A Fast-CSMA Algorithm for Deadline-Constrained Scheduling over Wireless Fading Channels

Recently, low-complexity and distributed Carrier Sense Multiple Access (CSMA)-based scheduling algorithms have attracted extensive interest due to their throughput-optimal characteristics in general network topologies. However, these algorithms are not well-suited for serving real-time traffic under time-varying channel conditions for two reasons: (1) the mixing time of the underlying CSMA Markov Chain grows with the size of the network, which, for large networks, generates unacceptable delay for deadline-constrained traffic; (2) since the dynamic CSMA parameters are influenced by the arrival and channel state processes, the underlying CSMA Markov Chain may not converge to a steady-state under strict deadline constraints and fading channel conditions. In this paper, we attack the problem of distributed scheduling for serving real-time traffic over time-varying channels. Specifically, we consider fully-connected topologies with independently fading channels (which can model cellular networks) in which flows with short-term deadline constraints and long-term drop rate requirements are served. To that end, we first characterize the maximal set of satisfiable arrival processes for this system and, then, propose a Fast-CSMA (FCSMA) policy that is shown to be optimal in supporting any real-time traffic that is within the maximal satisfiable set. These theoretical results are further validated through simulations to demonstrate the relative efficiency of the FCSMA policy compared to some of the existing CSMA-based algorithms.Comment: This work appears in workshop on Resource Allocation and Cooperation in Wireless Networks (RAWNET), Princeton, NJ, May, 201

Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines

We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.Comment: 11 pages, 13 figure