arXiv subjects

Asim Ihsan

Publications and source records attributed to Asim Ihsan.

At least 19 recordsLinked to original sources

arXiv2026

Exploiting Movable-Element STARS for Rate Splitting Multiple Access

This paper investigates a movable-element simultaneously transmitting and reflecting reconfigurable intelligent surface (ME-STARS) assisted rate-splitting multiple access (RSMA) system under imperfect channel state information (CSI). Unlike conventional STARS with fixed element positions, the elements of ME-STARS can be repositioned within a predefined region, providing additional spatial degrees of freedom for improving the cascaded transmitter--STARS--user channels. To exploit this flexibility while accounting for CSI uncertainty, we formulate a robust sum-rate maximization problem that jointly optimizes the transmit beamforming, common-rate allocation, reflection and transmission coefficients, and ME-STARS element positions, subject to transmit-power, user-rate, minimum inter-element spacing, and movement-region constraints. The resulting problem is highly non-convex due to the strong coupling among the design variables and the position-dependent channels. To address this challenge, an iterative optimization framework is developed in which the transmit beamforming, STARS coefficients, and element positions are successively optimized through tractable convex reformulations. In particular, the element positions are updated sequentially using a majorization--minimization (MM) framework, where quadratic surrogate functions are constructed from the first- and second-order derivatives of the position-dependent channels while preserving the minimum inter-element spacing constraint. Simulation results demonstrate that the proposed ME-STARS design consistently outperforms the considered benchmark schemes. Moreover, the performance gains remain significant under increasing CSI uncertainty, highlighting the effectiveness of element repositioning for robust RSMA transmission.

eess.SP↗

arXiv2026

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.

eess.SP↗

arXiv2026

Adaptive UAV Communications for URLLC: From Preplanned Designs to Real-Time Intelligence

Unmanned aerial vehicles (UAVs) are emerging as a key enabler of next-generation wireless networks, particularly for applications that require ultra-reliable and low-latency communication (URLLC), such as emergency response, industrial automation, and autonomous systems. In these scenarios, maintaining reliable connectivity under strict transmission time constraints is challenging due to dynamic environments, mobility, and limited onboard energy. In particular, communication performance and energy are closely coupled with UAV movement, making trajectory design a critical component of system operation. Most existing approaches rely on offline joint communication and trajectory optimization, where the UAV trajectory and communication parameters are optimized prior to execution based on assumed system information. Although effective under ideal assumptions, such designs cannot adapt to real-time variations in user demand, channel conditions, or environmental disturbances, which are particularly critical in URLLC settings. To address these challenges, this article investigates model predictive control (MPC) as an adaptive framework for UAV-enabled communications. Using a receding-horizon strategy, MPC enables the UAV to continuously update its trajectory based on real-time information, improving reliability and robustness in dynamic environments. Representative application scenarios are discussed to highlight the role of MPC in UAV-enabled URLLC systems. Furthermore, a case study is presented to illustrate key design trade-offs and performance insights under finite blocklength-based URLLC transmission, followed by a discussion on open challenges and future research directions for practical and scalable MPC-enabled UAV communication systems.

eess.SP↗

arXiv2026

Channel Uncertainty-Aware Robust Beamforming for RIS-Assisted RSMA Communication With Movable Antennas

This work investigates a robust resource allocation framework for a downlink multi-user communication system integrating movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) under the rate-splitting multiple access (RSMA) transmission protocol. Unlike conventional fixed-position antenna architectures, the considered MAs-enabled system introduces spatially adaptive channel variations in which antenna positions directly influence the effective channel responses. Consequently, under imperfect channel state information (CSI), the impact of CSI uncertainty propagates not only through active and passive beamforming design, but also through the antenna position optimization process, leading to a highly coupled robust optimization problem. To address this challenge, we formulate a system sum-rate maximization problem by jointly optimizing the transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MAs positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. The resulting non-convex problem is efficiently handled through an iterative robust optimization framework, where the original problem is successively decomposed into active beamforming, RIS reflection matrix, and MAs position optimization subproblems, and tractable convex surrogate functions are constructed to enable iterative optimization. Moreover, system robustness is ensured by incorporating a bounded CSI uncertainty model that explicitly captures channel estimation errors and guarantees reliable communication performance under worst-case channel conditions. Finally, extensive simulation results demonstrate that the proposed framework achieves significant performance gains and enhanced robustness compared with benchmark schemes, while also exhibiting fast and stable convergence behavior under practical imperfect CSI conditions.

eess.SP↗

arXiv2026

Online Model Predictive Control for Trajectory and Beamforming Optimization in UAV-Enabled URLLC

This paper investigates joint trajectory and active beamforming design for unmanned aerial vehicle (UAV)-enabled ultra-reliable low-latency communication (URLLC) systems under finite blocklength (FBL) transmission. Unlike conventional Shannon-capacity formulations, the FBL regime introduces a signal-to-interference-plus-noise ratio (SINR)-dependent dispersion penalty that increases the sensitivity of reliability to mobility-induced channel variations. To address this challenge, we develop a propulsion-aware model predictive control (MPC) framework that performs receding-horizon joint trajectory and multi-user beamforming optimization while enforcing FBL-based rate constraints. The resulting long-horizon nonconvex problem is decomposed into beamforming and trajectory subproblems using alternating optimization. Concave surrogate is constructed for the Shannon-capacity term, while convex approximations are derived for the dispersion term and the nonlinear propulsion power model, yielding tractable convex subproblems solved iteratively. Compared with an offline MPC baseline, where the predictive problem is solved once over the entire mission horizon without feedback updates, and a conventional offline trajectory-beamforming optimization, the proposed closed-loop framework achieves disturbance-resilient mission completion under UAV position disturbances. Simulation results show that, compared with maximum ratio transmission (MRT) and equal-power allocation, the proposed interference-aware design significantly improves URLLC reliability under stringent minimum rate constraints. The results also quantify the impact of antenna scaling, transmit power, and transmission time on FBL performance, providing insights for reliability-centric UAV-enabled wireless networks in 5G and beyond.

eess.SP↗

arXiv2025

Robust Beamforming Optimization for STAR-RIS Empowered Multi-User RSMA Under Hardware Imperfections and Channel Uncertainty

This study explores the synergy between rate-splitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to enable ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while improving spectral and energy efficiency. Specifically, we investigate a STAR-RIS-assisted multi-user RSMA system and develop an intelligent optimization strategy that jointly designs the transmitter's active beamforming, the common stream rate allocation, and the passive beamforming vectors for the STAR-RIS transmission and reflection regions, considering transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured via a bounded channel estimation error model that captures CSI imperfections and guarantees resilience against worst-case errors. To address the highly non-convex problem, we propose an iterative optimization algorithm that decomposes it into two sub-problems. Firstly, active beamforming vectors for the common and private signals are determined by reformulating the original problem into a convex semi-definite programming (SDP) form using successive convex approximation (SCA) and semi-definite relaxation (SDR). Secondly, passive beamforming vectors are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Moreover, when higher-rank solutions arise, Gaussian randomization is applied to obtain rank-one solutions. Numerical simulations demonstrate that the proposed strategy achieves significant performance gains over benchmark schemes and exhibits fast convergence.

eess.SP↗

arXiv2025

Energy-Efficient Precoding for Dense VCSEL-Based OWC Systems Under a Cooperative Broadcast Model

As 6G and beyond aim for sustainable, high-capacity wireless connectivity, optical wireless communication (OWC) has emerged as a compelling solution.Recent advances in vertical-cavity surface-emitting laser (VCSEL) arrays have significantly enhanced OWC performance, enabling high-speed, low-power data transmission. However, dense VCSEL deployments introduce challenges related to interference and energy efficiency (EE). This paper proposes a scalable precoding framework for EE maximization in fully cooperative VCSEL-based OWC broadcast systems. We formulate a non-convex optimization problem to design the precoding matrix under practical optical constraints while guaranteeing minimum user rates. To solve this, we apply Dinkelbach's method to handle the fractional objective and the inner approximation technique to iteratively convexify and solve the problem. Simulation results show that our approach consistently outperforms regularized zero-forcing in terms of EE, particularly in large-scale deployments, demonstrating its potential for next-generation sustainable dense OWC networks.

physics.optics↗

arXiv2025

Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy-efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of BD-RIS node, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model under energy harvesting constraints. We design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. Further, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized iteratively. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming through successive-convex approximation (SCA), whereas the power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the Conjugate-Gradient method. Finally, numerical simulation results reveal that the proposed scheme provides significant performance improvements over existing benchmarks and demonstrates rapid convergence within a reasonable number of iterations.

eess.SP↗

arXiv2025

Optical Wireless Communications: Enabling the Next Generation Network of Networks

Optical wireless communication (OWC) is a promising technology anticipated to play a key role in the next-generation network of networks, especially as a complementary technology to traditional radio frequency communications, for enhancing networking capabilities beyond conventional terrestrial networks. OWC is already a mature technology with diverse usage scenarios, and can enable integrated applications via wireless access and backhaul networks, dynamic drone and satellite networks, underwater networks, inter- and intra-system interconnecting networks, and vehicular communication networks. Furthermore, novel and emerging technological opportunities such as photovoltaic cells, orbital angular momentum-based modulation, optical reconfigurable intelligent surfaces, organic light-emitting and photo diodes, and recent advances in ultraviolet communications can help enhance future OWC capabilities even further. Moreover, OWC networks can also support value-added services such as enhanced positioning and gesture recognition. Hence, OWC provides unique functionalities that can play a crucial role in building convergent and resilient future network of networks alongside radio frequency and optical fiber technologies.

eess.SP↗

arXiv2025

Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks

In this paper, we propose an innovative predict-and-optimize algorithm designed for hybrid WiFi/LiFi networks, aiming to achieve service differentiation while maximizing energy efficiency (EE). The proposed framework utilizes multi-access technology real-time intelligent controller (mATRIC) to dynamically predict the appropriate network slice for each user based on historically monitored key performance indicators (KPIs). This prediction is facilitated by a deep learning model trained using the resilient backpropagation algorithm, with training conducted on KPIs data at the universal non-real time RAN intelligent controller (non-RT RIC). This trained model enables real-time slice selection by mATRIC. In the subsequent phase, the algorithm focuses on optimizing EE of hybrid network as a function of precoding vectors for the predicted slices by employing techniques from sequential convex approximation and the inner approximation method. We introduce novel approximations to convert non-convex objective functions and constraints into convex forms, and develop an iterative algorithm to achieve sub-optimal solutions. Additionally, the EE maximization problem, ensures alignment with end-to-end latency requirements. It also addresses the various constraints inherent to hybrid systems, such as input signal limitations for LiFi LEDs, data rate restrictions, and power budget considerations. Simulation results validate the effectiveness of the proposed algorithm, demonstrating significant improvements in EE while ensuring service differentiation within hybrid network environments.

math.OC↗

arXiv2023

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KKT conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. Numerical results demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

cs.IT↗

arXiv2022

Energy-Efficient Beamforming and Resource Optimization for AmBSC-Assisted Cooperative NOMA IoT Networks

In this manuscript, we present an energy-efficient alternating optimization framework based on the multi-antenna ambient backscatter communication (AmBSC) assisted cooperative non-orthogonal multiple access (NOMA) for next-generation (NG) internet-of-things (IoT) enabled communication networks. Specifically, the energy-efficiency maximization is achieved for the considered AmBSC-enabled multi-cluster cooperative IoT NOMA system by optimizing the active-beamforming vector and power-allocation coefficients (PAC) of IoT NOMA users at the transmitter, as well as passive-beamforming vector at the multi-antenna assisted backscatter node. Usually, increasing the number of IoT NOMA users in each cluster results in inter-cluster interference (ICI) (among different clusters) and intra-cluster interference (among IoT NOMA users). To combat the impact of ICI, we exploit a zero-forcing (ZF) based active-beamforming, as well as an efficient clustering technique at the source node. Further, the effect of intra-cluster interference is mitigated by exploiting an efficient power-allocation policy that determines the PAC of IoT NOMA users under the quality-of-service (QoS), cooperation, SIC decoding, and power-budget constraints. Moreover, the considered non-convex passive-beamforming problem is transformed into a standard semi-definite programming (SDP) problem by exploiting the successive-convex approximation (SCA) approximation, as well as the difference of convex (DC) programming, where Rank-1 solution of passive-beamforming is obtained based on the penalty-based method. Furthermore, the numerical analysis of simulation results demonstrates that the proposed energy-efficiency maximization algorithm exhibits an efficient performance by achieving convergence within only a few iterations.

cs.IT↗

arXiv2022

Energy-Efficient Backscatter-Assisted Coded Cooperative-NOMA for B5G Wireless Communications

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations.

cs.IT↗

arXiv2022

Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano's formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

eess.SP↗

arXiv2022

LSTM-Based Distributed Conditional Generative Adversarial Network For Data-Driven 5G-Enabled Maritime UAV Communications

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

eess.SP↗

arXiv2022

Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications

This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications. Specifically, this work proposes a centralized IRS-enabled design for the NOMA-BF system to optimize the active beamforming and power allocation coefficient (PAC) of users at the transmitter in the first stage and passive beamforming at IRS in the 2nd stage to maximize the energy efficiency (EE) of the network. However, an increment in the number of supportable users with the NOMA-BF system will lead to NOMA user interference and inter-cluster interference (ICI). To mitigate the effect of ICI, first zero-forcing beamforming along with efficient user clustering algorithm is exploited and then NOMA user interference is tackled efficiently through a proposed iterative algorithm that computes PAC of NOMA user through simplified closed-form expression under the required system constraints. In the 2nd stage, the problem of passive beamforming is solved through a technique based on difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results demonstrate that the proposed alternating framework for energy-efficient IRS-assisted NOMA-BF system can achieve convergence within a few iterations and provide efficient performance in terms of EE of the system with low complexity.

eess.SP↗

arXiv2022

NOMA-enabled Backscatter Communications for Green Transportation in Automotive-Industry 5.0

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other Intelligent Transportation System (ITS) entities. Non-Orthogonal Multiple Access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this paper, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multi-cell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

eess.SP↗

arXiv2021

Joint Optimization for Secure Ambient Backscatter Communication in NOMA-enabled IoT Networks

Non-orthogonal multiple access (NOMA) has emerged as a novel air interface technology for massive connectivity in sixth-generation (6G) era. The recent integration of NOMA in backscatter communication (BC) has triggered significant research interest due to its applications in low-powered Internet of Things (IoT) networks. However, the link security aspect of these networks has not been well investigated. This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system. Our system model takes into account the simultaneous operation of NOMA IoT users and the backscatter node (BN) in the presence of multiple eavesdroppers (EDs). The EDs in the surrounding area can overhear the communication of base station (BS) and BN due to the wireless broadcast transmission. Thus, the main objective is to enhance the link security by optimizing the BN reflection coefficient and BS transmit power. To gauge the performance of the proposed scheme, we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes. Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without BC and conventional orthogonal multiple access scheme in terms of system secrecy rate.

eess.SP↗