Joint Antenna Selection Algorithm Research Articles

Antenna Selection in MIMO Cognitive Radio-Inspired NOMA Systems

This letter investigates a joint antenna selection (AS) problem for a MIMO cognitive radio-inspired non-orthogonal multiple access network. In particular, a new computationally efficient joint AS algorithm, namely subset-based joint AS (SJ-AS), is proposed to maximize the signal-to-noise ratio of the secondary user under the condition that the quality of service of the primary user is satisfied. The asymptotic closed-form expression of the outage performance for SJ-AS is derived, and the minimal outage probability achieved by SJ-AS among all possible joint AS schemes is proved. The provided numerical results demonstrate the superior performance of the proposed scheme.

A concise joint transmit/receive antenna selection algorithm

For reducing the computational complexity of the problem of joint transmit and receive antenna selection in Multiple-Input-Multiple-Output (MIMO) systems, we present a concise joint transmit/receive antenna selection algorithm. Using a novel partition of the channel matrix, we drive a concise formula. This formula enables us to augment the channel matrix in such a way that the computational complexity of the greedy Joint Transmit/Receive Antenna Selection (JTRAS) algorithm is reduced by a factor of 4nL, where nL is the number of selected antennas. A decoupled version of the proposed algorithm is also proposed to further improve the efficiency of the JTRAS algorithm, with some capacity degradation as a tradeoff. The computational complexity and the performance of the proposed approaches are evaluated mathematically and verified by computer simulations. The results have shown that the proposed joint antenna selection algorithm maintains the capacity performance of the JTRAS algorithm while its computational complexity is only 1/4nL of that of the JTRAS algorithm. The decoupled version of the proposed algorithm further reduces the computational complexity of the joint antenna selection and has better performance than other decoupling-based algorithms when the selected antenna subset is small as compared to the total number of antennas.

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.

Near-Optimal Joint Antenna Selection for Amplify-and-Forward Relay Networks

This paper considers a joint antenna selection method in amplify-and-forward (AF) relay networks where the source, relay and destination terminals are all equipped with multiple antennas. The fact that the system's full diversity can be maintained by antenna selection at each terminal makes it a promising solution to reduce the hardware complexity of multiple-input multiple-output (MIMO) terminals while realizing the diversity benefits of MIMO in relay networks. Since the exhaustive search for antenna subset selection is computationally prohibitive, we devise a low-complexity near-optimal joint antenna selection algorithm based on a constrained cross entropy optimization (CCEO) method to maximize the achievable rate and the convergence is guaranteed. Simulation results reveal both the effectiveness and the efficiency of the proposed algorithm and the significant performance improvement over other benchmark selection techniques. Finally, it is illustrated that the proposed CCEO algorithm can always achieve near-optimal results regardless of the number of selected antennas, outage probabilities and the signal-to-noise ratios (SNRs) at the terminals.