In a general wireless system, cells are loosely defined and user signals appear at multiple antennas with various powers and delays. Despite the enormous performance benefits of system-wide maximum-likelihood multiuser detection (ML MUD), its application to such systems is hampered by the lack of a regular structure. Prior work usually dismisses the possibility on computational grounds as exponential in the total number of users, at least. This paper is the first to address efficient computation of system-wide ML MUD. We present a computational organization that achieves dramatic reduction in complexity through exploitation of the partial overlap of user sets at different antennas. This algorithm, which applies to code-division multiple access or narrowband systems, can be viewed as a spatio-temporal extension of the well-known Viterbi algorithm (VA), and, like the VA, it is derived from dynamic programming principles.

The authors present a performance analysis for the reverse link of a wireless DS-code division multiple access (CDMA) system that exploits macrodiversity reception while adopting the maximum likelihood (ML) multiuser detector for the basic detection. This appears to be the first analytical treatment of the ML multiuser macrodiversity detector in asynchronous DS-CDMA systems. They also include the effects of frequency selective fading, imperfect synchronization, and imperfect channel state information. Power control and connection control companion algorithms are presented to assess the value of this detector in a system context. The authors have shown that the ML multiuser-macrodiversity detector is capable of reducing the bit-error rate for many users by several orders of magnitude compared with multiuser detectors that operate on each antenna separately.

This article presents a new computational tool for use in general fading channel analysis when the detection scheme can be expressed as a quadratic form in zero-mean complex Gaussian random variables. We develop a simple numerical algorithm which is capable of inverting a characteristic function consisting of both simple and multiple poles. The approach benefits from the inherent symmetry in the residue calculations and uses the well-known Vandermonde matrix in order to take advantage of this symmetry. It is numerically stable, eliminates singularities, and circumvents the need for differentiation.

The combination of macrodiversity reception with maximum likelihood (ML) multiuser detection has the capability to reduce the bit error rate (BER) for many users by several orders of magnitude compared with multiuser detectors that operate on each antenna separately. In this paper, we present the conditional metric merge (CMM) algorithm which reduces the computational complexity of the ML multiuser-macrodiversity detector by an enormous factor. The CMM algorithm can be viewed as a spatial variant of the Viterbi algorithm. It is a new algorithm and is the first of its kind as ML multiuser-macrodiversity detection (MUMD) is a relatively new area of research.

Errors and erasures decoding has the capability to improve the performance of a Reed-Solomon decoder if each error location is correctly identified, or "flagged", and is considered as an erasure. However, the erasure flagging must be extremely reliable. In this paper, we present a new way to flag erasures, based on the side information provided by pilot signals. We derive a Bayesian method for optimum character erasure flagging, using both the received data signals and the channel estimates provided by pilot signals, and we present a simpler method which flags erasures on a modulation symbol basis. The simulation results demonstrate that the method results in significant transmit power savings compared to errors-only decoding.