Leveraging Secondary Reflections and Mitigating Interference in Multi-IRS/RIS Aided Wireless Network

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all RSs to mitigate their interference as well as to leverage the secondary reflections amongst them. Motivated by the above, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require one to optimize the phase-shifts of all RSs' elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by properly managing interference and leveraging the secondary reflections amongst RSs, there is a great benefit of deploying more RSs to support different groups of users and so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. In contrast, without properly managing the secondary reflections, increasing the number of RSs can adversely impact the network throughput, especially for higher transmit power.<br>

Leveraging Secondary Reflections and Mitigating Interference in Multi-IRS/RIS Aided Wireless Network

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all RSs to mitigate their interference as well as to leverage the secondary reflections amongst them. Motivated by the above, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require one to optimize the phase-shifts of all RSs' elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by properly managing interference and leveraging the secondary reflections amongst RSs, there is a great benefit of deploying more RSs to support different groups of users and so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. In contrast, without properly managing the secondary reflections, increasing the number of RSs can adversely impact the network throughput, especially for higher transmit power.<br>

Scalable user selection in FDD massive MIMO

User subset selection requires full downlink channel state information to realize effective multi-user beamforming in frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. However, the channel estimation overhead scales with the number of users in FDD systems. In this paper, we propose a novel propagation domain-based user selection scheme, labeled as zero-measurement selection, for FDD massive MIMO systems with the aim of reducing the channel estimation overhead that scales with the number of users. The key idea is to infer downlink user channel norm and inter-user channel correlation from uplink channel in the propagation domain. In zero-measurement selection, the base-station performs downlink user selection before any downlink channel estimation. As a result, the downlink channel estimation overhead for both user selection and beamforming is independent of the total number of users. Then, we evaluate zero-measurement selection with both measured and simulated channels. The results show that zero-measurement selection achieves up to 92.5% weighted sum rate of genie-aided user selection on the average and scales well with both the number of base-station antennas and the number of users. We also employ simulated channels for further performance validation, and the numerical results yield similar observations as the experimental findings.

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware.<br>Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered.<br>Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. <br>We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system. <br>

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware.<br>Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered.<br>Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. <br>We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system. <br>