Adaptive MIMO antenna selection via discrete stochastic optimization

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

Knowledge of the propagation channel makes it possible for multiple-input multiple-output (MIMO) links to achieve higher system capacities by means of beamforming on the stronger channel eigenvectors. Assigning dedicated low bit rate feedback links is an efficient and practical method of providing transmitters with the required channel state information. One of the key problems in limited feedback precoding is the design of finite indexed codebooks known a priori to both the transmitter and receiver.Previous design methods include signal-to-noise ratio maximization through Grassmannian line packing, distortion minimization via vector quantization and antenna selection algorithms. In this paper we propose a new method that utilizes the dominant right singular vector of the MIMO channel to construct quantized unitary precoders. We show that although sub-optimum, this method can produce full diversity order codebooks. Using Monte-Carlo simulations, we compare the performance of the this method with optimum Grassmannian precoders and also antenna selection and subset selection algorithms.

In this work, a new antenna selection algorithm is proposed for multiple-input multiple-output (MIMO) wireless communication systems. It is assumed that the channel state information (CSI) is exactly known at the receiver, and hence the antenna selection is considered only at the receiver. The goal of antenna selection is to find a tradeoff between the maximization of the channel capacity and the minimization of the pair-wise error probability (PEP). It turns out that the scheme of antenna selection plays an important part when dealing with high antenna correlation. It is shown that our proposed scheme for a system with two-transmit antennas and five-receive antennas offers better PEP performance than the algorithm based on the maximization capacity, but has little capacity loss. The simulation results indicate that our proposed scheme can jointly meet the capacity maximization and the PEP minimization.

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> RF chains and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M &lt; N$ </tex-math></inline-formula> . Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

The IEEE 802.16/WiMAX standard has fully embraced multi‐antenna technology and can, thus, deliver robust performance and high transmission rates. Nevertheless, due to its inherent cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. This is because the former is often substantially more expensive than the latter. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited number of RF chains for transceiving, is a highly appealing performance enhancement technique for multi‐antenna WiMAX terminals. In this paper, a novel protocol for antenna selection in space division multiple access (SDMA) transmission is developed for the next‐generation IEEE 802.16 mobile stations. Both locally and globally optimal selection rules are considered together with various linear precoding designs at the base station (BS). The proposed protocol can readily accommodate various channel situations (e.g., reciprocal and non‐reciprocal channels). As demonstrated by analysis and simulation, the proposed protocol delivers considerable performance improvement over conventional IEEE 802.16 terminals that lack antenna selection capability. Moreover, the proposed protocol leverages the existing signaling method defined in IEEE 802.16, thereby incurring a negligible signaling overhead and requiring only minimal modifications of the standard. To the best of our knowledge, this paper represents the first effort to support antenna selection capability for SDMA transmissions in IEEE 802.16 mobile stations. Copyright © 2009 John Wiley &amp; Sons, Ltd.

An antenna selection scheme for multi-input multi-output (MIMO) ultra-wide band (UWB) communication system with time reversal (TR) is investigated in this paper. The system exploits spatial multiplexing (SM) scheme to achieve high data rate. In order to cope with long delay spread of the UWB channel, time reversal technique is adopted. TR can mitigate not only the interference intersymbol (ISI) but also multi stream interference (MSI) caused by transmitting several data streams simultaneously. Antenna selection algorithm allows to reduce the number of transmit antenna by using the channel state information (CSI), which is already available for time reversal. Simulation results show that the algorithm can considerably improve the BER performance of the system when the number of diversity branch is not so large.

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.

The antenna selection technology has become essential for massive multiple-input multiple-output (MIMO) systems since it allows us to benefit from the massive increase in the number of antennas without a necessary use of additional RF components. However, reducing the number of active antennas may exhibit a performance loss regardless of the used antenna selection technique. In this paper, we propose a low-complexity and near maximum-likelihood (ML) performance achieving receiver for large MIMO systems with antenna selection. The proposed approach is based on the likelihood ascent search (LAS) algorithm and allows to increase the system performance in terms of bit error rate (BER). The performance of the proposed detector is analyzed by Monte-Carlo simulations. Numerical results show that our technique provides a gain of about 1 dB.

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.

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.

Channel hardening in massive Multiple-Input Multiple-Output (MIMO) systems is a hot research field that needs investigation. As the number of antennas increases, channel starts to behave deterministically and small-scale fading decreases. This is called hardening phenomenon. Since there is a lack of real world study of the hardening effect in massive MIMO channels, this paper aims at investigating channel hardening in an outdoor real-world scenario by using a linear double polarized transmitting antenna array and a cylindrical dual polarized receiving array. Measurements are performed in two different routes and hardening is analyzed in terms of different polarization combinations and different antenna subsets by fixing the transmitter or receiver and using coefficient of variation (CV²) as a metric. The results show that in terms of getting more pronounced hardening, it is more beneficial to fix the position of the transmitting antenna along the array. Selecting a certain subset of antennas, it can be observed that channel does not soften. This gives rise to the possibility of using antenna selection algorithms. For achieving the same hardening level, more antennas are needed in the Line of Sight (LoS) scenario since the channel is shown to experience more hardening in Non-Line of Sight (NLoS) scenario. Furthermore, it is shown that polarization has little impact in the channel hardening of a NLoS scenario. These results are very important from the point of view of network operators.

Existing fast antenna selection algorithms have either low performance gains or they are efficient only for the selection of particular size sub-matrices. Our motivation to the study of NP-Hard antenna selection problem is two-fold. First, we intend to propose an optimal algorithm that is equally efficient for each possible subset cardinality of the transmit and/or receive antennas. Second, different from any of the antenna selection algorithms proposed so far, our algorithm deterministically increases the strength of the singular values during the selection of each column of the selected matrix. This increase in the singular values is defined by a very tight bound. This bound has previously been used to analyze the efficiency of low rank approximation algorithms. Hence, our antenna selection procedure is as efficient as that of a best algorithm for matrix low rank approximations.

Full-duplex (FD) distributed antenna system (DAS) can take advantage of both FD and DAS to dramatically improve system capacity. The challenges of designing such a system are self-interference at the base station (BS) and multiuser interference, as well as hardware cost, computational complexity and signaling overhead, especially for dense antenna deployments. To address these problems mentioned above, in this paper, we investigate an antenna selection strategy at the BS for FD DAS including FD-capable BS antennas and half-duplex (HD) users. In particular, we separately optimize the receive and transmit antenna selection problems to maximize the achievable sum rate. To reduce the computational complexity and signaling overhead, each user is restricted to selecting only the BS antennas in its virtual cell. The optimization problem is a nonconvex integer programming problem, for which it is difficult to find a globally optimal solution. Using variable relaxation and successive convex approximation, we present an iterative antenna selection algorithm based on difference of convex functions (D.C.) programming to obtain a suboptimal solution. The simulation results demonstrate that the proposed antenna selection algorithm can provide significant performance gain over various reference algorithms.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

In this paper, we investigate a communication strategy combining of transmit antenna selection with adaptive modulation and coding for space-time block coded system. The antenna selection is based on the criterion of maximizing the average spectral efficiency (ASE) of the whole system for a given bit error rate (BER) constraint. In order to enhance system performance, low density parity check codes (LDPC) is employed as the error correcting codes. We assume that the channel state information (CSI) is known at the receiver and the adaptive control signaling can be perfectly fed back to the transmitter. Simulation results show that our proposed scheme provides a well behaved trade-off between the ASE and BER under MIMO environment