Antenna Selection In MIMO Systems Research Articles

Joint Transmit and Receive Antenna Selection in Mimo Systems Based on Swarm Intelligence Algorithm

Multiple-input-multiple-output (MIMO) can provide superior performances such as system capacity, linkage, etc. But also it will bring high RF costs and system complexity, especially in large scale MIMO systems. Antenna selection (AS) is proved to be a trade-off between good performances and complexity. Specifically, from the perspective of both transmit and receive antennas, the joint transmit and receive antenna selection (JTRAS) is employed in MIMO systems. Up to now, some algorithms of JTRAS have been studied in MIMO systems. However, most of them are mainly focused on just one aspect about accuracy or complexity. Especially, compared to numerical analysis, the implementation of swarm intelligence algorithm in JTRAS needs to be studied extensively. In the paper, three intelligent algorithms, i.e. genetic algorithm, cat swarm algorithm and particle swarm algorithm are studied and compared in terms of accuracy, cost, and complexity. In addition, fractional coding is proposed in the algorithms instead of binary integer coding. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. PSO has the best accuracy and stability, but the complexity of PSO is also highest. If we take overall performances in consideration, CSO is the best choice especially in practical implementation. Moreover, fractional coding will provide better performance than binary integer coding.

Antenna Selection and Detection Performance on Correlation Based Detection Systems

The increase data size transmitted in digital communication systems necessitates the use of high-capacity communication channels for the transmission of these data. To increase the channel capacity, MIMO systems are widely used. Antenna selection in MIMO systems is very important for efficient use of communication sources. Cognitive Radio systems generally aim to increase spectrum efficiency by using MIMO systems. Therefore, Cognitive Radio must find empty spectrum regions with spectrum detection methods. Spectrum detection performance is related to the antenna numbers in MIMO systems. Therefore, this study examines the effect of antenna numbers on detection performance in correlation-based detection method. Thanks to this study, the relationship between channel capacity, detection performance and antenna number for MIMO systems was investigated.

Joint Transmit Beamforming and Antenna Selection in MIMO Systems

This letter investigates joint beamforming and antenna selection (JBAS) for a spatial diversity multiple-input multiple-output system, where the transmitter equipped with multiple antennas performs both antenna selection and beamforming optimization to maximize the received signal-to-noise ratio (SNR). The JBAS problem is NP-hard to solve due to a sparsity constraint. In this letter, we propose smooth approximation-based iterative algorithms to address the JBAS problem. The simulation results validate the effectiveness of the proposed algorithms in terms of the achieved SNR and the computational complexity.

Invited Paper: Antenna selection in energy efficient MIMO systems: A survey

With the explosive growth and need for high-speed wireless communications, more and more energy is consumed to support the required quality of service. Therefore, energy efficient or green communication has become a very hot topic under the ground of limited energy resource and environmentally friendly transmission schemes. MIMO technique is capable of reducing the transmission power thanks to its diversity and multiplexing gain. Moreover, antenna selection (AS) is an alternative to extract many of the benefits in MIMO systems with a reduced cost of complexity and power. Although many works including several survey papers have investigated AS in MIMO systems, the goal of these works is only the capacity maximization or error rate minimization, which fails to guarantee the optimality of the energy efficiency in MIMO systems. In this paper, we overview the state of the art in the AS schemes in energy efficient MIMO systems, the goal of which is to optimize the energy efficiency of the whole system. Specifically, we introduce energy efficient AS in point-to-point MIMO, cooperative MIMO, multiuser MIMO and largescale MIMO systems, respectively. Several challenging and practical issues in this area are also addressed.

Retraction: Iterative Transmit/Receive Antenna Selection in MIMO Systems Based on Channel Capacity Analysis

Retraction: Iterative Transmit/Receive Antenna Selection in MIMO Systems Based on Channel Capacity Analysis

Sub-Modularity and Antenna Selection in MIMO Systems

In this paper, we show that the optimal receive antenna subset selection problem for maximizing the mutual information in a point-to-point MIMO system is sub-modular. Consequently, a greedy step-wise optimization approach, where at each step, an antenna that maximizes the incremental gain is added to the existing antenna subset, is guaranteed to be within a (1-1/e)-fraction of the global optimal value independent of all parameters. For a single-antenna-equipped source and destination with multiple relays, we show that the relay antenna selection problem to maximize the mutual information is modular and a greedy step-wise optimization approach leads to an optimal solution.

Particle Swarm Optimization for Antenna Selection in MIMO System

This paper investigates the receive antenna selection problem to maximize capacity in wireless MIMO communication system, which can be formulated as an integer programming optimization problem and ...

A New Joint Antenna Selection Algorithm Based on Capacity

Antenna Selection in MIMO systems can increase the system capacity, reduce the MIMO system complexity and cost of radio links effectively. In this paper, a new joint antenna selection algorithm was presented which can adaptively change the number of the selected transmitting and receiving antenna number according to the channel station. It can obtain the similar system capacity with the optimal joint antenna selection algorithm in any correlation coefficient, but the calculated amount is far less than the optimal joint algorithm.

Low-complexity joint transmit and receive antenna selection for MIMO systems

In this paper, we present a low-complexity algorithm for real-time joint transmit and receive antenna selection in MIMO systems. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. We apply Binary Particle Swarm Optimization (BPSO) to the joint transmit and receive antenna selection problem. In addition, applying the general BPSO to joint antenna selection, we also present a specific improvement to this population-based heuristic algorithm, namely, we feed cyclically shifted initial population so that the average convergence time (the number of iterations until reaching an acceptable solution) is reduced. The proposed BPSO for joint antenna selection problem has a low computational complexity, and its effectiveness is verified through simulation results.

Performance Analysis of Joint User Scheduling and Antenna Selection Over MIMO Fading Channels

In this letter, a performance analysis is presented for a joint multiuser scheduling and antenna selection in MIMO systems employing orthogonal space-time block coding (OSTBC) over Rayleigh fading channels under a general case of heterogeneous scenario. Using numerical evaluations the performance of different cases of the considered scheme are compared for heterogeneous and homogeneous scenarios.

Retraction: Iterative Transmit/Receive Antenna Selection in MIMO Systems Based on Channel Capacity Analysis

Notification from Editors “Iterative Transmit/Receive Antenna Selection in MIMO Systems Based on Channel Capacity Analysis” by Peng LAN, Ju LIU, Fenggang SUN and Peng XUE, IEICE Transactions on Communications, vol.E94-B, no.3, pp.844-847, March 2011. This manuscript was canceled on August 6, 2013 because it was found to be a duplicate submission.

Performance of Antenna Selection in MIMO System Using Channel Reciprocity with Measured Data

The channel capacity of MIMO system increases as a function of antenna pairs between transmitter and receiver but it suffers from multiple expensive RF chains. To reduce cost of RF chains, antenna selection (AS) method can offer a good tradeoff between expense and performance. For a transmitting AS system, channel state information (CSI) feedback is required to choose the best subset of available antennas. However, the delay and error in feedback channel are the most dominant factors to degrade performances. In this paper, the concept of AS method using reciprocal CSI instead of feedback channel is proposed. The capacity performance of proposed system is investigated by own developing Testbed. The obtained results indicate that the reciprocity technique offers a capacity close to a system with perfect CSI and gains a higher capacity than a system without AS method. This benefit is from 0.9 to 2.2 bps/Hz at SNR 10 dB.

Joint Transmit/Receive Antenna Selection for MIMO Systems: A Real-Valued Genetic Approach

This study proposes a new antenna selection (AS) method using the real-valued genetic algorithm (RVGA) to improve the channel capacity of multiple-input/multiple-output (MIMO) systems. By defining a score function based on channel capacity, AS can be formulated as an optimization problem over a multidimensional real space, and solved by implementing the RVGA method. The simulation results show that the performance of the proposed RVGA AS provides approximately the same achievable rates as the optimal exhaustive search (ES) AS, while maintaining a moderate computational load.

Using Delayed Feedback for Antenna Selection in MIMO Systems

Antenna selection in multiple-input-multipleoutput (MIMO) systems preserves diversity gain while significantly reducing hardware complexity. However, imperfect channel state information (CSI) affects performance. In this paper, we first analyze the performance of a MIMO system employing antenna selection at the transmitter and maximal ratio combining (MRC) at the receiver in the presence of feedback delay and channel estimation errors. Then, we determine whether channel prediction can compensate for the effect of feedback delay. Outage probability is analyzed as a function of ?, the correlation coefficient between the CSI used at the receiver for decoding (CSIR) and the CSI used at the transmitter for selection (CSIT). Analytical results show that the effect of feedback delay is more significant than the effect of estimation error. In order to overcome the effect of delay, ? should increase with SNR. For a given SNR, the length of the linear minimum mean square error (LMMSE) prediction filter required is calculated and shown to increase with SNR. Finally, we determine the asymptotic diversity order as a function of the feedback quality. Results show that if 1 - ? ? SNR-1, the diversity order with imperfect CSI is same as that with perfect CSI.

Performance Analysis of Transmit and Receive Antenna Selection over Flat Fading Channels

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.

Joint Transmit/Receive Antenna Selection in MIMO Systems Based on the Priority-Based Genetic Algorithm

In this letter, a novel genetic algorithm (GA) for joint transmit and receive antenna selection in multiple-input-multiple-output (MIMO) systems is presented. The new GA employs a priority mechanism in the crossover and mutation operations in the evolution process. Computer simulations show that the proposed GA can achieve close performance to the optimum algorithm but with much lower computational complexity.

Antenna selection in MIMO systems

Multiple-antenna systems, also known as multiple-input multiple-output radio, can improve the capacity and reliability of radio communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power, and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. This article reviews classic results on selection diversity, followed by a discussion of antenna selection algorithms at the transmit and receive sides. Extensions of classical results to antenna subset selection are presented. Finally, several open problems in this area are pointed out.