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Muhammad Ayzed Mirza

Publications and source records attributed to Muhammad Ayzed Mirza.

2 recordsLinked to original sources

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

Carbon-aware decentralized dynamic task offloading in MIMO-MEC networks via multi-agent reinforcement learning

Massive internet of things microservices require integrating renewable energy harvesting into mobile edge computing (MEC) for sustainable eScience infrastructures. Spatiotemporal mismatches between stochastic task arrivals and intermittent green energy along with complex inter-user interference in multi-antenna (MIMO) uplinks complicate real-time resource management. Traditional centralized optimization and off-policy reinforcement learning struggle with scalability and signaling overhead in dense networks. This paper proposes CADDTO-PPO, a carbon-aware decentralized dynamic task offloading framework based on multi-agent proximal policy optimization. The multi-user MIMO-MEC system is modeled as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP) to jointly minimize carbon emissions and buffer latency and energy wastage. A scalable architecture utilizes decentralized execution with parameter sharing (DEPS), which enables autonomous IoT agents to make fine-grained power control and offloading decisions based solely on local observations. Additionally, a carbon-first reward structure adaptively prioritizes green time slots for data transmission to decouple system throughput from grid-dependent carbon footprints. Finally, experimental results demonstrate CADDTO-PPO outperforms deep deterministic policy gradient (DDPG) and lyapunov-based baselines. The framework achieves the lowest carbon intensity and maintains near-zero packet overflow rates under extreme traffic loads. Architectural profiling validates the framework to demonstrate a constant $O(1)$ inference complexity and theoretical lightweight feasibility for future generation sustainable IoT deployments.

cs.DC