Existing Generalized Receive Spatial Modulation(GRSM)with Symbol-Level Precoding(SLP)forces the received signals(excluding noise)at unintended antennas to be zero,which restricts the generation of strong constructive ...Existing Generalized Receive Spatial Modulation(GRSM)with Symbol-Level Precoding(SLP)forces the received signals(excluding noise)at unintended antennas to be zero,which restricts the generation of strong constructive interference to intended receive antennas and thus limits the performance improvement over conventional GRSM with Zero-Forcing(ZF)precoding.In this paper,we propose a novel GRSM-SLP scheme that relaxes the zero receive power constraint and achieves superior performance by integrating Intelligent Reflecting Surfaces(IRSs).Specifically,our advanced GRSM-RSLP jointly exploits SLP at the transmitter and passive beamforming at the IRS to maximize the power difference between intended and unintended receive antennas,where the received signals at unintended antennas are relaxed to lie in a sphere centered at origin with a preset radius that depends on the Signal-to-Noise Ratio(SNR)value.The precoding matrix and passive beamforming vectors are optimized alternately by considering both phase shift keying and quadrature amplitude modulation signaling.It is worth emphasizing that GRSM-RSLP is a universal solution,also applicable to systems without IRS,although it performs better in IRS-assisted systems.We finally conduct extensive simulations to prove the superiority of GRSM-RSLP over GRSM-ZF and GRSM-SLP.Simulation results show that the performance of GRSM-RSLP is significantly influenced by the number of unintended antennas,and the larger the number,the better its performance.In the best-case scenario,GRSM-RSLP can achieve SNR gains of up to 10.5 dB and 12.5 dB over GRSM-SLP and GRSM-ZF,respectively.展开更多
Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible ...Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible domain of a1-bit transmitting signal is non-continuous, and thus it is more challenging to exploit multi-user interference(MUI) by precoding. In this paper, to improve symbol decision accuracy, we investigate MUI exploitation 1-bit precoding methods for massive MU-MIMO systems under QAM modulations. Because MUIs may be constructive or destructive, we define a modified mean square error(MSE) metric for QAM constellations to jointly evaluate the effect of both MUIs and noise. Then, we model the 1-bit precoding optimization problems to minimize the sum modified MSE or the maximum modified MSE, where both the transmitting vector and receiving processing factor are optimization variables. Based on whether the receiving processing factor remains constant during the whole transmission block, two scenarios are taken into consideration. Referring to existing interference exploitation 1-bit precoding methods, we design efficient algorithms to solve the two modified MSE based problems.Compared to existing 1-bit precoding methods, our proposed methods provide better bit error rate performance, especially in more practical scenario Ⅱ(constant receiving processing factor in one block).展开更多
基金supported in part by the National Key R&D Program of China under Grant 2023YFB2904500in part by the National Natural Science Foundation of China under Grant 62471183in part by the Fundamental Research Funds for the Central Universities under Grant 2024ZYGXZR076.
文摘Existing Generalized Receive Spatial Modulation(GRSM)with Symbol-Level Precoding(SLP)forces the received signals(excluding noise)at unintended antennas to be zero,which restricts the generation of strong constructive interference to intended receive antennas and thus limits the performance improvement over conventional GRSM with Zero-Forcing(ZF)precoding.In this paper,we propose a novel GRSM-SLP scheme that relaxes the zero receive power constraint and achieves superior performance by integrating Intelligent Reflecting Surfaces(IRSs).Specifically,our advanced GRSM-RSLP jointly exploits SLP at the transmitter and passive beamforming at the IRS to maximize the power difference between intended and unintended receive antennas,where the received signals at unintended antennas are relaxed to lie in a sphere centered at origin with a preset radius that depends on the Signal-to-Noise Ratio(SNR)value.The precoding matrix and passive beamforming vectors are optimized alternately by considering both phase shift keying and quadrature amplitude modulation signaling.It is worth emphasizing that GRSM-RSLP is a universal solution,also applicable to systems without IRS,although it performs better in IRS-assisted systems.We finally conduct extensive simulations to prove the superiority of GRSM-RSLP over GRSM-ZF and GRSM-SLP.Simulation results show that the performance of GRSM-RSLP is significantly influenced by the number of unintended antennas,and the larger the number,the better its performance.In the best-case scenario,GRSM-RSLP can achieve SNR gains of up to 10.5 dB and 12.5 dB over GRSM-SLP and GRSM-ZF,respectively.
文摘Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible domain of a1-bit transmitting signal is non-continuous, and thus it is more challenging to exploit multi-user interference(MUI) by precoding. In this paper, to improve symbol decision accuracy, we investigate MUI exploitation 1-bit precoding methods for massive MU-MIMO systems under QAM modulations. Because MUIs may be constructive or destructive, we define a modified mean square error(MSE) metric for QAM constellations to jointly evaluate the effect of both MUIs and noise. Then, we model the 1-bit precoding optimization problems to minimize the sum modified MSE or the maximum modified MSE, where both the transmitting vector and receiving processing factor are optimization variables. Based on whether the receiving processing factor remains constant during the whole transmission block, two scenarios are taken into consideration. Referring to existing interference exploitation 1-bit precoding methods, we design efficient algorithms to solve the two modified MSE based problems.Compared to existing 1-bit precoding methods, our proposed methods provide better bit error rate performance, especially in more practical scenario Ⅱ(constant receiving processing factor in one block).
