Department Publications

This manuscript introduces NeuroDetect, a model-free deep learning-based signal detection framework tailored for phase-modulated wireless systems with low-resolution analog-to-digital converters (ADCs). The proposed framework eliminates the need for explicit channel state information, which is typically difficult to acquire under coarse quantization. NeuroDetect utilizes a neural network architecture to learn the nonlinear relationship between quantized received signals and transmitted symbols directly from data. It achieves near-optimum performance, within a worst-case 12% margin of the maximum likelihood detector that assumes perfect channel knowledge. We rigorously investigate the interplay between ADC resolution and detection accuracy, introducing novel penalty metrics that quantify the effects of both quantization and learning errors. Our results shed light on the design trade-offs between ADC resolution and detection accuracy, providing future directions for developing energy-efficient high-speed and wideband wireless systems.

Future wireless systems are expected to rely on large antenna arrays at the base station (BS) to meet growing demands for data rates. However, the power consumption and hardware complexity of high-resolution analog-to-digital converters (ADCs) pose significant challenges to the practical deployment of such arrays. Low-resolution ADCs offer a power- and cost-effective alternative, motivating a deeper understanding of information-theoretic limits with low-resolution quantization. This letter establishes capacity scaling laws for both single-input single-output (SISO) and multi-antenna systems with low-resolution quantization. We show that, unlike the classical capacity growth in unquantized systems, the capacity of low-resolution systems approaches n at a rate for SISO and multiple-input single-output (MISO) systems, where n is the number of quantization bits and is the signal-to-noise ratio. For …

This paper investigates an intelligent reflective surface (IRS) assisted secure multi-user two-way communication system. The aim of this paper is to enhance the physical layer security by optimizing the minimum secrecy-rate among all user-pairs in the presence of a malicious user. The optimization problem is converted into an alternating optimization problem consisting of two sub-problems. Transmit power optimization is handled using a fractional programming method, whereas IRS phase shift optimization is handled with semi-definite programming. The convergence of the proposed algorithm is investigated numerically. The performance gain in minimum secrecy-rate is quantified for four different user configurations in comparison to the baseline scheme. Results indicate a 3.6-fold gain in minimum secrecy rate over the baseline scheme when the IRS is positioned near a legitimate user, even when the malicious …