder 212 of the receiver in accordance with the invention are adapted to execute the inventive method described hereinafter and thus operate different than known signal detectors.

[0098] FIG. 4 describes the performance evaluation in comparison with the state-of-the art receivers. It shows the uncoded BER performance evaluation of the proposed method in comparison with the state-of-the-art signal recovery methods.

[0099] As it can be seen from the FIG. 4, the proposed scheme provides significant gain (more than one order of magnitude) in terms of reliability (BER) with respect to existing methods in cases where the CSI estimation error is tolerable (T.sup.2<13 db). In the figure, the total gain has been decomposed in terms of the one due to the Discrete Awareness (DA gain) and the additional one obtained due to the Impairment Awareness (IA), for convenience.

[0100] To realize 5G and Beyond 5G, various technologies are proposed, including Massive multiple-input multiple output (MIMO), Cooperative MIMO, millimeter wave (mmWave) communications, NOMA, device-to-device (D2D), proximity services (ProSe), mobile relays, airborne relays, software-defined networking, fog computing, and distributed Artificial Intelligence (AI). Many infrastructure functions can be pushed with the help of this proposed method to the network's edge to reduce latency, extend coverage, enhance versatility, and exploit the computational resources of the vast number of user devices. Mobile edge computing (MEC) can promptly process computationally intensive jobs offloaded from mobile devices, thus reducing end-to-end latency. Edge computing modules can be in a base transceiver stations, relay, or user equipment.

[0101] Aspects disclosed herein are broadly applicable to wireless standards and use case families disclosed herein, including (but not limited to) cellular, mobile broadband, vehicular ad-hoc networks, fixed broadband, Internet of Things (IoT), peer-to-peer networks, mesh networks, wireless personal area networks (WPANs), wireless local area networks (WLANs), wireless sensor networks, airborne networks, satellite networks, network fabrics, software-defined networks (SDNs), and hybrid networks.