Receive Antenna Selection Research Articles

QRD-Based Antenna Selection for ML Detection of Spatial Multiplexing MIMO Systems: Algorithms and Applications

Antenna selection is a simple but effective technique to enhance the performance of a spatial multiplexing multiple-input-multiple-output (MIMO) system. The selection criterion depends on the detector used at the receiver. For the maximum-likelihood detector, the criterion is to maximize the free distance. However, an exhaustive search is required to derive the distance, and the computational complexity can be prohibitively high. To avoid the exhaustive search, a lower bound of the free distance derived with the singular value decomposition (SVD) was then developed. This bound only involves the smallest singular value of the channel matrix, and its maximization can easily be conducted. An alternative lower bound of the free distance with the QR decomposition (QRD) was also derived in the literature. In this paper, we first propose a QRD-based selection method maximizing the lower bound. With some matrix properties, we theoretically prove that the lower bound yielded by the QRD is tighter than that by the SVD. We then propose a basis transformation method so that the lower bound yielded by the QRD can further be tightened. As a result, the QRD-based selection method can achieve near optimum performance. Finally, we extend the use of the proposed methods to other applications, such as receive antenna selection, joint transmit/receive antenna selection, and antenna selection in MIMO relay systems. Simulations show that the proposed selection methods can significantly outperform existing methods.

Effects of Channel Prediction for Transmit Antenna Selection With Maximal-Ratio Combining in Rayleigh Fading

Antenna selection is a low-complexity method for pragmatically exploiting spatial diversity in wireless systems. It has potentially reduced hardware cost compared with space-time or multiple-input-multiple-output (MIMO) coding due to the reduction in the amount of radio-frequency hardware required. Although receive antenna selection is, perhaps, more common, transmit antenna selection (TAS) offers several advantages, particularly for hardware-costly transmit schemes, such as, methods that require linearization. In use, TAS requires at least partial channel knowledge at the transmitter to perform selection. This knowledge usually comes in the form of an index to the best set of antenna/antennas that are fed back from the receiver, which implies a delay between the channel that is sampled (at the receiver) and this knowledge being acted upon (at the transmitter). In this paper, performance degradation due to outdated channel knowledge is analytically determined and related to channel characteristics. A predictive scheme is then developed to mitigate delay-induced degradation. Several factors that are related to TAS system performance under different channel scenarios, both with and without mitigation, are explored. Closed-form expressions for performance metrics such as bit error rate, outage probability, average signal-to-noise ratio (SNR) gain, and higher order moments of the output SNR, are derived and verified by simulations. The impact of prediction is analyzed for different TAS setups and channel prediction scenarios, as well as various system design parameters.

Training and Voids in Receive Antenna Subset Selection in Time-Varying Channels

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of training voids, in which the last pilot transmission is not fully exploited by the receiver. We then present new filling methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed form equations for the symbol error probability with and without void-filling are also developed.

On the Throughput of MIMO Relay Wireless Network with Receive Antenna Selection

In this letter, we consider a single-relay and K users multiple-input multiple-output (MIMO) system with the channel state information (CSI)-assisted amplify-and-forward (CAF) protocol, where each node is equipped with N antennas. We propose one antenna selection scheme for the multiuser wireless relay network. According to the order statistics, we derive the exact expressions for the probability density functions (PDF) and use them to evaluate the average throughput. The numerical results show that for a N = 2 and K = 3 relay channel the antenna selection scheme results about 2dB SNR loss compared to the full antenna case.

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.

PCA-Combined Receive Antenna Selection for MMSE V-BLAST in Correlated Channels

A computationally efficient receive antenna subset selection in conjunction with the principal component analysis (PCA) is proposed for the minimum mean square error (MMSE) V-BLAST systems in correlated channels. This proposed PCA-combined antenna subset selection is capable of sustaining system performance and reducing complexity burden in signal processing and hardware cost.

Performance Analysis of Joint Single Transmit and Receive Antenna Selection in Nakagami-m Fading Channels

In this paper, multi-antenna systems employing both single transmit and single receive antenna selection (single TAS/single RAS) are examined for independent and identically distributed (i.i.d.) flat Nakagami-m fading channels. In this joint selection scheme, signal transmission and reception is performed over a single communication link with the highest signal-to-noise ratio (SNR). Exact bit/symbol error rate expressions for binary modulation, M-ary phase shift keying and square M-ary quadrature amplitude modulation signals are derived by using the moment generating function based analysis method. Also, upper bounds are obtained in order to show that the investigated systems achieve full diversity orders at high SNRs. Besides, outage probability analysis is carried out for examining system capacity. Analytical results are validated by simulations.

A Novel Convex Optimization for Receive Antenna Selection

This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. The algorithm is based on approximated relaxed original optimization problem. The main effort in the approximated relaxed method is computing the Newton step for the centering problem, which consists of solving sets of linear equations constraints. The method produces not only a suboptimal choice of receive antennas, but also, a bound on how well the globally optimal choice does. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.

Design and preliminary evaluation of a compact four-element terminal multiple-input multiple-output antenna for receiving antenna selection

A compact four-element multiple-input multiple-output (MIMO) antenna for receiving antenna selection at 2.3 GHz is designed and evaluated. Comparisons of MIMO systems employing antennas with different antenna selection schemes are presented. The system performances are evaluated and discussed in terms of channel capacity, average received signal-to-noise ratio and correlation coefficient between elements at the receiver. The impact of antenna orientations is also investigated. Based on the measured results, several subset antenna selection schemes are recommended in indoor application scenarios, which will contribute to compact terminal MIMO antenna design and optimisation complexity reduction.

Global and Fast Receiver Antenna Selection for MIMO Systems

For a multiple-input multiple-output (MIMO) system with more antennas at the receiver than the transmitter, selecting the same number of receiver antennas as the number of transmit antennas captures most of the advantages of MIMO capacity performance and reduces the system hardware and computational cost at the same time. In this paper, a fast and global-search receive antenna selection algorithm is proposed for this MIMO array configuration. Different from many existing fast but `local' antenna selection algorithms which obtain the sub-optimal channel submatrix by adding or removing one row per step, our algorithm acquires the near-optimal channel matrix by {directly} and {rapidly} the maximum-volume submatrix of the original channel matrix. Due to its globally searching property, our antenna selection algorithm leads to a substantial improvement in the capacity optimality for moderate to high SNRs, and obtains almost the same capacity performance as that of the exhaustive-search-based optimal antenna selection algorithm. Furthermore, the computational load and memory requirement for our antenna selection method are still comparable to those of the existing sub-optimal antenna selection methods. Numerical results are provided to verify the validity of the proposed methods.

A Novel, Balanced, and Energy-Efficient Training Method for Receive Antenna Selection

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

Global and Fast Receiver Antenna Selection for MIMO Systems

For a multiple-input multiple-output (MIMO) system with more antennas at the receiver than the transmitter, selecting the same number of receiver antennas as the number of transmit antennas captures most of the advantages of MIMO capacity performance and reduces the system hardware and computational cost at the same time. In this paper, a fast and global-search receive antenna selection algorithm is proposed for this MIMO array configuration. Different from many existing fast but `local' antenna selection algorithms which obtain the sub-optimal channel submatrix by adding or removing one row per step, our algorithm acquires the near-optimal channel matrix by {directly} and {rapidly} searching the maximum-volume submatrix of the original channel matrix. Due to its "globally searching" property, our antenna selection algorithm leads to a substantial improvement in the capacity optimality for moderate to high SNRs, and obtains almost the same capacity performance as that of the exhaustive-search-based optimal antenna selection algorithm. Furthermore, the computational load and memory requirement for our antenna selection method are still comparable to those of the existing sub-optimal antenna selection methods. Numerical results are provided to verify the validity of the proposed methods.

Optimal Receive Antenna Selection in Time-Varying Fading Channels with Practical Training Constraints

Hardware constraints, which motivate receive antenna selection, also require that various antenna elements at the receiver be sounded sequentially to obtain estimates required for selecting the 'best' antenna and for coherently demodulating data thereafter. Consequently, the channel state information at different antennas is outdated by different amounts and corrupted by noise. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, a preferable strategy is to linearly weight the channel estimates of different antennas differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then characterize explicitly the optimal selection weights that minimize the SEP. We also consider packet reception, in which multiple symbols of a packet are received by the same antenna. New suboptimal, but computationally efficient weighted selection schemes are proposed for reducing the packet error rate. The benefits of weighted selection are also demonstrated using a practical channel code used in third generation cellular systems. Our results show that optimal weighted selection yields a significant performance gain over conventional unweighted selection.

A computationally efficient near-optimal algorithm for capacity-maximization based joint transmit and receive antenna selection

This letter presents a computationally efficient joint transmit and receive antenna selection (EJTRAS) algorithm based on a modification of the selection criterion in [1]. We show that the modification allows us to reduce the algorithm's computational complexity by a factor of L, where L is the number of selected antennas without sacrificing the performance. Nearoptimal outage and ergodic capacity can therefore be attained with significantly lower complexity as verified by the extensive computer simulations.

Robustness of Weighting Receive Antenna Selection Algorithm

The conventional antenna selection schemes suffer from severe performance degradations in most fading channels. This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. With this method, each Radio Frequency chain is not allocated to a single antenna element, but instead to the complex-weighted and combined response of a group array of elements. In this paper, we firstly get optimal solution under no constraints. Then, suboptimal algorithms are introduced based on minimised the squared error and convex optimization technique. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.

Receive antenna selection for closely-spaced antennas with mutual coupling

We investigate the achievable rate of receive antenna selection MIMO systems in the presence of mutual coupling and spatial correlation. For that, we assume the antenna array to consist of dipole antennas placed side-by-side in a linear pattern and in a very limited physical space. In a first step, we will assume perfect channel state information at the receiver side only and a negligible training overhead compared with the payload. We will demonstrate that in contrast to what might be expected based on results for cases without mutual coupling, MIMO receive antenna selection can achieve higher data rates than the system using all antennas provided that the total number of receive antennas is larger than a critical value that we will further discuss. We then propose an optimal antenna selection processing that ensures rate maximization regardless of the number of antennas used. In a later step, we will address the impact of training overhead on the system achievable rate when the training overhead is considerable. We will show that such a rate is reduced dramatically due to the large amount of training overhead arising from the presence of mutual coupling. To overcome this problem, we will thus propose a novel channel estimation method, which reduces the training overhead greatly and improves the system achievable rate performance.

Receive antenna selection for unitary space-time modulation over semi-correlated Ricean channels

Receive antenna selection for unitary space-time modulation (USTM) over semi-correlated Ricean fading channels is analyzed (this work generalizes that of Ma and Tepedelenlio-glu for the independent and identically distributed (i.i.d.) Rayleigh fading case). The antenna selection rule is that the receive antennas with the largest signal powers are chosen. For single antenna selection, we derive the maximum likelihood decoding for the correlated Ricean case. We also derive the Chernoff bound on the pairwise error probability for the high signal to- noise ratio (SNR) region and obtain the coding gain and diversity order. Our results show that even when there are transmitter side correlations and a line of sight component, receive antenna selection with USTM preserves the full diversity order if the USTM constellation is of full rank. We also give an approximation to the distribution function of a quadratic form of non-zero mean complex Gaussian variates (from Nabar et al.) at the high SNR region. Based on this approximation, a closed-form expression for the coding gain is also obtained and compared with that of the i.i.d. Rayleigh case. We also analyze the case of multiple receive antenna selection and derive the coding gain and diversity order. We show that USTM constellations, which have been proposed for the i.i.d. Rayleigh channel, can be used with the correlated Ricean channel as well.

Performance Analysis of Joint Transmit and Receive Antenna Selection With Orthogonal Space-Time Coding

This paper analyzes the performance of multiple-input-multiple-output (MIMO) systems with transmit and receive antenna selection (T-RAS). The average bit error rate (BER) and the average symbol error rate (SER) are derived by utilizing the characteristic function (CF) of the joint output signal-to-noise ratios (SNRs). Our approach can be used over not only independent but also arbitrary correlated channels. For the sake of brevity, this paper focuses on Nakagami-m fading channels. The simulation results are provided to validate the numerical calculations.

Sum-of-squares and sum-of-amplitudes antenna selection for correlated alamouti MIMO

The performances of two recently proposed receiver antenna selection schemes based on maximum sum-of-squares and maximum sum-of-amplitudes are investigated for the Alamouti space-time block coded multiple-input multiple-output system with correlated receiver antennas and M-ary signals. Their performances are compared with the conventional antenna selection scheme based on maximum signal-to-noise ratio. Unlike the independent antenna case, where the performances of the new selection schemes are very close to that of the conventional scheme, the performance differences between the new schemes and the conventional scheme are significant when the receiver antennas are correlated. Depending on the system operating parameters, the new receiver selection schemes can perform better or worse than the coventional diversity selection scheme.

Receive antenna selection for MIMO systems over correlated fading channels

In this letter, we propose a novel receive antenna selection algorithm based on cross entropy optimization to maximize the capacity over spatially correlated channels in multiple-input multiple-output (MIMO) wireless systems. The performance of the proposed algorithm is investigated and compared with the existing schemes. Simulation results show that our low complexity algorithm can achieve near-optimal results that converge to within 99% of the optimal results obtained by exhaustive search. In addition, the proposed algorithm achieves near-optimal results irrespective of the mutual relationship between the number of transmit and receive antennas, the statistical properties of the channel and the operating signal-to-noise ratio.