Antenna Selection in Spatial Modulation Systems

Novel transmit antenna selection techniques are conceived for Spatial Modulation (SM) systems and their symbol error rate (SER) performance is investigated. Specifically, low-complexity Euclidean Distance optimized Antenna Selection (EDAS) and Capacity Optimized Antenna Selection (COAS) are studied. It is observed that the COAS scheme gives a better SER performance than the EDAS scheme. We show that the proposed antenna selection based SM systems are capable of attaining a significant gain in signal-to-noise ratio (SNR) compared to conventional SM systems, and also outperform the conventional MIMO systems employing antenna selection at both low and medium SNRs

Near Optimal Receive Antenna Selection Scheme for MIMO System under Spatially Correlated Channel

Spatial correlation is a critical impairment for practical Multiple Input Multiple Output (MIMO) wireless communication systems. To overcome from this issue, one of the solutions is receive antenna selection. Receive antenna selection is a low-cost, low-complexity and no requirement of feedback bit alternative option to capture many of the advantages of MIMO systems. In this paper, symbol error rate (SER) versus signal to noise ratio (SNR) performance comparasion of proposed receive antenna selection scheme for full rate non-orthogonal Space Time Block Code (STBC) is obtained using simulations in MIMO systems under spatially correlated channel at transmit and receive antenna compare with several existing receive antenna selection schemes. The performance of proposed receive antenna selection scheme is same as conventional scheme and beat all other existing schemes

Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems

Energy efficiency (EE) is a critical design when taking into account circuit power consumption (CPC) in fifth-generation cellular networks. These problems arise because of the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, attributable to inter-cell interference for channel state information. Apart from that, a higher number of radio frequency (RF) chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers. Therefore, antenna selection, user selection, optimal transmission power, and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems. This work aims to investigate joint antenna selection, optimal transmit power and joint user selection based on deriving the closed-form of the maximal EE, with complete knowledge of large-scale fading with maximum ratio transmission. It also accounts for channel estimation and eliminating pilot contamination as antennasM→∞. This formulates the optimization problem of joint optimal antenna selection, transmits power allocation and joint user selection to mitigate inter-cellinterference in downlink multi-cell massiveMIMO systems under minimized reuse of pilot sequences based on a novel iterative low-complexity algorithm (LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise power consumption, a novel power consumption scheme is proposed for each individual antenna, based on the transmit power amplifier and CPC. Simulation results demonstrate that the maximal EE was achieved using the iterative LCA based on reasonable maximum transmit power, in the case the noise power is less than the received power pilot. The maximum EE was achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the optimal EE=71.232 Mb/j

Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

Transmit antenna selection for multiuser massive mimo

In massive multiple input multiple output (MIMO) systems, major challenges are present due to the large number of active antennas and radio frequency (RF) chains,suchasincreasedpowerconsumptionandcomputationcomplexity. Transmitantennaselection(TAS)isbeinginvestigatedasasolutiontotacklethesechallenges. In this thesis, a dynamic transmit antenna selection technique is proposed whichcanmaximizethesumrateofamultiuser(MU)-MIMOcommunicationsystem. In order to satisfy the objective, the number of transmit antennas required is determined by remodeling it as a binary Knapsack Problem (KP) and then extending to a Multiple KP (MKP) for MU-MIMO. Furthermore, an improvement in the decision making is also proposed with the introduction of a ?exible decision criteria, whilst reducing the structure of the MKP to resemble that of a single binary KP. Additionally, comparisons of the KP based algorithms are done with two low complexity techniques, which are the sequential selection algorithm and random selection algorithm. Results show that the KP based techniques outperform these low complexity techniques. The modi?ed binary KP algorithm is also superior to that of the MKP, as it is not sensitive to solving as binary knapsack sub-problems. The proposed technique has good performance for di?erent antenna selection measures and is suitable to ensure communication e?ciency in future wireless communication systems