A fast and optimal deterministic algorithm for NP-Hard antenna selection problem

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.

An algorithm for low-rank approximation of bivariate functions using splines

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

Recently it has been shown that it is possible to improve the performance of multiple-input multiple-output (MIMO) systems by employing a larger number of antennas than actually used and selecting the optimal subset based on the channel state information. Existing antenna selection algorithms assume perfect channel knowledge and optimize criteria such as Shannon capacity or various bounds on error rate. This paper examines MIMO antenna selection algorithms where the set of possible solutions is large and only a noisy estimate of the channel is available. In the same spirit as traditional adaptive filtering algorithms, we propose simulation based discrete stochastic optimization algorithms to adaptively select a better antenna subset using criteria such as maximum mutual information, bounds on error rate, etc. These discrete stochastic approximation algorithms are ideally suited to minimize the error rate since computing a closed form expression for the error rate is intractable. We also consider scenarios of time-varying channels for which the antenna selection algorithms can track the time-varying optimal antenna configuration. We present several numerical examples to show the fast convergence of these algorithms under various performance criteria, and also demonstrate their tracking capabilities.

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.

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.

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 study the problem of transmit antenna selection for massive multiple-input multiple-output systems by maximizing the determinant modulus of the selected channel matrix. Based on the maximum-volume submatrix finding method, we propose a real-time antenna-by-antenna iterative swapping enhancement (RAISE) transmit antenna selection algorithm with low memory cost and low computational complexity. The convergence of the proposed algorithm is proved and the performance of it is evaluated via numerical simulations. Our results show that, compared to the traditional antenna selection algorithms, RAISE can achieve near optimal capacity performance while the computational complexity and the memory cost are significantly reduced.

We consider antenna selection in massive multi-input multi-output (MIMO) systems. Most prior works ideally assume perfect channel estimation at the receiver side. In this paper, we present an antenna selection scheme based on a genetic algorithm for massive MIMO systems in the presence of channel estimation error (CEE). Numerical results suggest that the proposed algorithm outperforms traditional antenna selection algorithms in terms of channel capacity, especially for low signal-to-noise ratio (SNR) scenarios.

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.

Energy efficient communications in cognitive radio (CR) networks has been of great interest. In order to address the power consumption problem and to maximise the energy efficiency (EE) in MIMO-OFDM underlay CR systems, power allocation (PA) and antenna selection (AS) algorithms are proposed. Interference alignment, which helps in mitigating the interference at primary and secondary receivers, is considered for setting-up the underlay CR scenario. The AS transmit is employed for the secondary users (SUs) on a per-subcarrier basis to improve their QoS. Further, to improve the QoS of both primary user (PU) and SUs, the AS is followed by energy efficient PA. Due to the fractional nature of EE, the EE maximisation problem is transformed into an equivalent parametric optimisation problem based on Dinkelbach method. Simulation results confirm that the proposed EE aware algorithms can improve the EE performance of both PU and SUs than the SE aware algorithms.

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 low-rank approximation problem is to approximate optimally, with respect to some norm, a matrix by one of the same dimension but smaller rank. It is known that under the Frobenius norm, the best low-rank approximation can be found by using the singular value decomposition (SVD). Although this is no longer true under weighted norms in general, it is demonstrated here that the weighted low-rank approximation problem can be solved by finding the subspace that minimizes a particular cost function. A number of advantages of this parameterization over the traditional parameterization are elucidated. Finding the minimizing subspace is equivalent to minimizing a cost function on the Grassmann manifold. A general framework for constructing optimization algorithms on manifolds is presented and it is shown that existing algorithms in the literature are special cases of this framework. Within this framework, two novel algorithms (a steepest descent algorithm and a Newton-like algorithm) are derived for solving the weighted low-rank approximation problem. They are compared with other algorithms for low-rank approximation as well as with other algorithms for minimizing a cost function on a Grassmann manifold.

In multiple-input–multiple-output (MIMO) wireless systems, antenna selection is a methodology of achieving high capacity with less cost. In this paper, three computationally very efficient and simple sub-optimal joint and decoupled transmit/receive antenna selection algorithms are proposed for capacity maximisation of MIMO wireless systems. These algorithms can be used in applications, where channel is not sufficiently-slow varying and is somewhat fast varying. Simulations have been carried out to assess their performances and complexities relative to other schemes.

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.