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With the recent introduction of electromagnetic meta-surfaces and reconfigurable intelligent surfaces, a paradigm shift is currently taking place in the world of wireless communications and related industries. These new technologies have enabled the inclusion of the wireless channel as part of the optimization process. This is of great interest as we transition from 5G mobile communications towards 6G. In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G communication system. We investigate how the performance of the MUSIC algorithm at the unintended receiver is degraded by correlated reflected signals introduced by a reconfigurable intelligent surface in the wireless channel. We analyze the impact of the direction of arrival, delay, correlation, and strength of the reconfigurable intelligent surface signal with respect to the line-of-sight path from the transmitter to the unintended receiver. An effective method is introduced for defeating direction-finding efforts using dual sets of surface reflections. This novel method is called Geolocation-Probability Reduction using Dual Reconfigurable Intelligent Surfaces (GPRIS). We also show that the efficiency of this method is highly dependent on the geometry, that is, the placement of the reconfigurable intelligent surface relative to the unintended receiver and the transmitter., Comment: 6 pages, 13 figures, 1 table, submitted to 2023 IEEE Wireless Communications and Networking Conference

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. However, the MF approximation is not particularly effective for multi-signal detection. For this reason, we propose a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We also developed a low-complexity variation to the proposed construction in which Gaussian BP is applied to detection and expectation propagation links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection and, in general, detection in the presence of interference.

In this paper, indoor passive localization and tracking using a multiple-input multiple-output (MIMO) radar are studied. Indoor localization suffers from severe multipath propagation. We propose two novel clustering-based algorithms for multi-target tracking in such environments. These techniques aim to exploit either the delay-Doppler diversity offered by spread spectrum signals or the delay-angle diversity of received signals using successive beamforming. The targets are tracked using a bank of extended Kalman filters and the filters' predicted future locations of all targets are intelligently utilized to solve the data association problem. Finally, the feasibility of both techniques is analyzed and discussed in light of two common indoor tracking layouts.

We consider joint detection of co-channel signals— specifically, signals which do not possess a natural separability due to, for example, the multiple access technique or the use of multiple antennas. Iterative joint detection and decoding is a well known approach for utilizing the error correction code to improve detection performance. However, the joint maximum a posteriori probability (MAP) detector may be prohibitively complex, especially in a multipath channel. In this paper, we present an approximation to the joint MAP detector motivated by a factor graph model of the received signal. The proposed algorithm is designed to approximate the joint MAP detector as closely as possible within the computational capability of the receiver.

Next generation wireless communication systems are pushing the limits of both energy efficiency and spectral efficiency. This presents a challenge to other functions in the receiver such as frame synchronization. In this paper we examine the trade-off between increased complexity and the improvement in energy and spectral efficiency of code-aided frame synchronization. Parallel and serial approaches to the frame synchronization problem are considered as well as methods for minimizing their complexity. We identify regions over which code-aided frame synchronization improves performance, with respect to a conventional receiver, while maintaining reasonable complexity.

We present a signal detection algorithm for digital amplitude-phase modulated signals in frequency-selective fading channels with non-Gaussian noise. We consider an asynchronous scenario in which the timing (symbol transition epochs) is unknown and a symbol rate offset is present due to clock drift. A Gibbs sampling-based algorithm is proposed to estimate the unknown parameters and signal detection is performed using a maximum-likelihood procedure. The additive noise is modeled by a Gaussian mixture distribution, a well-known model for man-made and natural noise. Numerical results are presented to characterize the performance of the proposed algorithm.