Performance of Optimum Combining in a Poisson Field of Interferers and Rayleigh Fading Channels

This paper studies the performance of antenna array processing in distributed multiple access networks without power control. The interference is represented as a Poisson point process. Desired and interfering signals are subject to both path-loss fading (with an exponent greater than 2) and to independent Rayleigh fading. Using these assumptions, we derive the exact closed form expression for the cumulative distribution function of the output signal-to-interference-plus-noise ratio when optimum combining is applied. This results in a pertinent measure of the network performance in terms of the outage probability, which in turn provides insights into the network capacity gain that could be achieved with antenna array processing. We present and discuss examples of applications, as well as some numerical results.Comment: Submitted to IEEE Trans. on Wireless Communication (Jan. 2009

A nonlinear M-estimation approach to robust asynchronous multiuser detection in Non-gaussian noise

A nonlinear M-estimation approach is proposed to solve the multiuser detection problem in asynchronous code-division multiple-access (CDMA) systems where the ambient noise is impulsive and the delays are not known. We treat the unknown delays as nuisance parameters and the transmitted symbols as parameters of interest. We also analyze the asymptotic performance of the proposed estimator and propose suboptimal but computationally efficient procedures for solving the nonlinear optimization function. Simulation results show considerable improvements over the conventional approaches

Modeling and Mitigation of Wireless Communications Interference for Spectrum Sharing with Radar

Due to both economic incentives and policy mandates, researchers increasingly face the challenge of enabling spectrum sharing between radar and wireless communications systems. In the past eight years, researchers have begun to suggest a wide variety of approaches to radar-communications spectrum sharing, ranging from transmitter design to receiver design, from spatial to temporal to other-dimensional multiplexing, and from cooperative to non-cooperative sharing. Within this diverse field of innovation, this dissertation makes two primary contributions. First, a model for wireless communications interference and its effects on adaptive-threshold radar detection is proposed. Based on both theoretical and empirical study, we find evidence for both Gaussian and non-Gaussian communications interference models, depending on the modeling situation. Further, such interference can impact radar receivers via two mechanisms—model mismatch and boost to the underlying noise floor—and both mechanisms deserve attention. Second, an innovative signal processing algorithm is proposed for radar detection in the presence of cyclostationary, linearly-modulated, digital communications (LMDC) interference (such as OFDM or CDMA) and a stationary background component. The proposed detector consists of a novel whitening filter followed by the traditional matched filter. Performance results indicate that the proposed cyclostationary-based detector outperforms a standard equivalent detector based on a stationary interference model, particularly when the number of cyclostationary LMDC transmitters is small and their interference-to-noise ratio (INR) is large relative to the stationary background

Capacity of a Nonlinear Optical Channel with Finite Memory

The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite memory of realistic fiber channels. This finite-memory model is harder to analyze mathematically but, in contrast to previous models, it is valid also for nonstationary or heavy-tailed input signals. For uncoded transmission and standard modulation formats, the new model gives the same results as the regular GN model when the memory of the channel is about 10 symbols or more. These results confirm previous results that the GN model is accurate for uncoded transmission. However, when coding is considered, the results obtained using the finite-memory model are very different from those obtained by previous models, even when the channel memory is large. In particular, the peaky behavior of the channel capacity, which has been reported for numerous nonlinear channel models, appears to be an artifact of applying models derived for independent input in a coded (i.e., dependent) scenario

Adaptive detection of distributed targets in compound-Gaussian noise without secondary data: A Bayesian approach

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available.The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios