RED theory for quality of service provisioning in wireless communications

Abstract

In this paper, we study the performance limit of a wireless communication system over a fading channel. The system under study consists of 1) a finite‐buffer discrete‐time queueing system on the link layer; and 2) a rate‐adaptive channel coding system on the physical layer. The objective of this paper is to analyze the relationship among data rate (R), packet error probability (E), and delay bound (D) under the interaction between the link layer and the physical layer. In our analysis, we consider three types of packet errors; that is, 1) packet drop due to full buffer; 2) packet drop due to delay bound violation; and 3) packet decoding error due to channel noise. We obtain an upper bound on the packet error probability. Furthermore, by minimizing the packet error probability over the transmission rate, we obtain an optimal rate control policy that guarantees the user‐specified data rate and delay bound. In the case of constant arrival, the optimal rate control policy results in a rate‐error‐delay triplet; then, by varying data rate and delay bound, we obtain rate‐error‐delay Pareto‐optimal surface, which serves as the performance limit of the system under study. Copyright © 2012 John Wiley & Sons, Ltd.

Multi-Hop Quantum Communication Based on Greenberger-Horne-Zeilinger States and Teleportation

“…i , from (48)-(49), we have D < D , Therefore, our scheme has shorter delay for quantum communication, which has a great impact on improving the network throughput and reducing packet error rate efficiently [31], [32]. Besides, a 'quantum internet' in the future will allow various network services.…”

Analysis of minimum transmit sum power and scheduling power gain for multi‐user MIMO‐OFDM networks with rate constraints

“…Particularly in systems with limited battery power, such as wireless sensor networks, reducing power consumption is the first priority in the design of scheduling schemes. In such a context, recent studies are more focusing on minimizing transmit power while supporting the required information rates of users . Such a problem formulation can be applied in the analysis of the multi‐user communications systems considering quality of services (QoS) , electrical circuits power , or system energy efficiency .…”