To extend network coverage and to possibly increase data packet throughput, the future wireless cellular networks could adopt relay nodes for multi-hop data transmission. This letter proposes a novel distributed scheduling algorithm for downlink relay networks. Soft-information indicating the probability of activating each network link is exchanged iteratively among neighboring network nodes to determine an efficient schedule. To ensure collision-free simultaneous data transmissions, collision-avoiding local constraint rules are enforced at each network node. To increase resource utility, the soft-information is weighted according to the urgency of data transmission across each link, which also helps maintain throughput fairness among network users.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications

A low-complexity high-accuracy algorithm is proposed to estimate the location of a target MS based on network-side time-of-arrival (TOA) measurements. Under a factor graph framework, the proposed algorithm first constructs a graphical model for the mobile position location problem by dividing the problem into many mutually-interactive local constraints. Each local constraint is enforced by a separate local processing unit. Efficient exchange of soft-information among local processing units in the mobile switching center (MSC) then iteratively purifies the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable solution accuracy, the maximum likelihood (ML) solution accuracy

Recent advances in information theory show that employing multiple antennas at both sides of a wireless channel promises enormous capacity potential. With perfect knowledge of channel state information (CSI) at the transmitter, eigen-beamforming is the optimal coding scheme to exploit this potential. However, in nonstationary wireless environments, high complexity on multiple-input-multiple-output (MIMO) channel tracking and large amounts of CSI feedback render such an approach impractical. By exploiting the wireless multipath channel structure characterized by the path delays and the path directions-of-departure/arrival, we propose a new space-time transmit scheme which employs a structure-based water-filling algorithm.

In a wireless system with multipath multiple-input multiple-output (MIMO) channels using antenna arrays, the delay spread of the multipaths results in intersymbol interference (ISI) and channel frequency selectivity. In contrast with the channel frequency selectivity, spatial gains at the multipath angles also naturally result in channel angle selectivity. Based on the channel selectivity structures characterized by the path delays and the path directions-of-departure/arrival, we seek new insights into the matching of space-time codes and multipath MIMO channels in their angle-frequency (AF) structures.

Systems that employ multiple antennas in both the transmitter and the receiver of a wireless system have been shown to promise extraordinary spectral efficiency. With full channel knowledge at the transmitter and receiver, Raleigh and Cioffi (1998) proposed a spatio-temporal coding scheme, discrete matrix multitone (DMMT), to achieve asymptotically optimum multiple-input-multiple-output (MIMO) channel capacity. The DMMT can be regarded as an extension of the discrete multitone for a digital subscriber lines (DSL) system to the MIMO wireless application. However, the DMMT is basically impracticable in nonstationary wireless environments due to its high-computational complexity. Exploring second-order statistics, we develop an efficient adaptive blind coding scheme for a high-capacity time-division duplexing (TDD) system with slow time-varying frequency-selective MIMO channels. With this method, neither a training sequence nor feedback of channel information is required in the proposed blind approach. Besides, the computational complexity of the proposed scheme is significantly lower than that of the coding scheme described by Raleigh and Cioffi. Simulation results show that the proposed architecture works efficiently in indoor wireless local area network applications.

We use the parametric channel identification algorithm proposed by Chen, Kim and Liang (see IEEE Transactions on Vehicular Technology, p.1923-35, 1999) (to adaptively track the fast-fading channels for the multi-channel MLSE equalizer. The channel tracking scheme per-survivor processing recursive least square (PSP RLS) is considered. An analytic lower bound for the multi-channel MLSE equalizer in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms are presented, and then these results are compared with the analytic BER lower bound and with the conventional MLSE equalizers directly tracking the FIR channel taps. The proposed algorithm is shown to perform significantly better than the conventional adaptive MLSE algorithm.

In wireless communication systems with specular multipath channels, the channel structure can be used to improve the system performance.] However, the channel structure turns obscure for diffused multipath channels. We analyze the diffused multipath channels and develop a low-complexity algorithm to determine the optimal dimension of the approximated channel subspace as well as the optimal basis for the subspace, in the sense of least-square error of the channel estimates. Simulation results show that (1) the system performance is significantly improved by exploring the structure of the diffused multipath channels, (2) the performance improvement is even more significant in a severe environment where the signal to noise ratio is low.

The parametric channel estimator is used in the parametric maximum-likelihood sequential estimation algorithms used to equalize the multipath wireless channels. We examine the performance of the parametric channel estimators against additive white Gaussian noise and the nonparametric channel model. We first show that the parametric channel estimators eliminate all the noise outside the signal subspace, and thus significantly reduce the performance degradation caused by the noise. Next, we show that the conventional channel estimators are special cases of the parametric channel estimator subject to some deteriorating constraints regardless of the channel model.

We use the parametric channel identification algorithm proposed by Chen and Paulraj (see Proc. IEEE Vehicular Technology Conf., p.710-14, 1997) and by Chen, Kim and Liang (see IEEE Trans. Veh. Technol., p.1923-35, 1999) to adaptively track the fast-fading channels for the multichannel maximum likelihood sequence estimation (MLSE) equalizer using multiple antennas. Several commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. An analytic lower bound for the multichannel MLSE equalizer with no channel mismatch in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms working with various channel tracking schemes are presented, and then these results are compared with the analytic bit error rate (BER) lower bound and with the conventional MLSE equalizers directly tracking the finite impulse response (FIR) channel tap coefficients. We found that the proposed algorithm always performs better than the conventional adaptive MLSE algorithm, no matter what channel tracking scheme is used. However, which is the best tracking scheme to use depends on the scenario of the system.