Multiple access (MA) design is investigated to facilitate pinching-antenna systems (PASS)-based multi-user communications. By exploiting the newly introduced waveguide domain and existing frequency domain, two MA schemes are explored, namely pure waveguide division multiple access (WDMA) and hybrid WDMA. For each MA scheme, the corresponding resource allocation problem is formulated to maximize the rate fairness via the joint optimization of pinching beamforming and power allocation. For both schemes, a majorization-minimization (MM)-based alternating optimization (AO) algorithm is proposed that alternately optimizes pinching beamforming and transmit power. A low-complexity framework is further developed, including a two-stage pinching beamforming design and successive convex approximation (SCA)-based power allocation. Numerical results demonstrate that: 1) PASS significantly improve communication rate performance over conventional antenna systems; 2) The proposed MM-based AO algorithm provides higher performance at the cost of increased complexity, while the low-complexity framework achieves comparable performance with lower computational complexity; and 3) Pure WDMA achieves better performance compared to hybrid WDMA, efficiently supporting multi-user communications enabled by pinching beamforming.
Qiao Ren, Xi-Dong Mu, Siyu Lin et al.· IEEE Transactions on Wireles...· 0 citations
As computing demands continue to grow, a single server is no longer sufficient to meet user requirements, leading to increasing interest in multiserver collaborative edge computing. However, load imbalance is a prevalent issue in multiserver edge computing systems, resulting in inefficient resource utilization and degraded service quality. To address this issue, a multiserver collaborative edge computing architecture is established, and a joint optimization problem is formulated to minimize task latency and energy consumption under latency constraints. Considering the dynamic nature of task arrivals and queue evolution, the problem is further modeled as a Markov Decision Process (MDP). To characterize more accurately the dynamic evolution of computation queue states in the MDP during task transmission, an arrival order-based queue state (AOBQS) model is introduced to capture the impact of transmission delay on task execution order. Furthermore, as transmission delay alters the task execution order in the computation queue and thus invalidates the system’s Markov property, the task waiting time and a virtual queue are introduced to reconstruct the queue state. Based on the reconstructed state representation, a queue-aware twin-delayed deep deterministic policy gradient (QATD3) algorithm is developed to solve the task scheduling and resource allocation problem, thereby achieving load balancing in multiserver collaborative edge computing systems. Extensive simulation results demonstrate that the proposed method effectively achieves joint optimization of task latency and energy consumption, significantly improving overall system performance. Compared with baseline algorithms, the proposed QATD3 reduces average task delay by 24.53%, reduces normalized energy consumption by 16.06%, and improves average reward by 5.27%.
Jingzhe Wang, Si-yu Lin, Qingqing Pan et al.· IEEE Internet of Things Jour...· 0 citations
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