Emre Aktas

Distributed Decoding in a Cellular Multiple-Access Channel

Distributed decoding in the uplink of a rectangular planar cellular array with local message passing is considered. Two algorithms are proposed and compared: a BCJR-type algorithm applied to linear subgraphs, and belief propagation applied to the 2D graph of the cellular array.

Distributed decoding in a cellular multiple-access channel

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local message passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single-user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to have full frequency re-use and achieve near-optimal performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

Complexity reduction in subspace-based blind channel identification for DS/CDMA systems

Direct-sequence code-division multiple access is emerging as a potential multiple-access communication scheme for future digital wireless communications systems. Such wide-band systems usually operate in a frequency-selective fading channel that introduces intersymbol interference and thus potential performance degradation. Previously proposed subspace-based blind channel identification algorithms, which provide estimates of channel parameters for effective equalization, suffer from high numerical complexity for systems with large spreading gains. In this paper, it is shown that, through the use of matched filter outputs, reduction in numerical complexity can be obtained. The complexity reduction is considerable when the channel length is small and the system is moderately loaded. The results show that the new algorithm suffers a slight performance loss. Although the employed matched filter outputs do not form a set of sufficient statistics for the unknown channels, the difference between the matched filter outputs and the sufficient statistics becomes negligible for large observation lengths and the asymptotic normalized Fisher information does not change. Performance is evaluated through simulations, the derivation of a tight approximation of the mean-squared channel estimation error, and through comparisons to the Cramer-Rao bound for the estimation error variance. It is shown that the approximation of the mean-squared error can be obtained in terms of the correlation of the spreading codes and the channels. This representation of the error supplies a tool for investigating the relationship between performance and spreading sequence correlations.

Semiblind channel estimation for CDMA systems with parallel data and pilot signals

Semiblind channel estimation combines the methods of channel estimation based on a pilot signal and blind channel estimation based on a data-only conveying signal. Maximum-likelihood (ML)-based semiblind estimators with Gaussian assumptions can provide improvement in performance, compared with channel-estimation schemes using the pilot signal only. This improvement can be even larger when the pilot and the data signals are sent simultaneously, as is the case in the third-generation wideband code-division multiple-access standards. However, the Gaussian ML approach results in very large complexity. Previously proposed semiblind methods with low complexity have been derived for serial pilot and data transmission, and are not suitable for the parallel transmission case. In this paper, algorithms for semiblind channel estimation for the parallel data and training signal case are developed. Approximations which reduce the computational complexity of the Gaussian ML method significantly are proposed. Solutions with iterations with very low attendant complexity are provided. The mean squared error analysis of the proposed method is obtained and compared with that of a method with no approximations. The approximations are justified through simulations, and the performance improvement over estimation schemes using the pilot signal solely is verified.

Distributed Base Station Processing in the Uplink of Cellular Networks

This paper considers the problem of joint detection in the uplink of cellular multiaccess networks with base-station cooperation. Distributed multiuser detection algorithms with local passing among neighbor base stations are proposed and compared in terms of computational complexity required in the base stations, the amount of serial communications among them, error rate performance, and convergence speed. The algorithms based on the belief propagation algorithm result in complexity and delay per base station which do not grow as the network size increases. In addition, it is observed that these algorithms have near single user error rate performance for the fading channels considered. Thus it is illustrated that using the belief propagation algorithm, it is possible to use non-orthogonal signaling and still achieve near single user performance with moderate computational complexity and a limited amount of message passing between base stations of adjacent cells.

Single-user sparse channel acquisition in multiuser DS-CDMA systems

Single-user channel estimation in multiuser DS-CDMA systems for the case of sparse channels with large delay spreads is addressed. In addition, practical pulse shapes are considered. In sparse channels, the efficient way to estimate the parameters is to estimate the continuous delays of each path, instead of using the typical discrete tapped delay-line model. Due to the facts that the desired delays are not drawn from a simple finite set and that band-limited pulse shapes are employed, the resulting methods require numerical optimization techniques. To facilitate estimation, it is proposed to optimize the spreading code employed during the training, or estimation, phase. The optimal single-path spreading code is derived and extended for estimation in the multipath scenario. Both single-path and multipath channel estimation are considered. The proposed algorithms are evaluated through simulation and via the determination of the Cramer-Rao lower bound on the estimation variance. Analytical approximations of key performance measures are also derived and are seen to be tight for a variety of scenarios.

Practical Methods for Wireless Network Coding With Multiple Unicast Transmissions

We propose a simple yet effective wireless network coding and decoding technique for a multiple unicast network. It utilizes spatial diversity through cooperation between nodes which carry out distributed encoding operations dictated by generator matrices of linear block codes. In order to exemplify the technique, we make use of greedy codes over the binary field and show that the arbitrary diversity orders can be flexibly assigned to nodes. Furthermore, we present the optimal detection rule for the given model that accounts for intermediate node errors and suggest a low-complexity network decoder using the sum-product (SP) algorithm. The proposed SP detector exhibits near optimal performance. We also show asymptotic superiority of network coding over a method that utilizes the wireless channel in a repetitive manner without network coding (NC) and give related rate-diversity trade-off curves. Finally, we extend the given encoding method through selective encoding in order to obtain extra coding gains.

High data rate MIMO-OFDM system with orthogonal delay diversity realization

A high rate delayed space-frequency cyclic group code is proposed in this paper, which applies a new bandwidth and power-efficient space-frequency processing on the orthogonal-frequency-division-multiplexing (OFDM) system. The delayed orthogonality with respect to frequency tones among different transmitter antennas guarantees to exploit the full spatial and frequency diversity of the frequency-selective multi-input multi-output (MlMO) channel. In addition, after partitioning the subcarriers into groups and coding each group with the proposed block code, a maximized multiplexing gain can be obtained without any diversity loss. The proposed code can also be easily generalized to multiple transmitter antenna systems.

Error Rate Analysis of GF(q) Network Coded Detect-and-Forward Wireless Relay Networks Using Equivalent Relay Channel Models

This paper investigates simple means of analyzing the error rate performance of a general q-ary Galois Field network coded detect-and-forward cooperative relay network with known relay error statistics at the destination. Equivalent relay channels are used in obtaining an approximate error rate of the relay network, from which the diversity order is found. Error rate analyses using equivalent relay channel models are shown to be closely matched with simulation results. Using the equivalent relay channels, low complexity receivers are developed whose performances are close to that of the optimal maximum likelihood receiver.

Blind equalization for an application of unitary space-time modulation in ISI channels

Transmit diversity schemes have gained attention due to the promise of increased capacity and improved performance. Among these schemes, unitary space-time modulation and differentially encoded unitary space-time modulation allow for simple noncoherent decoding for flat-fading channels. In this paper, a new blind equalization algorithm for these transmission schemes in intersymbol interference (ISI) channels is proposed. A matrix-type constant modulus algorithm that exploits the unitary structure of the space-time codes is developed. The equalizer is paired with a noncoherent decoder, resulting in a completely blind, low-complexity method for decoding in the presence of ISI. A noiseless convergence analysis is conducted and verified via simulation in both noiseless and noisy cases. The performance of the overall system is evaluated via simulation and semi-analytically, and the achieved performance is between that of the ideal zero-forcing and the minimum-mean squared-error equalizers.