Abstract-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 timevarying 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.

Abstract-A method to improve the performance of multiple-input-multiple-output systems is to employ a large number of antennas and select the optimal subset depending on the specific channel realization. A simple antenna-selection criterion is to choose the antenna subset that maximizes the mutual information. However, when the receiver has finite complexity decoders, this criterion does not necessarily minimize the error rate (ER). Therefore, different selection criteria should be tailored to the specific receiver implementation. In this paper, we develop new antenna-selection criteria to minimize the ER in spatial multiplexing systems with lattice-reduction-aided receivers. We also adapt other known selection criteria, such as maximum mutual information, to this specific receiver. Moreover, we consider adaptive antenna-selection algorithms when the channel is not perfectly known at the receiver but can only be estimated. We present simulation examples to show the ER of the different selection criteria and the convergence of the adaptive algorithms. We also discuss the difference in complexity and performance among them.Index Terms-Adaptive algorithm, antenna selection, lattice reduction, minimum error rate (ER), multiple-input-multiple-output (MIMO).

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We propose a simple PAM-based coded modulation scheme that overcomes two major constraints of power-line channels, viz., severe insertion-loss and impulsive noise.The scheme combines low-density parity-check (LDPC) codes, along with cyclic randomerror and burst-error correction codes to achieve high spectral efficiency, low decoding complexity, and a high degree of immunity to impulse noise. To achieve good performance in the presence of inter-symbol interference (ISI) on static or slowly timevarying channels, the proposed coset-coding is employed in conjunction with TomlinsonHarashima precoding and spectral shaping at the transmitter. In Gaussian noise, the scheme performs within 2 dB of un-shaped channel capacity (the sphere-bound) at a BER of 10 −11 , even with simple regular LDPC codes of modest length (1000-2000 bits). To mitigate errors due to impulse noise (a combination of synchronous and asynchronous impulses), a multi-stage interleaver is proposed, each stage tailored to the error-correcting property of each layer of the coset decomposition. In the presence of residual ISI, colored Gaussian noise, as well as severe synchronous and asynchronous impulse noise, the gap to Shannon-capacity of the scheme to a Gaussian-noise-only channel is 5.5 dB at a BER of 10 −7 .

Abstract-Orthogonal Frequency Division Multiplexing (OFDM) significantly reduces receiver complexity in wireless broadband systems and therefore has recently been proposed for use in wireless broadband multi-antenna (MIMO) systems. The performance of maximum likelihood detector in MIMO-OFDM system is optimal, however, its complexity, especially with higher order constellation is prohibitive. A number of other detectors, both linear and non-linear, may offer substantially lower complexity, however, their performance is significantly lower. This paper proposes a class of lattice-reduction-aided (LRA) receivers for MIMO-OFDM systems that can achieve near maximum likelihood detector performance with low complexity. Performance comparisons between LRA receiver and other popular receivers, including linear receivers and V-BLAST in both independent and correlated channels, are provided. It will be shown that the performance of LRA receiver is superior as compared to other sub-optimal detection methods, especially when the channel is correlated.

We consider transmit antenna subset selection in spatial multiplexing systems. In particular, we propose selection algorithms aiming to minimize the error rate when linear detectors are used at the receiver. Previous work on antenna selection has considered capacity and post-processing SNR selection criteria. However, in this work we consider a geometrical interpretation of the decoding process which also permits us to develop a suboptimal algorithm that yields a considerable complexity reduction with only a small loss in performance. Introduction: Wireless systems employing multiple antennas at the transmitter and at the receiver (MIMO systems) are a solution to increase the capacity of the wireless channel [1]. One major concern in the implementation of these systems is the high cost due to the price of the RF chains (analog-digital converters, low noise amplifiers, downconverters, etc.) attached to each antenna. A technique to reduce the cost of the MIMO system while maintaining part of the high performance is the use of antenna selection. The idea behind antenna selection is to employ a large number of inexpensive antenna elements and to use only the best subset. Then, only a limited number of the more expensive RF chains is necessary. The question that arises is how to select the best antenna subset among all the available antennas. An intuitive approach is to select the antenna subset that maximizes the mathematical expression of the mutual information between the transmitter and the receiver (see [2] and references therein). Using this approach, in [3] it is shown that the diversity obtained using antenna selection in spatial multiplexing systems is the same as the

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Abstract-We consider the downlink of multiuser multipleinput single-output (MISO) wireless systems, where the base station is equipped with multiple antennas and each mobile user is constrained to a single antenna. In particular, we consider linear precoded systems such that the single antenna receivers do not have to estimate the channel, but only scale and quantize the received data. In this scenario, we propose low complexity opportunistic user scheduling and antenna selection algorithms. The highly complex optimal scheduling and antenna selection algorithms are first derived, and then, low complexity greedy optimization algorithms are proposed. It is shown that the proposed algorithms obtain near optimal performance.