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Code-Division OFDM Joint Communication and Sensing System for 6G Machine-type Communication

Xu Chen, Zhiyong Feng, Zhiqing Wei, Ping Zhang, Xin Yuan

arXiv:2304.01517v1cs.ITeess.SPmath.IT

TL;DR

Dense 6G MTC must meet demanding reliability and sensing requirements despite severe interference at low SINR. The paper proposes SIC-based CD-OFDM JCS with code-division gain; simulations report lower BER than OFDM JCS and comparable sensing RMSE to TDD OFDM when code-channel use is appropriate.

  • Problem

    Dense MTC users experience severe mutual interference, making conventional OFDM JCS reliability difficult to ensure at low SINR amid demanding communication and sensing requirements.

    Source: A. Background and Motivations · p. 1, B. Related Works · p. 2

  • Method

    The paper proposes a CD-OFDM JCS signal and SIC-based processing that spreads symbols with orthogonal DSSS codes to obtain CDM gain.

    Source: B. The CD-OFDM Signal Model · p. 4

  • Results

    Approximately 30.1 dB CDM gain with 1 code channel; simulations report lower BER than OFDM JCS and comparable sensing RMSE to TDD OFDM JCS with a proper number of code channels.

    Source: §IV · pp. 10, 12

  • Takeaways & Limitations

    MTC devices can configure the code-channel count to adjust CDM gain and BER to satisfy reliability constraints.

    Source: §IV · p. 10

Abstract

from arXiv · show

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load-saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar sensing signals to improve the communication reliability and radar sensing accuracy, we propose a novel code-division orthogonal frequency division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. {\color{black} We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancellation (SIC) based signal processing technique that obtains code-division multiplex (CDM) gain, which is compatible with the prevalent orthogonal frequency division multiplex (OFDM) communication system.} To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system {\color{black} and the error propagation performance}. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in low signal-to-interference-and-noise ratio (SINR) regime.

I. INTRODUCTION

Dense 6G MTC demands high communication reliability, sensing accuracy, and efficient spectrum use. The paper proposes a CD-OFDM JCS system and signal processing approach to address these requirements.

A. Background and Motivations

  • By 2030, IoT connections are projected to reach 30 billion, while critical MTC applications require 99.9999% reliability and sensing accuracy of 0.1 m indoors or 1 m outdoors.The projected growth and requirements intensify demands on spectrum and energy efficiency.Source: A. Background and Motivations · p. 1

B. Related Works

  • Dense MTC users face severe mutual interference and minimum-range risks, while conventional OFDM JCS can struggle to ensure high reliability at low SINR.A single-array transceiver may miss a nearby target when it is not receiving as the radar echo returns.Source: B. Related Works · p. 2

C. Our Contributions

  • The proposed CD-OFDM JCS system uses double-array transceivers for simultaneous, constant communication and sensing over unified spectrum and transceivers.The system targets low-speed MTC scenarios and is compatible with OFDM JCS processing.Source: C. Our Contributions · p. 2, Figure 1 · p. 2
  • The paper proposes SIC-based CD-OFDM processing to obtain code-division gain and a unified channel model relating communication and radar-echo fading, power, delay, and Doppler.The channel model is based on MIMO communication and radar channel models.Source: C. Our Contributions · p. 2

D. Outline of This Paper

  • The paper introduces its system and channel models, presents SIC-based signal processing and simulations, and concludes with notation conventions for matrices, vectors, and operations.The paper's outline places the system and channel model in Section II, processing in Section III, and simulations in Section IV.Source: D. Outline of This Paper · p. 2

A. The CD-OFDM JCS MTC System Model

The system models low-speed MTC users communicating and sensing simultaneously over a shared spectrum. Its double-array transceivers support constant reception, while a SINR-based switch selects OFDM or CD-OFDM processing.

A. The CD-OFDM JCS MTC System Model

  • Each low-speed MTC device uses a double-array transceiver for simultaneous, constant communication and radar sensing with unified spectrum and transceivers.The devices are assumed to have considerable radar cross section.Source: A. The CD-OFDM JCS MTC System Model · p. 2
  • In-band full-duplex operation keeps the receiver active to address the minimum-range problem, but TxA-to-RxA self-interference requires isolation shielding and leakage cancellation.Residual self-interference from quantization error is modeled as noise.Source: A. The CD-OFDM JCS MTC System Model · p. 3
  • Communication uses one-way links, while radar echoes traverse transmit and reflection paths and are weaker, so they are obtained after the communication signal is canceled.The system assumes estimated communication CSI and channel reciprocity.Source: A. The CD-OFDM JCS MTC System Model · p. 3
  • An SINR threshold selects CD-OFDM with DSSS spreading at low SINR and conventional OFDM at high SINR, avoiding extra computation when OFDM meets reliability constraints.The switch compares the threshold with SINR obtained in communication channel estimation.Source: A. The CD-OFDM JCS MTC System Model · p. 3, Figure 2 · p. 3
  • The paper treats angle detection as outside its innovation scope, noting that existing MIMO-OFDM methods such as MUSIC can be applied.Source: A. The CD-OFDM JCS MTC System Model · p. 3
  • The receiver first demodulates and decodes communication, optionally despreads, cancels the reconstructed communication signal, and then detects radar from the residual.The DSSS switch determines whether despreading is used.Source: A. The CD-OFDM JCS MTC System Model · p. 3

B. The CD-OFDM Signal Model

The signal model spreads information symbols across subcarriers with DSSS codes before OFDM modulation. Orthogonality among the codes provides CDM gain, and the paper specifies the corresponding time- and frequency-domain representations.

B. The CD-OFDM Signal Model

  • The time-domain expression represents the ith CD-OFDM symbol transmitted on the kth code channel, with subcarrier spacing Δf = 1/T = B/Nc.Here, T is the elementary CD-OFDM symbol duration, B is bandwidth, and Nc is the number of subcarriers.Source: B. The CD-OFDM Signal Model · pp. 3–4
  • The model uses Walsh-Hadamard DSSS codes, with Nc codes available and each code assigned to a channel transmitting information symbols.The code vectors are orthogonal.Source: B. The CD-OFDM Signal Model · p. 4
  • Sampling the transmitted waveform at Nc/T produces a discrete time-domain signal vector, which the model also rewrites using Fourier transforms.The sampling interval is the useful symbol interval after the cyclic prefix.Source: B. The CD-OFDM Signal Model · p. 4
  • The frequency-domain symbols linearly superpose information symbols spread by the code-book matrix, and code orthogonality provides CDM gain.The normalized signal vector has entries with the same power as the information-symbol vector entries.Source: B. The CD-OFDM Signal Model · p. 4

C. The Unified JCS Channel Model

The unified JCS channel model relates communication and radar channel responses to describe the joint signal’s propagation. It captures their shared features and key differences, including the radar channel’s two-way path.

C. The Unified JCS Channel Model

  • Communication uses a one-way channel, whereas radar uses a two-way transmit-and-reflect channel with radar delay double the communication delay.The model also notes a strong LoS path between users and correlated communication and radar channel average power and phase, with RCS adding fading.Source: C. The Unified JCS Channel Model · pp. 4–5
  • The model addresses the lack of detailed JCS channel models relating fading coefficients, average power, delay, and Doppler.These quantitative relations are presented as necessary for modeling JCS propagation.Source: C. The Unified JCS Channel Model · p. 4
  • The communication and radar channels share a transmitting beam, while their distinct fading includes radar reflection and RCS effects.The communication channel response is reciprocal between the two user directions.Source: C. The Unified JCS Channel Model · p. 5
  • Frequency-domain communication CSI is used to equalize communication distortion, while radar channel information supports delay and Doppler estimation.Radar information is obtained by correlating the received echo with the known transmitted signal.Source: C. The Unified JCS Channel Model · p. 5

III. JCS SIGNAL PROCESSING

The proposed CD-OFDM JCS processing uses successive interference cancellation (SIC) to separate communication and radar signals. Its code-division gain improves communication reliability, including at low SINR, and reduces error propagation affecting radar sensing.

III. JCS SIGNAL PROCESSING

  • User 1 demodulates user 2’s communication signal while treating radar echoes as small interference, then cancels the restored signal from the superposition.The residual contains radar signal and AWGN only if communication demodulation is correct.Source: §III · p. 5
  • Communication demodulation BER and SINR both directly affect JCS radar detection accuracy.Source: §III · p. 5
  • Code-division processing provides CDM gain that improves communication reliability at low SINR and reduces error propagation affecting IBFD radar sensing.The passage contrasts the proposed system with conventional OFDM JCS.Source: §III · p. 6

A. Communication Demodulation and Radar Signal Acquirement

The receiver uses code-channel processing to demodulate the other user’s communication signal from the superposed JCS reception. The code-division gain suppresses noise and multiple-access interference, including radar echoes.

A. Communication Demodulation and Radar Signal Acquirement

  • The received signal at user 1 includes user 2’s communication symbol, a radar echo, and co-channel interference plus noise.The communication and radar components are represented using their respective channel responses and DSSS code matrices.Source: A. Communication Demodulation and… · p. 6
  • User 1 despreads the received signal with the Hermitian code-channel matrix to detect user 2’s transmitted communication symbols.The communication channel is inverted as part of the processing.Source: A. Communication Demodulation and… · p. 6
  • CDM gain suppresses noise and multiple-access interference, including co-channel communication interference and radar echoes.The cited passage expresses the gain for the communication receiver of MTC user 1.Source: A. Communication Demodulation and… · p. 6

B. The CD-OFDM Radar Signal Processing

Radar processing removes the demodulated communication signal, then estimates the echo’s delay and Doppler shift from the remaining signal. Communication decoding errors can propagate into radar sensing, while the CD-OFDM processing has lower error-propagation power than OFDM JCS.

B. The CD-OFDM Radar Signal Processing

  • After communication demodulation, the receiver removes the communication signal from the superposed signal to retain the radar echo and error-plus-interference terms.Source: B. The CD-OFDM Radar Signal Processing · p. 6
  • Communication decoding errors directly interfere with radar sensing, and CD-OFDM has lower error-propagation power than OFDM JCS.The text identifies high communication reliability as a prerequisite for high radar detection performance.Source: B. The CD-OFDM Radar Signal Processing · p. 6
  • Multicarrier radar detection uses Ms transmitted and received JCS symbols demodulated with the same DSSS code books.Source: B. The CD-OFDM Radar Signal Processing · p. 6
  • Element-wise division between transmitted and received frequency-domain symbols forms a correlation matrix used to recover radar-channel delay and Doppler information.The radar-channel response contains the Doppler and delay information, alongside noise and SIC error-propagation terms.Source: B. The CD-OFDM Radar Signal Processing · p. 7
  • Both code-channel counts must be odd to prevent zero entries in the transmitted-symbol matrix and keep radar detection valid.The passage states that zero entries would make the phase-shift matrix undefined.Source: B. The CD-OFDM Radar Signal Processing · p. 7
  • IFFT across matrix columns and FFT across rows produce modulus peaks whose indices determine the users’ time delay and Doppler shift.The peak indices are found by one-dimensional search; the delay and Doppler then give distance and radial relative velocity.Source: B. The CD-OFDM Radar Signal Processing · p. 7

C. The OFDM JCS Signal Processing

The OFDM JCS processing is implemented as a compatible configuration of the CD-OFDM system, using identity matrices in place of DSSS precoding. The CD-OFDM SINR is related to OFDM SINR through the code-division multiplex gain.

C. The OFDM JCS Signal Processing

  • The processing can be made compatible by modifying baseband software without changing the OFDM RF front end.The DSSS switch selects whether CD-OFDM or OFDM processing is used.Source: C. The OFDM JCS Signal Processing · p. 8
  • Setting the DSSS precoding matrices to identity makes the CD-OFDM system equivalent to OFDM JCS processing.The information vectors have dimension Nc×1, and both scaling factors are set to 1.Source: C. The OFDM JCS Signal Processing · p. 8
  • The CD-OFDM SINR at a subcarrier is expressed relative to OFDM SINR using the CDM gain.The passage identifies ηC as the CDM gain but does not provide the full expression.Source: C. The OFDM JCS Signal Processing · p. 8

D. Error Propagation Analysis

The analysis characterizes error propagation under SIC using the expected squared error of detected symbols. It concludes that CDM gain lowers interference-plus-noise variance and suppresses error propagation relative to OFDM JCS processing.

D. Error Propagation Analysis

  • The analysis models each entry of the error-propagation matrix as identically distributed and focuses on its expected squared magnitude.The average error propagation power is obtained by considering combinations of transmitted and decoded symbols.Source: D. Error Propagation Analysis · pp. 8–9
  • For M-QAM, the analysis treats the constellation as independent in-phase and quadrature PAM components and assumes unit average power.The received symbol is modeled as Gaussian, and maximum-likelihood detection determines the decoded-symbol probabilities.Source: D. Error Propagation Analysis · p. 8
  • The error-power analysis assumes independently and identically distributed in-phase and quadrature symbol components drawn uniformly from the PAM constellation.The component values are associated with transmitted and decoded PAM symbols.Source: D. Error Propagation Analysis · p. 8
  • CD-OFDM has lower interference-plus-noise variance than OFDM, resulting in smaller BER and suppressed error propagation.The comparison attributes the variance reduction to CDM gain.Source: D. Error Propagation Analysis · p. 9

IV. SIMULATION AND NUMERICAL RESULTS

Simulations compare CD-OFDM and OFDM communication and sensing, with TDD OFDM as a sensing and communication reference. CD-OFDM offers configurable communication reliability and sensing performance, with computation cost and code-channel count shaping the trade-offs.

IV. SIMULATION AND NUMERICAL RESULTS

  • The simulation compares CD-OFDM and OFDM processing achievable within the proposed system against TDD OFDM, which avoids communication/radar mutual interference by assigning transmission to one device at a time.The simulations generate communication and sensing channel-state matrices before performing JCS signal processing.Source: §IV · p. 9
  • CD-OFDM has lower BER than OFDM, with approximately 30.1, 6.03, and 3.02 dB CDM gain at 1, 255, and 511 code channels, respectively.The BER of OFDM and TDD OFDM processing is approximately equal.Source: §IV · p. 10
  • At equal AEPP constraint, 1- and 511-code CD-OFDM provide 30 dB and 3 dB SINR gain over OFDM, respectively; more code channels increase AEPP.The AEPP comparisons use normalized unit transmit power under 4-, 16-, and 64-QAM.Source: §IV · pp. 10–11, Figure 6 · p. 10, Figure 7 · p. 10
  • One-code CD-OFDM has better sensing RMSE than OFDM at a given SINR, while increasing code-channel count deteriorates sensing RMSE.The velocity-estimation curves are reported to follow the same variation pattern as the corresponding ranging curves.Source: Figure 8 · p. 10, Figure 9 · p. 10, §IV · p. 11
  • CD-OFDM provides more reliable communication and comparable sensing to OFDM/TDD, while its configurable CDM gain costs more computation and can approach TDD sensing with a proper code-channel count.The sensing comparison covers radar ranging and velocity RMSE; the reported setup considers two devices moving in the same straight direction.Source: Figure 8 · p. 10, §IV · pp. 11–12, Figure 11 · p. 11

V. CONCLUSION

The conclusion presents CD-OFDM JCS for low-speed 6G MTC and reports its communication and sensing performance relative to conventional OFDM JCS. It recommends choosing the processing mechanism according to real-time SINR and the scenario’s sensing and communication requirements.

V. CONCLUSION

  • CD-OFDM JCS achieves better BER than OFDM JCS at low SINR, while OFDM JCS matches its communication performance at high SINR, at the cost of more computation for CD-OFDM.The conclusion also reports comparable sensing RMSE to TDD OFDM JCS when a proper number of code channels are used.Source: §V · p. 12
  • The proposed system combines a CD-OFDM JCS signal and SIC-based processing with a unified channel model based on MIMO communication and MIMO radar channel models.The simulations compare BER, radar ranging and Doppler estimation RMSE, and average error propagation power against conventional OFDM JCS.Source: §V · p. 12
  • For practical use, choose CD-OFDM or OFDM JCS according to real-time SINR and the MTC scenario’s sensing and communication requirements.Source: §V · p. 12

APPENDIX A PROOF OF THEOREM 1

The proof uses the code-matrix and constellation notation to show that the transmitted entry cannot be zero when the code-channel count is odd, while an even count can permit zero.

APPENDIX A PROOF OF THEOREM 1

  • The proof defines c^k_{i,m,n} as an entry of C_i^k and d^k_{i,n} as an M-ary QAM constellation point.Source: Appendix A · p. 12
  • For even N_C, sequences of symmetric constellation points can make d^k_{T X,i}(m) zero, so N_C cannot be even under the proof’s stated condition.Source: Appendix A · p. 12
  • For odd N_C, the transmitted entry d^k_{T X,i}(m) cannot be zero, as a zero would require an even sum of constellation points that cannot be an M-ary QAM constellation entry.Source: Appendix A · p. 12
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