RAISE: A New Fast Transmit Antenna Selection Algorithm for Massive MIMO Systems

In multiple-input multiple-output (MIMO) systems, transmit antenna selection is a very promising technique for reducing implementation cost and computational complexity. In this paper, we propose two novel fast near-optimal antenna selection algorithms based on channel capacity maximization, whose core idea is to use an effective iterative process to reduce computational complexity and retain near-optimal performance. Compared with the conventional algorithm, the proposed algorithms have lower computational complexity and achieve almost the same capacity and bit-error rate (BER) performance as the optimal one. In particular, the BER performance gain of the proposed algorithms is much larger than the conventional algorithms in correlated fading channels. Finally, computer simulations are presented to validate and demonstrate performance of our algorithms.

Low complexity transmit antenna selection for spatial multiplexing systems

The Transmit antenna selection (TAS) technique is essential to increase the efficiency of spatial modulation (SM) systems. This TAS is an effective technique for reducing the Multiple Input Multiple Output (MIMO) systems, which can increase the computational difficulty and Bit error rate (BER) by various TAS algorithms. But these selection methods cannot provide code gain, so it is essential to join the external code in TAS to obtain code gain advantages in BER. In some existing work, the improved BER has been perceived by joining Forward Error Correction Code (FEC) and Space Time Block Code (STBC) for MIMO systems provided greater code gain. A multiple TAS-OSTBC technique with new integration of Bose–Chaudhuri–Hocquenghem (BCH)-Turbo code (TC) is proposed in this paper. The TAS-OSTBC system connects with the external BCH code in sequence of internal Turbo code. This combination can provide increasing code gain and the effective advantages of the TAS-OSTBC system. To perform the system analysis Rayleigh channel is utilized. In the case with multiple TAS-OSTBC systems, better performance can provide by this new combination of the BCH-Turbo compared to the conventional Turbo code for the Rayleigh fading.

Massive MIMO is a multi-user MIMO (multiple-input multiple output) technology that has the ability to provide uniformly better service to wireless terminals in high-mobility environs. The major concept behind this is to equip base stations with arrays of more antennas that are utilized to serve various terminals simultaneously. The term "massive" refers to the count of antennas and not the physical size. However, deployment of multiple antennas at each node obviously maximizes the hardware cost, system complexity, and transmitter power. Particularly, in MIMO systems, each antenna needs a specific radio frequency chain that rapidly increases the system complexity and is unpleasant since the antenna count increases. Therefore, it is disincentive to employ multiple antennas at all the nodes. As a solution to this, transmit antenna strategy (T AS) is evolved that minimizes system complexity, hardware cost and so on. Still, the problem rises in the selection criteria of transmit antenna. Recently, more research works have been focusing on this issue by offering advanced algorithms or models. Such a way that, this research work concentrates on optimal transmit antenna selection to solve the problems like system complexity, cost, etc. In this paper an improved optimization model is introduced for the optimal selection of antenna. The proposed algorithm is named as Modified Velocity vector-based GSA (MV -GSA), which is the enhanced version of Gravitational Search Algorithm (GSA) that defines the count of antenna. This work goes on with the determination of solving multi-objective problem that increases the capacity and relative Energy Efficiency (EE) as well. At last, this model proved as better performer compared over conventional models, like Artificial Bee Colony (ABC), Genetic Algorithm (GA), Firefly (FF) Particle Swarm Optimization (PSO), and conventional GSA, respectively.

In this paper, a new transmit antenna selection algorithm for spatial multiplexing ultra‐wideband multiple‐input multiple‐output systems is proposed. It utilizes the null space of the channel matrix spatially and temporally combined for available transmit antennas. A selection strategy is used to successively find and remove the transmit antennas having the largest contribution to the null space. Its error performance is shown to be close to that of the optimal antenna selection algorithm with low complexity. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This paper studies the performance of transmit antenna selection algorithms in spatially correlated fading MIMO channels. The antenna subset is selected based on maximizing the channel capacity. A novel method is proposed to select the optimum set of transmit antennas based on the upper bound on the mean channel capacity. The proposed method based only on the fading correlation and not the instantaneous channel realization. And also its computational complexity is low and tends to be realized. The simulation results show that antenna selection of transmitter can increase the channel capacity of MIMO system by 1 bit/s in a flat fading environment with a rank deficient channel.

Full-duplex (FD) distributed antenna system (DAS) can take advantage of both FD and DAS to dramatically improve system capacity. The challenges of designing such a system are self-interference at the base station (BS) and multiuser interference, as well as hardware cost, computational complexity and signaling overhead, especially for dense antenna deployments. To address these problems mentioned above, in this paper, we investigate an antenna selection strategy at the BS for FD DAS including FD-capable BS antennas and half-duplex (HD) users. In particular, we separately optimize the receive and transmit antenna selection problems to maximize the achievable sum rate. To reduce the computational complexity and signaling overhead, each user is restricted to selecting only the BS antennas in its virtual cell. The optimization problem is a nonconvex integer programming problem, for which it is difficult to find a globally optimal solution. Using variable relaxation and successive convex approximation, we present an iterative antenna selection algorithm based on difference of convex functions (D.C.) programming to obtain a suboptimal solution. The simulation results demonstrate that the proposed antenna selection algorithm can provide significant performance gain over various reference algorithms.

Existing fast antenna selection algorithms have either low performance gains or they are efficient only for the selection of particular size sub-matrices. Our motivation to the study of NP-Hard antenna selection problem is two-fold. First, we intend to propose an optimal algorithm that is equally efficient for each possible subset cardinality of the transmit and/or receive antennas. Second, different from any of the antenna selection algorithms proposed so far, our algorithm deterministically increases the strength of the singular values during the selection of each column of the selected matrix. This increase in the singular values is defined by a very tight bound. This bound has previously been used to analyze the efficiency of low rank approximation algorithms. Hence, our antenna selection procedure is as efficient as that of a best algorithm for matrix low rank approximations.

This paper applies transmit antenna selection algorithms to spatial-temporal combining-based spatial multiplexing (SM) ultra-wideband (UWB) systems. The employed criterion is based on the largest minimum output signal-to-noise ratio of the multiplexed streams. It is shown via simulations that the bit error rate (BER) performance of the SM UWB systems based on the two-dimensional Rake receiver is significantly improved by antenna diversity through transmit antenna selection on a log-normal multipath fading channel. When the transmit antenna diversity through antenna selection is exploited in the SM UWB systems, the BER performance of the spatial-temporal combining-based zero-forcing (ZF) receiver is also compared with that of the ZF detector followed by the Rake receiver.

In this paper, transmit antenna selection (TAS) is applied to space shift keying (SSK) modulation to obtain diversity gain. The receiver selects a transmit antenna subset in each transmission block according to the channel state information (CSI) and sends the subset index to the transmitter through feedback. Several antenna selection schemes are evaluated for SSK from perspectives of performance, feedback overhead and complexity. A greedy transmit antenna selection scheme is proposed to reduce the computation complexity, which shows almost the same performance as exhaustive search. In the special case that the number of active antennas equals two, an antenna selection scheme with one transmit antenna fixed (Fix-1 scheme) is proposed to reduce the amount of feedback, whose exact bit error ratio (BER) performance is derived. Through asymptotic BER analysis we show that Fix-1 scheme has the same diversity order as exhaustive search.

In this paper, a new transmit antenna selection (TAS) algorithm which gives optimum error performance while reducing the computational complexity is proposed for spatial modulation (SM) systems. The idea of the proposed algorithm is based on the eigen decomposition of the matrix composed from the transmit signal difference vector. Although eigen decomposition has itself weighty computational complexity, finding only the base vectors which also reduces the complexity will suffice for the algorithm since the eigenvectors can be defined up to a phase.

The conventional transmit antenna selection in massive multiple-input multiple-output multiple antenna eavesdropper (MIMOME) channels has been focused on the optimal transmit antenna selection corresponding to a given number of transmit antennas to select. To maximize the secrecy capacity, the antenna selection should be optimized considering the number of transmit antennas to select. In this letter, we propose the greedy search based optimal transmit antenna selection (G-OTAS) algorithm to maximize the secrecy capacity by pursuing the optimal number of transmit antennas in massive MIMOME channels. Throughout the simulation and numerical results, it is demonstrated that the proposed G-OTAS algorithm maximizes the secrecy capacity and effectively reduces the computational complexity compared to those of the reference schemes.

Knowledge of the propagation channel makes it possible for multiple-input multiple-output (MIMO) links to achieve higher system capacities by means of beamforming on the stronger channel eigenvectors. Assigning dedicated low bit rate feedback links is an efficient and practical method of providing transmitters with the required channel state information. One of the key problems in limited feedback precoding is the design of finite indexed codebooks known a priori to both the transmitter and receiver.Previous design methods include signal-to-noise ratio maximization through Grassmannian line packing, distortion minimization via vector quantization and antenna selection algorithms. In this paper we propose a new method that utilizes the dominant right singular vector of the MIMO channel to construct quantized unitary precoders. We show that although sub-optimum, this method can produce full diversity order codebooks. Using Monte-Carlo simulations, we compare the performance of the this method with optimum Grassmannian precoders and also antenna selection and subset selection algorithms.

Intelligent reflecting surface (IRS), which uses passive reflective elements instead of active radio frequency (RF) chains, is a cost and energy-efficient solution to improve the wireless system performance. With a similar objective, transmit antenna selection (AS) reduces the number of RF chains at the base station while harnessing the benefits of multiple antennas. In our work, we focus on joint optimization of antenna subset and transmit beamforming at the base station, and passive beamforming at the IRS to maximize the receive signal power. For single AS, we first derive a closed-form optimal rule. We then propose a simpler AS rule, which significantly reduces the computational complexity and the number of pilot transmissions required. For a system with Nt antennas at the base station and N IRS elements, the optimal AS rule requires N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> + N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> N pilots. However, the proposed simpler rule requires only 2N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> +N pilots. For subset AS, we develop a manifold optimization based algorithm. To reduce its subset search complexity, which is exponential in the number of RF chains at the base station, we propose an alternating optimization based iterative algorithm. Our numerical results show that the proposed simpler AS rules are near optimal.

In this paper, we perform transmit antenna selection to improve the energy efficiency of large scale multiple antenna systems. We derive a good approximation of the distribution of the mutual information in this antenna selection system. It shows that channel hardening phenomenon is still retained as full complexity with antenna selection. Then, we use this closed-form expression to assess the energy efficiency performance. Specifically, we evaluate the performance of the energy efficiency in two different cases: 1) the circuit power consumption is comparable to or even dominates the transmit power, and 2) the circuit power can be ignored due to relatively much higher transmit power. The theoretical analysis indicates that there exists an optimal number of selected antennas to maximize the energy efficiency in the first case, whereas in the second case, the energy efficiency is maximized when all the available antennas are used. Based on these conclusions, two simple but efficient antenna selection algorithms are proposed to obtain the maximum energy efficiency. All the analytical results are verified through computer simulations.