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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

In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as non-AI mitigation approaches (for example, adaptive filters). The key downside to non-AI techniques is the need for domain expertise in the extraction or exploitation of signal features such as cyclostationarity, bandwidth and modulation of the interfering signals. More recently, researchers have successfully explored AI/ML enabled physical (PHY) layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. The underlying idea of ML based approaches is to learn the interference or the interference characteristics from the data, thereby sidelining the need for domain expertise in suppressing the interference. In this paper, we review a wide range of techniques that have used deep learning to suppress interference. We provide comparison and guidelines for many different types of deep learning techniques in interference suppression. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in interference suppression., Comment: 26 pages, 10 figures, journal article

Next generation wireless communications systems are pushing the limits of both energy efficiency and spectral efficiency. This presents a challenge at the receiver when it comes to accomplishing tasks such as synchronization, channel estimation, and equalization and has motivated the development of code-aided iterative receiver algorithms in the technical literature. In this paper, we focus on the task of frame synchronization. While previous work has predominately assumed an additive white Gaussian noise channel, we develop code-aided frame synchronization algorithms for multipath channels. An iterative receiver is presented which integrates frame synchronization with iterative channel estimation, equalization, demodulation, and decoding. The receiver design includes a novel frame pre-processing stage to reduce the complexity of the proposed receiver. The complexity and performance of the proposed receiver is compared with that of a receiver based on conventional synchronization. The results demonstrate that the proposed receiver is capable of achieving a gain of up to 3 dB while increasing complexity by only 20%.

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.

Detection of co-channel signals is important as wireless communication systems become increasingly dense. A particularly challenging case is single antenna reception in both the presence of inter-symbol interference and co-channel interference. Optimal joint maximum a posteriori probability (MAP) detection of the co-channel signals is prohibitively complex. Therefore, in this paper, we propose a factor graph-based iterative receiver which approximates joint MAP detection. In the receiver, noise is modeled with a Gaussian mixture distribution since studies have shown that the noise affecting wireless communication systems is often impulsive. Furthermore, the parameters of the factor graph model (channel and noise parameters) are iteratively estimated within the receiver. The proposed receiver is shown to the outperform state-of-the-art receiver algorithms while having a lower complexity in both Gaussian and impulsive noise. We also show significant gains from iterative parameter estimation, especially in non-Gaussian noise.