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Syed Tariq Shah

Publications and source records attributed to Syed Tariq Shah.

10 recordsLinked to original sources

Robust Transmission Design for RIS-Assisted RSMA-SWIPT Systems With Movable Antennas Under Hardware Distortions

This paper investigates a robust transmission design for a multi-user rate-splitting multiple access (RSMA)-based simultaneous wireless information and power transfer (SWIPT) system empowered by movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under channel state information (CSI) uncertainty and residual hardware impairments (HIs). The effective channels in MAs-enabled systems depend on antenna positions, causing CSI uncertainty to affect not only active and passive beamforming but also antenna position optimization. Furthermore, residual HIs distort the effective SINRs, creating additional coupling among beamforming, RIS reflection control, common-rate allocation, power-splitting ratio optimization, and antenna position optimization. Consequently, the joint impact of CSI uncertainty and HIs leads to a highly coupled and challenging resource allocation problem. To address this challenge, we propose a robust resource allocation framework that jointly optimizes common-rate allocation, transmit beamforming, RIS reflection coefficients, power-splitting ratios, and MAs positions to maximize the achievable sum-rate while satisfying practical system constraints. To obtain an efficient solution, the original problem is decomposed into active beamforming, RIS reflection design, power-splitting ratio optimization, and MAs position optimization subproblems, where tractable convex surrogate functions are constructed to handle the non-convex objective and constraints. Simulation results verify the effectiveness of the proposed framework and demonstrate substantial improvements in achievable sum-rate, robustness against CSI uncertainty and hardware impairments, and convergence performance compared with benchmark schemes.

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A Survey on STAR-RIS Enabled Joint Communications and Sensing: Fundamentals, Recent Advances and Research Challenges

The joint communications and sensing (JCAS) paradigm is envisioned as a core capability of sixth-generation (6G) wireless networks, enabling the integration of data communication and environmental sensing within a unified system. By reusing spectrum, waveforms, and hardware resources, JCAS improves spectral efficiency, reduces system complexity, and hardware cost, while enabling new use cases. Nevertheless, the realization of JCAS is hindered by inherent trade-offs between communication and sensing objectives, limited controllability of wireless propagation, and stringent hardware and design constraints. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have recently emerged as a promising technology to address these challenges by enabling full-space programmable manipulation of electromagnetic waves. This survey provides a systematic and in-depth review of STAR-RIS-enabled JCAS systems. Specifically, we first introduce the fundamental principles of JCAS and STAR-RIS. We then classify and review the state-of-the-art research on STAR-RIS-assisted JCAS from multiple perspectives, encompassing system architectures, waveform and beamforming design, resource allocation, optimization frameworks, and learning-based control. Finally, we identify key open challenges that remain unsolved and outline promising future research directions toward intelligent, flexible, and perceptive 6G wireless networks.

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Reconfigurable Intelligent Surface-Assisted Cross-Layer Authentication for Secure and Efficient Vehicular Communications

Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC; Pd: detection vs. PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64 x 64 reflective meta-surfaces. Experimental results show a significant improvement in Pd, increasing from 0.82 to 0.96 at SNR = -6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme's ability to resist passive and active attacks.

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Survey on Beyond Diagonal RIS Enabled 6G Wireless Networks: Fundamentals, Recent Advances, and Challenges

Beyond Diagonal Reconfigurable Intelligent Surfaces (BD-RIS) represent a groundbreaking innovation in sixth-generation (6G) wireless networks, enabling unprecedented control over wireless propagation environments compared to conventional diagonal RIS (D-RIS). This survey provides a comprehensive analysis of BD-RIS, detailing its architectures, operational principles, and mathematical modeling while highlighting its performance benefits. BD-RIS classifications, including single-connected, fully-connected, and group-connected architectures, and their reflective, transmissive, hybrid, and multi-sector operating modes are examined. Recent advances in BD-RIS-enabled 6G networks are reviewed, focusing on critical areas such as channel estimation, sum-rate and spectral efficiency optimization, energy efficiency enhancement, and security. The survey identifies fundamental challenges in BD-RIS research, including hardware design limitations, adaptive channel estimation, and the impact of non-ideal hardware effects. Future research directions for BD-RIS are proposed, emphasizing the integration of artificial intelligence and machine learning (AI/ML), joint optimization of communication and sensing, and enhanced physical layer security (PLS). This study concludes by underscoring BD-RIS's transformative potential to redefine 6G wireless networks, offering valuable insights and lessons for future research and development.

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Harnessing DRL for URLLC in Open RAN: A Trade-off Exploration

The advent of Ultra-Reliable Low Latency Communication (URLLC) alongside the emergence of Open RAN (ORAN) architectures presents unprecedented challenges and opportunities in Radio Resource Management (RRM) for next-generation communication systems. This paper presents a comprehensive trade-off analysis of Deep Reinforcement Learning (DRL) approaches designed to enhance URLLC performance within ORAN's flexible and dynamic framework. By investigating various DRL strategies for optimising RRM parameters, we explore the intricate balance between reliability, latency, and the newfound adaptability afforded by ORAN principles. Through extensive simulation results, our study compares the efficacy of different DRL models in achieving URLLC objectives in an ORAN context, highlighting the potential of DRL to navigate the complexities introduced by ORAN. The proposed study provides valuable insights into the practical implementation of DRL-based RRM solutions in ORAN-enabled wireless networks. It sheds light on the benefits and challenges of integrating DRL and ORAN for URLLC enhancements. Our findings contribute to the ongoing discourse on advancements in URLLC and ORAN, offering a roadmap for future research to pursue efficient, reliable, and flexible communication systems.

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Towards Resilient 6G O-RAN: An Energy-Efficient URLLC Resource Allocation Framework

The demands of ultra-reliable low-latency communication (URLLC) in ``NextG" cellular networks necessitate innovative approaches for efficient resource utilisation. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimisation individually, often neglecting the intricate balance required to optimise both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximise energy efficiency (EE) while minimising URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks.

cs.NI

CR-Enabled NOMA Integrated Non-Terrestrial IoT Networks with Transmissive RIS

This work proposes a T-RIS-equipped LEO satellite communication in cognitive radio-enabled integrated NTNs. In the proposed system, a GEO satellite operates as a primary network, and a T-RIS-equipped LEO satellite operates as a secondary IoT network. The objective is to maximize the sum rate of T-RIS-equipped LEO satellite communication using downlink NOMA while ensuring the service quality of GEO cellular users. Our framework simultaneously optimizes the total transmit power of LEO, NOMA power allocation for LEO IoT (LIoT) and T-RIS phase shift design subject to the service quality of LIoT and interference temperature to the primary GEO network. To solve the non-convex sum rate maximization problem, we first adopt successive convex approximations to reduce the complexity of the formulated optimization. Then, we divide the problem into two parts, i.e., power allocation of LEO and phase shift design of T-RIS. The power allocation problem is solved using KKT conditions, while the phase shift problem is handled by Taylor approximation and semidefinite programming. Numerical results are provided to validate the proposed optimization framework.

cs.ET

DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN

This work addresses resource allocation challenges in multi-cell wireless systems catering to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) users. We present a distributed learning framework tailored to O-RAN network architectures. Leveraging a Thompson sampling-based Deep Reinforcement Learning (DRL) algorithm, our approach provides real-time resource allocation decisions, aligning with evolving network structures. The proposed approach facilitates online decision-making for resource allocation by deploying trained execution agents at Near-Real Time Radio Access Network Intelligent Controllers (Near-RT RICs) located at network edges. Simulation results demonstrate the algorithm's effectiveness in meeting Quality of Service (QoS) requirements for both eMBB and URLLC users, offering insights into optimising resource utilisation in dynamic wireless environments.

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Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks

In the rapidly evolving landscape of 5G and beyond, cloud-native Open Radio Access Networks (O-RAN) present a paradigm shift towards intelligent, flexible, and sustainable network operations. This study addresses the intricate challenge of energy efficient (EE) resource allocation that services both enhanced Mobile Broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users. We propose a novel distributed learning framework leveraging on-policy and off-policy transfer learning strategies within a deep reinforcement learning (DRL)--based model to facilitate online resource allocation decisions under different channel conditions. The simulation results explain the efficacy of the proposed method, which rapidly adapts to dynamic network states, thereby achieving a green resource allocation.

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Proportional Fair Scheduling Using Water-Filling Technique for SC-FDMA Based D2D Communication

The resource allocation in SC-FDMA is constrained by the condition that multiple subchannels should be allocated to a single user only if they are adjacent. Therefore, the scheduling scheme of a D2D-cellular system that uses SC-FDMA must also conform to the so-called adjacency constraint. This paper proposes a heuristic algorithm with low computational complexity that applies proportional fair (PF) scheduling in the D2D-cellular system. The proposed algorithm consists of two main phases: i) subchannel allocation and ii) adjustment of data rates, which are executed for both CUEs and DUEs. In the subchannel allocation phase for CUEs (or D2D pairs), the users' data rates are maximized via optimal power allocation to frequency-contiguous subchannels. In the second phase, a PF scheduling problem is solved to decide the modulation and coding scheme (MCS) of both CUEs and D2D pairs. Both phases of the proposed algorithm benefit from the Water-Filling (WF) technique. The simulation results suggest that the proposed scheme performs similarly to optimal PF scheduling from the perspective of users' data rate and their logarithmic sum. An additional benefit of the proposed scheme is its low computational overhead.

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