Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Stochastic approximation with two time scales
Systems & Control Letters
Asynchronous Stochastic Approximations
SIAM Journal on Control and Optimization
Dynamic Control of a Queue with Adjustable Service Rate
Operations Research
Convex Optimization
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Distributive stochastic learning for delay-optimal OFDMA power and subband allocation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Cross-Layer Design for OFDMA Wireless Systems With Heterogeneous Delay Requirements
IEEE Transactions on Wireless Communications
Quality-of-Service Driven Power and Rate Adaptation over Wireless Links
IEEE Transactions on Wireless Communications
Optimal Downlink OFDMA Resource Allocation with Linear Complexity to Maximize Ergodic Rates
IEEE Transactions on Wireless Communications
Stability of N interacting queues in random-access systems
IEEE Transactions on Information Theory
Providing quality of service over a shared wireless link
IEEE Communications Magazine
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
A tutorial on decomposition methods for network utility maximization
IEEE Journal on Selected Areas in Communications
Non-Cooperative Resource Competition Game by Virtual Referee in Multi-Cell OFDMA Networks
IEEE Journal on Selected Areas in Communications
Distributive stochastic learning for delay-optimal OFDMA power and subband allocation
IEEE Transactions on Signal Processing
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In this paper, we consider the distributive queue-aware power and subband allocation design for a delay-optimal OFDMA uplink system with one base station, K users and NF independent subbands. Each mobile has an uplink queue with heterogeneous packet arrivals and delay requirements. We model the problem as an infinite horizon average reward Markov decision problem (MDP) where the control actions are functions of the instantaneous channel state information (CSI) as well as the joint queue state information (QSI). To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the subband allocation Q-factor by the sum of the per-user subband allocation Q-factor and derive a distributive online stochastic learning algorithm to estimate the per-user Q-factor and the Lagrange multipliers (LM) simultaneously and determine the control actions using an auction mechanism. We show that under the proposed auction mechanism, the distributive online learning converges almost surely (with probability 1). For illustration, we apply the proposed distributive stochastic learning framework to an application example with exponential packet size distribution. We show that the delay-optimal power control has the multilevel water-filling structure where the CSI determines the instantaneous power allocation and the QSI determines the water-level. The proposed algorithm has linear signaling overhead and computational complexity O(KNF), which is desirable from an implementation perspective.