Delay Constrained Scheduling over Fading Channels: Optimal Policies for Monomial Energy-Cost Functions

A point-to-point discrete-time scheduling problem of transmitting $B$ information bits within $T$ hard delay deadline slots is considered assuming that the underlying energy-bit cost function is a convex monomial. The scheduling objective is to minimize the expected energy expenditure while satisfying the deadline constraint based on information about the unserved bits, channel state/statistics, and the remaining time slots to the deadline. At each time slot, the scheduling decision is made without knowledge of future channel state, and thus there is a tension between serving many bits when the current channel is good versus leaving too many bits for the deadline. Under the assumption that no other packet is scheduled concurrently and no outage is allowed, we derive the optimal scheduling policy. Furthermore, we also investigate the dual problem of maximizing the number of transmitted bits over $T$ time slots when subject to an energy constraint.Comment: submitted to the IEEE ICC 200

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

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

Individual packet deadline delay constrained opportunistic scheduling for large multiuser systems

This work addresses opportunistic distributed multiuser scheduling in the presence of a fixed packet deadline delay constraint. A threshold-based scheduling scheme is proposed which uses the instantaneous channel gain and buffering time of the individual packets to schedule a group of users simultaneously in order to minimize the average system energy consumption while fulfilling the deadline delay constraint for every packet. The multiuser environment is modeled as a continuum of interference such that the optimization can be performed for each buffered packet separately by using a Markov chain where the states represent the waiting time of each buffered packet. We analyze the proposed scheme in the large user limit and demonstrate the delay-energy trade-off exhibited by the scheme. We show that the multiuser scheduling can be broken into a packet-based scheduling problem in the large user limit and the packet scheduling decisions are independent of the deadline delay distribution of the packets

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at http://www.mdpi.org/sensors/papers/s8074265.pd

Proactive Location-Based Scheduling of Delay-Constrained Traffic Over Fading Channels

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.Comment: Conference: VTC2016-Fall, At Montreal-Canad

Real-time and fault tolerance in distributed control software

Closed loop control systems typically contain multitude of spatially distributed sensors and actuators operated simultaneously. So those systems are parallel and distributed in their essence. But mapping this parallelism onto the given distributed hardware architecture, brings in some additional requirements: safe multithreading, optimal process allocation, real-time scheduling of bus and network resources. Nowadays, fault tolerance methods and fast even online reconfiguration are becoming increasingly important. All those often conflicting requirements, make design and implementation of real-time distributed control systems an extremely difficult task, that requires substantial knowledge in several areas of control and computer science. Although many design methods have been proposed so far, none of them had succeeded to cover all important aspects of the problem at hand. [1] Continuous increase of production in embedded market, makes a simple and natural design methodology for real-time systems needed more then ever