Greedy Search-Based Optimal Transmit Antenna Selection for Massive MIMOME Channels

Antenna selection is often used for physical layer security to implement secure communications. However, due to the rapid changes of the main channel and the feedback delay of the channel state information (CSI), the transmitter obtains outdated CSI, and the outdated CSI leads to the outdated optimal transmit antenna. In order to improve the security of the system based on outdated CSI, in this paper, we propose a deep reinforcement learning framework of Deep Q Network (DQN) to predict the optimal transmit antenna in the multiple input multiple output (MIMO) wiretap channel. The legitimate receiver receives the pilot signals from each transmitting antenna, and the signal-to-noise ratio (SNR) of the pilot signals transmitted by each transmitting antenna can be obtained through maximal ratio combining. And then the legitimate receiver uses the DQN to predict the transmitting antenna at the next moment according to these SNRs. The simulation results show that DQN algorithm can effectively predict the optimal antenna at the next moment, and reduce the secrecy outage probability of MIMO wiretap system, compared with the traditional algorithm.

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.

The paper considers two different antenna selection schemes for space-time coded systems over flat fading channels. First we explore antenna selection at the transmitter side based on the received signal to noise ratios. We then study the joint selection of receive and transmit antennas. Both schemes assume a slowly fading channel (i.e., quasi-static fading) and require some limited feedback from the receiver to the transmitter. By computing upper bounds on the pairwise error probabilities and conducting extensive simulations, we show that the space-time coded systems achieve full diversity even with antenna selection provided that the code is full rank. These results are extensions of earlier work on antenna selection for MIMO systems (Bahceci et al., 2003) which only considers receive antenna selection.

Multiple Transmit Antenna Selection in MIMO-OSTBC System in Nakagami-m Fading Channel

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.

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.

An increase in the number of transmit antennas (M) poses an equivalent rise in the number of Radio Frequency (RF) chains associated with each antenna element, particularly in digital beamforming. The chain exhibits a substantial amount of power consumption accordingly. Hence, to alleviate such problems, one of the potential solutions is to reduce the number of RFs or to minimize their power consumption. In this paper, low-resolution Digital to Analogue Conversion (DAC) and transmit antenna selection at the downlink are evaluated to favour reducing the total power consumption and achieving energy efficiency in mMIMO with reasonable complexity. Antenna selection and low-resolution DAC techniques are proposed to leverage massive MIMO systems in free space and Close In (CI) path-loss models. The simulation results show that the power consumption decreases with antenna selection and low-resolution DAC. Then, the system achieves more energy efficiency than without low-resolution of DAC and full array utilization.

Euclidean distance optimized antenna selection (EDAS) has proven to be able to achieve high transmit diversity gains for spatial modulation (SM) multiple-input multiple-output (MIMO) systems, but the exhaustive search over all possible antenna subsets may result in an intractable search complexity, especially for large-scale MIMO (LS-MIMO) systems. In this paper, a novel EDAS-equivalent criterion, relaying on matrix dimension reduction, is proposed to reduce the search complexity. Based upon this criterion, for small-scale SM-MIMO systems, a pair of transmit antenna selection (TAS) schemes, namely, the tree search based antenna selection (TSAS), and the decremental antenna selection (D-AS) schemes, are developed. Specifically, the TSAS scheme works by iteratively splitting the original Euclidean distance (ED) matrix into submatrices with smaller dimensions. TSAS achieves the same bit error rate (BER) performance as that of the exhaustive search at the cost of exponential search complexity. To address this issue, D-AS is carried out by excluding one specific transmit antenna from all the possible candidates in each iteration based on the channel information. D-AS is able to offer a near-optimal BER performance with a further reduction in terms of the search complexity. Moreover, we extend the D-AS scheme to LS-SM-MIMO systems by sorting all the ED elements with the low-complexity heapsort algorithm. Our simulation results show that the proposed D-AS algorithm outperforms conventional TAS schemes conceived for LS-SM-MIMO systems.

In underlay spectrum sharing, transmit antenna selection (TAS) improves the performance of a secondary system and helps it control the interference it causes to a primary system. TAS does so with a hardware complexity and cost comparable to a single antenna system. We present a novel and optimal joint TAS and continuous power adaptation rule for a practically relevant, less explored model in which the secondary transmitter knows only the statistics of channel gains from itself to one or more primary receivers. The rule minimizes the average symbol error probability (SEP) of the secondary system for an entire class of stochastic interference constraints. This general class subsumes the average interference constraint and its novel generalization, and the interference-outage constraint. We derive closed-form expressions for the transmit power and selected antenna. We then develop a general analysis of the optimal average SEP that applies to several widely-used fading models. We also present computationally-efficient approaches to determine the parameters that specify the optimal rule. Our comprehensive numerical results characterize the very different impacts of the interference constraint on both secondary and primary systems. They show that the optimal rule reduces the average SEP by two orders of magnitude compared to conventional approaches.

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.

Exploiting multiple-input-multiple-output (MIMO) diversity, systems with error-prone and bandwidth-limited wireless channels can easily support reliable transmission. We develop optimal cross-layer scheduling that consists of QoS scheduling at the upper layer and optimal antenna selection at the physical layer. For QoS scheduling, we design a framework for optimal scheduling to meet users' QoS requirements. To solve this optimization problem, we consider a clique-searching algorithm for antenna selection that maximally utilizes the characteristics of MIMO systems by adopting the graph theoretical approach. As the clique searching problem becomes NP complete with the increase of transmit antennas in number, we propose a suboptimal antenna selection algorithm to deal with a large number of transmit antennas. We derive some theorems and properties for our approach and, through simulations, we demonstrate the performance of QoS scheduling which is effective to handle real-time traffic.

Iterative signal detection based on energy spreading transform (EST) in multiple input multiple output systems has been shown to perform close to the genie bound with the complexity of linear receivers [2]. In this paper, we consider a transmit antenna selection scheme for the EST based iterative receiver and propose some transmit selection criteria. Simulation results are provided to verify the usefulness of the proposed schemes.

The performance of an underlay cognitive radio (CR) system, which can transmit when the primary is on, is curtailed by tight constraints on the interference it can cause to the primary receiver. Transmit antenna selection (AS) improves the performance of underlay CR by exploiting spatial diversity but with less hardware. However, the selected antenna and its transmit power now both depend on the channel gains to the secondary and primary receivers. We develop a novel Chernoff-bound based optimal AS and power adaptation (CBBOASPA) policy that minimizes an upper bound on the symbol error probability (SEP) at the secondary receiver, subject to constraints on the average transmit power and the average interference to the primary. The optimal antenna and its power are presented in an insightful closed form in terms of the channel gains. We then analyze the SEP of CBBOASPA. Extensive benchmarking shows that the SEP of CBBOASPA for both MPSK and MQAM is one to two orders of magnitude lower than several ad hoc AS policies and even optimal AS with on-off power control.

In this paper, we study the problem of transmit antenna selection for massive multiple-input multiple-output systems by maximizing the determinant modulus of the selected channel matrix. Based on the maximum-volume submatrix finding method, we propose a real-time antenna-by-antenna iterative swapping enhancement (RAISE) transmit antenna selection algorithm with low memory cost and low computational complexity. The convergence of the proposed algorithm is proved and the performance of it is evaluated via numerical simulations. Our results show that, compared to the traditional antenna selection algorithms, RAISE can achieve near optimal capacity performance while the computational complexity and the memory cost are significantly reduced.

A new space-time block coded transmit antenna selection scheme over spatially correlated fading channels is presented. This scheme allows two or more transmit antennas to simultaneously use one radio frequency frontend. A system with four transmit antennas is considered in particular. The four antennas are selectively grouped into two subsets. Alamouti code is then applied on top of the subsets as if each was a single antenna. This scheme is shown to be more efficient than the conventional transmit antenna selection combined with Alamouti code in correlated channels. Moreover, it lowers the bitrate of the feedback channel.