Antenna Subset Selection Schemes Research Articles

Novel Receive Antenna Selection Scheme for Precoding-Aided Spatial Modulation with Lattice Reduction.

In this paper, a new receive antenna subset (RAS) selection scheme is proposed for precoding-aided spatial modulation (PSM). First, a lattice reduction (LR)-based precoder is employed instead of a conventional zero-forcing (ZF) precoder. It is analytically shown that a full diversity gain can be achieved by the LR-based ZF precoder without RAS selection. Then, an optimal LR-based RAS selection criterion is derived for the over-determined LR-based PSM systems, and a suboptimal selection algorithm is additionally presented. It is also shown that optimal and suboptimal RAS selection algorithms based on LR improve the BER performance of the LR-based PSM system. Further, the overall diversity order of the over-determined LR-based PSM systems with optimal LR-based RAS selection is analyzed. Finally, diversity analysis and simulation results show that the LR-ZF-based PSM system with optimal LR-based RAS selection outperforms the conventional ZF-based PSM system with conventional optimal RAS selection.

Efficient Transmit Antenna Selection for Receive Spatial Modulation-Based Massive MIMO

In this article, two efficient transmit antenna subset (TAS) selection schemes are proposed for receive spatial modulation (RSM)-based massive multiple-input multiple-output. First, an incremental TAS selection algorithm based on the maximization of the received signal-to-noise ratio is presented to select N S active transmit antennas effectively among the available Nτ transmit antennas. Then, to reduce complexity further, the modified TAS selection algorithm performs two consecutive selection stages. The pre-processing stage selects active transmit antennas whose number Np is less than the number of N S of the total transmit antennas to be selected and is equal to or greater than the number N R of the receive antennas. Then the post-processing stage chooses the remaining N S - Np active antennas. In the first stage, a simple norm-based algorithm is employed to reduce the complexity significantly. In the second stage, an incremental selection strategy is performed to find additional transmit antennas. It is demonstrated that the bit error rate and achievable rate of the proposed TAS selection algorithms are close to those of the decremental algorithm. Further, the simulation results show that the proposed TAS selection schemes offer significantly reduced complexity compared to the decremental TAS selection when the difference between the number of selected transmit antennas and the number of total available transmit antennas available is large. Furthermore, the impacts of the channel estimation error on the performance of TAS selection-based RSM systems are examined.

Antenna subset selection for massive MIMO systems: A trace-based sequential approach for sum rate maximization

Massive MIMO technology is a promising candidate for the next generation wireless communication systems. The basic idea is to implement a large number of antennas at the base station (BS) of a cell to serve a relatively small number of users. Such an approach brings many benefits in terms of the throughput and energy efficiency. Two major challenges associated with massive MIMO systems are high power consumption and high system complexity. These problems can effectively be dealt with by performing antenna-subset selection at the BS. In this paper, we propose low-complexity antenna-subset selection schemes for both single-cell and multiple-cell cooperative massive MIMO systems for equal-rate zero-forcing beamforming transmission at the BS. These schemes sequentially select an antenna that contributes the most to the overall system sum rate. Simulation results show that our proposed schemes outperform other schemes with similar computational complexities and even surpass some of the higher computational complexity schemes in a massive MIMO setting.

Adaptive Antenna Subset Selection and Constellation Breakdown for Spatial Modulation

The problem of antenna subset selection (ASS) and constellation breakdown (CBD) for spatial modulation (SM) is investigated. Assuming the availability of the channel state information, an ASS scheme is introduced such that the symbol error rate of SM is minimized. A graph-based heuristic search algorithm is then proposed to significantly reduce the search complexity of ASS compared to the exhaustive search, while maintaining the performance. As it is not always optimal to use all the transmit antennas, optimal CBD in conjunction with ASS is then considered to develop an adaptive low-complexity ASS-CBD scheme.

On capacity gain of antenna subset selection for multiple antenna multicasting

AbstractIn this letter, we consider the transmit antenna subset selection scheme in multicast channels where the antenna subset is selected to maximise the minimum signal‐to‐noise power ratio among the associated users. Using an asymptotic analysis, the average capacity is derived for the large number of users asymptote under the assumption that the antennas are divided into non‐overlapping subsets, and each of them is independent, identical, spatially uncorrelated, and Rayleigh distributed. The numerical results demonstrate that the asymptotic average capacity is close to the exact capacity when signal‐to‐noise power ratio is not large or the number of users is large. Using this result, we compare the asymptotic average capacity of the selection scheme with that of the open‐loop multiple antenna multicasting scheme, in which only one antenna subset is employed, and its number of antennas is the same as that of the antenna subset of the selection scheme. Specifically, it is shown that the asymptotic capacity gain over the open‐loop scheme is significant when the number of antennas in each antenna subset is small. Copyright © 2013 John Wiley & Sons, Ltd.

A Two-Stage Antenna Subset Selection Scheme for Amplify-and-Forward MIMO Relay Systems

A two-stage antenna subset selection scheme is proposed to maximize the channel capacity of amplify-and-forward multiple-input multiple-output relay systems. To reduce the computational complexity of the antenna selection process, the receive and transmit antenna selections are separated in a two-stage algorithm. In stage 1, a subset of receive antennas is selected at the relay node. A subset of transmit antennas is then selected at the relay node in stage 2. The simulation results demonstrate that the proposed scheme achieves nearly the same channel capacity with a reduced amount of computational complexity as compared to an exhaustive search.

Dual-mode SM/STBC system with antenna subset selection employing ML detection

Multiple input multiple output (MIMO) antenna system is a promising candidate to meet the demands of 4th Generation (4G) cellular communication systems by offering increased spectral efficiency through the spatial multiplexing (SM) gain, and improved link reliability through the space–time block coding (STBC) diversity gain. This paper presents a new scheme that combines the dual-mode SM/STBC and the antenna subset selection (AnSS) schemes. In the proposed scheme, the combination of the SM/STBC switching and the full antenna subset selection (AnSS) at both the transmitter (Tx) and the receiver (Rx) ends of the communication channel are adaptively selected through a simple algorithm based on the singular values of the channel matrix at the Rx side. Thus, the new scheme achieves the best BER performance over the previous works regardless of the data rate. The simulation results show that the proposed scheme with the full AnSS outperforms the previous works, by up to the 12.5 dB at the bit error rate (BER) of 10 ‐ 5 . Further, a partial AnSS is also proposed which dramatically reduces both the computational complexity (by 31%) and the hardware (by 50%), cost, without any appreciable loss in the BER performance, when compared with the full AnSS.

Outage performance of multi-antenna multicasting for wireless networks

Wireless cellular networks often need to convey the same data to multiple users simultaneously. This kind of transmission is known as physical layer multicasting. Unfortunately, when any user fails to receive the data correctly, the information must be retransmitted to all the users, resulting in a waste of radio resources. Thus, it is important to investigate the outage probability of multicast channels, which is defined as the probability that the smallest maximum achievable rate among all of the users is smaller than a specified transmission rate. In this paper, we consider the use of multiple antenna multicast channels where the transmitter is equipped with M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> antennas and independent data is transmitted on each antenna to K users. Using extreme value theory, we derive a closed form limiting distribution that the exact distribution of the multicasting channel converges to when the number of users K is taken to infinity. From this result, we find upper and lower-bounds on the outage probability. It is shown that for a given outage probability the upper-bound becomes sufficiently tight to approximate the exact outage probability with K such that it converges to the exact one with a speed faster than Theta (K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/M</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ). Using this upper-bound, we identify conditions on the transmission rate and the transmit power necessary to maintain constant outage performance as K increases. Specifically, the transmission rate should be decreased as Theta (K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/M</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ) or the transmit power should be increased as Theta (K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/M</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ). This means that the outage performance improves as the number of transmit antennas increases, because spatial diversity can be well exploited. However, this increase in the number of transmit antennas requires a significant cost. To improve the performance without requiring additional cost, we consider a multiple-slot multicasting that transmits the same data over multiple slots and an antenna subset selection scheme that transmits the data over some subset of the transmit antennas. It is shown that the transmission rate of multiple-slot scheme can be increased when the number of slots is carefully chosen and the diversity-multiplexing trade-off is the same as that of K = 1. Also, the gain in the transmission rate from selecting some subset of the transmit antennas is derived.

Capacity Limits of Multiple Antenna Multicasting Using Antenna Subset Selection

Both military and commercial wireless cellular networks often require the same data to be conveyed to multiple users simultaneously. This technique is usually referred to as physical layer multicasting. Unfortunately, the capacity of this multicasting setup is dictated by the smallest maximum achievable rate among all the users. In this paper, we consider the use of antenna subset selection in multicast channels where the transmitter is equipped with multiple antennas and sends a common message to multiple users. In this set-up, the antenna subset selection scheme selects some subset of the basestation transmit antennas to maximize the minimum signal-to-noise ratio (SNR) among the associated users. Using an asymptotic analysis, the average capacity of the antenna subset selection scheme is compared with that provided by max-min transmit beamforming, which requires channel state information at the transmitter to maximize the minimum user SNR, and an open-loop scheme where independent data is transmitted on each antenna. Then, we identify the minimum required antenna configuration condition for multicasting in which the antenna subset selection scheme universally performs better than the open-loop scheme and outperforms max-min transmit beamforming when the number of users is large. In addition, for a fixed number of radio frequency chains and users, it is shown that the antenna subset selection scheme can exploit the antenna selection diversity and its average capacity grows double logarithmically with the number of transmit antennas without bound.

Efficient Capacity-Based Joint Transmit and Receive Antenna Selection Schemes in MIMO Systems

In this letter, we propose two different joint transmit and receive antenna subset selection schemes for multiple-input multiple-output (MIMO) systems on the basis of capacity maximization criterion. We assume that perfect channel state information (CSI) is known at the receiver but unknown to the transmitter. As the selection signaling is perfectly fed back to the transmitter, we propose a flexible two-step selection algorithm (TSSA) in practical MIMO channel scenarios. Computer simulations show that TSSA can maximize the capacity at low computation cost in most scenarios. It performs well in terms of capacity, computational complexity and flexibility. Furthermore, we propose a simplified algorithm based on the correlation matrix when the channel correlation information (CCI) is known to the transmitter. Simulation results show that the proposed correlation matrix based selection algorithm is only slightly inferior to an optimal selection algorithm.