Harsh Raj

Abstract: This paper analyzes the performance of the non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) system over Rician fading channels. We consider a transceiver at the base station communicating with two downlink (DL) and uplink (UL) users while simultaneously performing mono-static target sensing in the presence of co-channel interferences. We investigate the communication performance of this system by analytically deriving the closed-form expressions of outage probabilities of near and far users in the DL and UL scenarios. Moreover, we utilize Jensen’s inequality and first-order Taylor series approximation to obtain the ergodic rates of the DL and UL communication users (CUs). As a special case, we also derive closed-form expressions of the ergodic rates in the presence and absence of a target for the Rayleigh fading scenario. Furthermore, we analyze the performance of target sensing by deriving the probability of detection and false alarm. To ensure uniform quality-of-service to the near and far CUs in the UL, we have designed a communication-centric max-min power allocation optimization problem under a radar service constraint. We analyze the performance of the proposed system model in terms of outage probabilities, ergodic rates, target detection probability, and receiver operating characteristics under both Rayleigh and Rician fading scenarios, considering the impact of interference between communication and sensing functionalities. Moreover, we compare the obtained results with those of the orthogonal multiple access (OMA)-based scheme. All derived results have been validated through Monte-Carlo simulations.

Abstract: This paper investigates the intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) framework employing a non-orthogonal multiple access (NOMA) transmission scheme. In the proposed system, an ISAC transceiver employing NOMA transmits information to the down-link near and far users in the presence of the radar target and clutters, with the IRS assisting the far user. The ISAC transceiver communicates with the users while simultaneously performing mono-static sensing to detect the target. For the presented system, a new exact channel statistic for the cascaded Rician-Rayeligh link via IRS is derived using the Laplace transform to address the central limit theorem (CLT) approximation limitation in a low channel gain scenario. Thus, it enables an accurate derivation of the closed-form analytical expression for the outage probability of the system in a high signal-to-noise ratio (SNR) regime, where the optimal phase shifts at the IRS have been employed to improve the received signal at the far user. We have shown that when random phase shifts are employed at the IRS, CLT approximation is satisfied to provide accurate outage probability in all SNR regimes. Our derived analysis thoroughly investigates considering the interference caused by sensing echoes and clutters to the communication users. Further, we have derived the probability of detection of the radar target by employing the Neyman-Pearson criterion and analyzed the receiver operating characteristics for target detection in the presence of clutters. Numerical results demonstrate the proposed analytical framework through Monte-Carlo simulations for various system parameters, and comparative performance analysis reveals that the NOMA-assisted ISAC outperforms orthogonal multiple-access systems.

Abstract: Machine learning (ML), a subfield of artificial intelligence, holds immense applications across various domains owing to its potential to analyze vast datasets and discern intricate patterns. By leveraging algorithms and statistical models, ML facilitates the extraction of actionable insights and predicts future outcomes based on historical data. This predictive capability underpins a multitude of applications, spanning computer vision, natural language processing, finance, healthcare, transportation, wireless communications, and beyond. Through sophisticated algorithms, such as neural networks (NNs), ML empowers systems to autonomously adapt and refine their behavior based on experience, driving innovation and efficiency in diverse fields. Consequently, ML is a cornerstone of modern technological advancements, offering unparalleled opportunities for data-driven decision-making, process optimization, and transformative breakthroughs. ML has been successful and is envisioned to play a pivotal role in revolutionizing wireless communication systems by offering innovative solutions to enhance system performance and efficiency, reliability, and security. With the proliferation of wireless devices and the exponential growth in data traffic, conventional communication system techniques face unprecedented challenges, such as managing network resources, complex wireless networks, unknown physical and signal models, mitigating interferences, and ensuring seamless connectivity.

Abstract: This paper presents the index modulation, that is, the space-shift keying (SSK) and reflection phase modulation (RPM) schemes for intelligent reflecting surface (IRS)-assisted wireless network. IRS simultaneously reflects the incoming information signal from the base station and explicitly encodes the local information bits in the reflection phase shift of IRS elements. The phase shift of the IRS elements is employed according to local data from the RPM constellation. A joint detection using a maximum-likelihood (ML) decoder is performed for the SSK and RPM symbols over a realistic fading scenario modeled as the Rician fading channel. The pairwise error probability over Rician fading channels is derived and utilized to determine the average bit error rate. In addition, the ergodic capacity of the presented system is derived. The derived analytical results are verified and are in exact agreement with Monte-Carlo simulations.