Iterative decoding for MIMO channels via modified sphere decoding

In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain

Three-transmit-antenna space-time codes based on SU(3)

Fully diverse constellations, i.e., a set of unitary matrices whose pairwise differences are nonsingular, are useful in multiantenna communications especially in multiantenna differential modulation, since they have good pairwise error properties. Recently, group theoretic ideas, especially fixed-point-free (fpf) groups, have been used to design fully diverse constellations of unitary matrices. Here, we give systematic design methods of space-time codes which are appropriate for three-transmit-antenna differential modulation. The structures of the codes are motivated by the special unitary Lie group SU(3). One of the codes, which is called the AB code, has a fast maximum-likelihood (ML) decoding algorithm using complex sphere decoding. Diversity products of the codes can be easily calculated, and simulated performance shows that they are better than group-based codes, especially at high rates and as good as the elaborately designed nongroup code

How much training is needed in multiple-antenna wireless links?

Multiple-antenna wireless communication links promise very high data rates with low error probabilities, especially when the wireless channel response is known at the receiver. In practice, knowledge of the channel is often obtained by sending known training symbols to the receiver. We show how training affects the capacity of a fading channel-too little training and the channel is improperly learned, too much training and there is no time left for data transmission before the channel changes. We compute a lower bound on the capacity of a channel that is learned by training, and maximize the bound as a function of the received signal-to-noise ratio (SNR), fading coherence time, and number of transmitter antennas. When the training and data powers are allowed to vary, we show that the optimal number of training symbols is equal to the number of transmit antennas-this number is also the smallest training interval length that guarantees meaningful estimates of the channel matrix. When the training and data powers are instead required to be equal, the optimal number of symbols may be larger than the number of antennas. We show that training-based schemes can be optimal at high SNR, but suboptimal at low SNR

On the sphere-decoding algorithm II. Generalizations, second-order statistics, and applications to communications

In Part 1, we found a closed-form expression for the expected complexity of the sphere-decoding algorithm, both for the infinite and finite lattice. We continue the discussion in this paper by generalizing the results to the complex version of the problem and using the expected complexity expressions to determine situations where sphere decoding is practically feasible. In particular, we consider applications of sphere decoding to detection in multiantenna systems. We show that, for a wide range of signal-to-noise ratios (SNRs), rates, and numbers of antennas, the expected complexity is polynomial, in fact, often roughly cubic. Since many communications systems operate at noise levels for which the expected complexity turns out to be polynomial, this suggests that maximum-likelihood decoding, which was hitherto thought to be computationally intractable, can, in fact, be implemented in real-time-a result with many practical implications. To provide complexity information beyond the mean, we derive a closed-form expression for the variance of the complexity of sphere-decoding algorithm in a finite lattice. Furthermore, we consider the expected complexity of sphere decoding for channels with memory, where the lattice-generating matrix has a special Toeplitz structure. Results indicate that the expected complexity in this case is, too, polynomial over a wide range of SNRs, rates, data blocks, and channel impulse response lengths

Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding

In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter $\alpha$ and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter $\alpha_{\mathrm{LS}}$ of the precoder using large-system analysis. We show that the RCI precoder with $\alpha_{\mathrm{LS}}$ outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed $\alpha$. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing $\alpha$ and the power allocation vector. The jointly optimized precoder outperforms RCI with $\alpha_{\mathrm{LS}}$ and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas.Comment: IEEE Transactions on Communications, accepted for publicatio

On the Capacity Region of Multi-Antenna Gaussian Broadcast Channels with Estimation Error

In this paper we consider the effect of channel estimation error on the capacity region of MIMO Gaussian broadcast channels. It is assumed that the receivers and the transmitter have (the same) estimates of the channel coefficients (i.e., the feedback channel is noiseless). We obtain an achievable rate region based on the dirty paper coding scheme. We show that this region is given by the capacity region of a dual multi-access channel with a noise covariance that depends on the transmit power. We explore this duality to give the asymptotic behavior of the sum-rate for a system with a large number of user, i.e., n rarr infin. It is shown that as long as the estimation error is of fixed (w.r.t n) variance, the sum-capacity is of order M log log n, where M is the number of antennas deployed at the transmitter. We further obtain the sum-rate loss due to the estimation error. Finally, we consider a training-based scheme for block fading MISO Gaussian broadcast channels. We find the optimum length of the training interval as well as the optimum power used for training in order to maximize the achievable sum-rate

Turbo space-time coded modulation : principle and performance analysis

A breakthrough in coding was achieved with the invention of turbo codes. Turbo codes approach Shannon capacity by displaying the properties of long random codes, yet allowing efficient decoding. Coding alone, however, cannot fully address the problem of multipath fading channel. Recent advances in information theory have revolutionized the traditional view of multipath channel as an impairment. New results show that high gains in capacity can be achieved through the use of multiple antennas at the transmitter and the receiver. To take advantage of these new results in information theory, it is necessary to devise methods that allow communication systems to operate close to the predicted capacity. One such method recently invented is space-time coding, which provides both coding gain and diversity advantage. In this dissertation, a new class of codes is proposed that extends the concept of turbo coding to include space-time encoders as constituent building blocks of turbo codes. The codes are referred to as turbo spacetime coded modulation (turbo-STCM). The motivation behind the turbo-STCM concept is to fuse the important properties of turbo and space-time codes into a unified design framework. A turbo-STCM encoder is proposed, which consists of two space-time codes in recursive systematic form concatenated in parallel. An iterative symbol-by-symbol maximum a posteriori algorithm operating in the log domain is developed for decoding turbo-STCM. The decoder employs two a posteriori probability (APP) computing modules concatenated in parallel; one module for each constituent code. The analysis of turbo-STCM is demonstrated through simulations and theoretical closed-form expressions. Simulation results are provided for 4-PSK and 8-PSK schemes over the Rayleigh block-fading channel. It is shown that the turbo-STCM scheme features full diversity and full coding rate. The significant gain can be obtained in performance over conventional space-time codes of similar complexity. The analytical union bound to the bit error probability is derived for turbo-STCM over the additive white Gaussian noise (AWGN) and the Rayleigh block-fading channels. The bound makes it possible to express the performance analysis of turbo-STCM in terms of the properties of the constituent space-time codes. The union bound is demonstrated for 4-PSK and 8-PSK turbo-STCM with two transmit antennas and one/two receive antennas. Information theoretic bounds such as Shannon capacity, cutoff rate, outage capacity and the Fano bound, are computed for multiantenna systems over the AWGN and fading channels. These bounds are subsequently used as benchmarks for demonstrating the performance of turbo-STCM

Low Complexity Blind Equalization for OFDM Systems with General Constellations

This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm