Some analysis results associated with the optimization problem for a discrete-time finite-buffer NT-policy queue

The prime objective of this paperis to give some analysis results concerning the discrete-time finite-buffer NT-policy queue, which can be utilized to determine the optimal threshold values. By recording the waiting time of the leading customer in server’s vacation period, the model is successfully described as a vector-valued Markov chain. Meanwhile, depending on the special block structure of the one-step transition probability matrix, the equilibrium queue length distribution is calculated through a more effective UL-type RG-factorization. Due to the number of customers served in the busy period does not have the structure of a Galton-Watson branching process, analysis of the regeneration cycle is regarded as a difficult problem in establishing the cost structure of the queueing system. However, employing the concept of i-busy period and some difference equation solving skills, the explicit expression for the expected length of the regeneration cycle is easily derived, and the stochastic decomposition structure of the busy period is also demonstrated. Finally, numerical results are offered to illustrate how the direct search method can be implemented to obtain the optimal management policy.This research was partially supported by grant from NSERC DAS programs, National Natural Science Foundation of China (Nos. 71301111,71171138, 71402072) and the FSUSE (No.2012RC23).http://link.springer.com/journal/123512017-07-30hb201

A Mean Field Approach for Optimization in Particles Systems and Applications

This paper investigates the limit behavior of Markov Decision Processes (MDPs) made of independent particles evolving in a common environment, when the number of particles goes to infinity. In the finite horizon case or with a discounted cost and an infinite horizon, we show that when the number of particles becomes large, the optimal cost of the system converges almost surely to the optimal cost of a discrete deterministic system (the ``optimal mean field''). Convergence also holds for optimal policies. We further provide insights on the speed of convergence by proving several central limits theorems for the cost and the state of the Markov decision process with explicit formulas for the variance of the limit Gaussian laws. Then, our framework is applied to a brokering problem in grid computing. The optimal policy for the limit deterministic system is computed explicitly. Several simulations with growing numbers of processors are reported. They compare the performance of the optimal policy of the limit system used in the finite case with classical policies (such as Join the Shortest Queue) by measuring its asymptotic gain as well as the threshold above which it starts outperforming classical policies

Cross-Layer Optimization of Network Performance over MIMO Wireless Mobile Channels

In the information theory, the channel capacity states the maximum amount of information which can be reliably transmitted over the communication channel. In the specific case of multiple-input multiple-output (MIMO) wireless systems, it is well recognized that the instantaneous capacity of MIMO systems is a random Gaussian process. Time variation of the capacity leads to the outages at instances when it falls below the transmission rate. The frequency of such events is known as outage probability. The cross-layer approach proposed in this work focuses on the effects of MIMO capacity outages on the network performance, providing a joint optimization of the MIMO communication system. For a constant rate transmission, the outage probability sensibly affects the amount of information correctly received at destination. Theoretically, the limit of the ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity variation. With an accurate channel state information, the capacity evolution can be predicted by a suitable autoregressive model based on the capacity time correlation. Taking into consideration the joint effects of channel outage at the physical layer and buffer overflow at the medium access control (MAC) layer, the optimal transmission strategy is derived analytically through the Markov decision processes (MDP) theory. The adaptive policy obtained by MDP is optimal and maximizes the amount of information correctly received at the destination MAC layer (throughput of the system). Analytical results demonstrate the significant improvements of the optimal variable rate strategy compared to a constant transmission rate strategy, in terms of both system throughput and probability of data loss

Reliable Transmission of Short Packets through Queues and Noisy Channels under Latency and Peak-Age Violation Guarantees

This work investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme---a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases---is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on large-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.Comment: To appear in IEEE journal on selected areas of communication (IEEE JSAC