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>

Power allocation in OFDM-based cognitive radio systems using iterative algorithms

As spectrum scarcity is becoming a serious problem, the worth of finding a general solution for such issue has become even serious due to the rapid development of wireless communications. The main objective of this thesis is to investigate the optimal power allocation procedure that maximizes the capacity in OFDM based Cognitive Radio Systems. The main purpose of the search is to modify the conventional water-filling algorithm applied in general OFDM based Cognitive Radio systems due to the per subchannel power constraints and individual peak power constraints. For Radio Resource Allocation (RRA), one of the most typical problems is to solve power allocation using the Conventional Water- filling. As communication system develops, the structures of the system models and the corresponding RRA problems evolve to more advanced and more complicated ones. In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted radio resource allocation problem with peak power constraint and total subchannel power constraint with channel partitions. The proposed IGPP algorithm requires less computation than the Conventional Water-filling algorithm (CWF). Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative concept which will allow us to solve the same problem intelligently with less complexity. It provides straight forward power allocation analysis, solutions and insights with reduced computation over other approaches under the same memory requirement and sorted parameters.

Power allocation in OFDM-based cognitive radio systems using iterative algorithms

As spectrum scarcity is becoming a serious problem, the worth of finding a general solution for such issue has become even serious due to the rapid development of wireless communications. The main objective of this thesis is to investigate the optimal power allocation procedure that maximizes the capacity in OFDM based Cognitive Radio Systems. The main purpose of the search is to modify the conventional water-filling algorithm applied in general OFDM based Cognitive Radio systems due to the per subchannel power constraints and individual peak power constraints. For Radio Resource Allocation (RRA), one of the most typical problems is to solve power allocation using the Conventional Water- filling. As communication system develops, the structures of the system models and the corresponding RRA problems evolve to more advanced and more complicated ones. In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted radio resource allocation problem with peak power constraint and total subchannel power constraint with channel partitions. The proposed IGPP algorithm requires less computation than the Conventional Water-filling algorithm (CWF). Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative concept which will allow us to solve the same problem intelligently with less complexity. It provides straight forward power allocation analysis, solutions and insights with reduced computation over other approaches under the same memory requirement and sorted parameters.