Topic
Channel allocation schemes
About: Channel allocation schemes is a research topic. Over the lifetime, 10656 publications have been published within this topic receiving 182117 citations.
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27 Jun 2005
TL;DR: This work presents a high-level framework for resource-distortion optimization on point-to-point coding and delivery schemes in which the sequences are encoded on the fly, and highlights recent advances in optimal resource allocation for real-time video communications over unreliable and resource constrained communication channels.
Abstract: Multimedia applications involving the transmission of video over communication networks are rapidly increasing in popularity. Such applications can greatly benefit from adapting video coding parameters to network conditions as well as adapting network parameters to better support the application requirements. These two dimensions can both be viewed as allocating source and network resources to improve video quality. We highlight recent advances in optimal resource allocation for real-time video communications over unreliable and resource constrained communication channels. More specifically, we focus on point-to-point coding and delivery schemes in which the sequences are encoded on the fly. We present a high-level framework for resource-distortion optimization. The framework can be used for jointly considering factors across network layers, including source coding, channel resource allocation, and error concealment. For example, resources can take the form of transmission energy in a wireless channel, and transmission cost in a DiffServ-based Internet channel. This framework can be used to optimally trade off resource consumption with end-to-end video quality in packet-based video transmission. After giving an overview of this framework, we review recent work in two areas-energy efficient wireless video transmission and resource allocation for Internet-based applications.
93 citations
10 Jun 2012
TL;DR: This paper forms the energy allocation problem as a sequential decision problem and proposes an optimal energy allocation (OEA) algorithm using dynamic programming and conducts simulations to compare the performance between the proposed OEA algorithm and the channel-aware energy allocation algorithm from [1].
Abstract: With the use of energy harvesting technologies, the lifetime of a wireless sensor network (WSN) can be prolonged significantly. Unlike a traditional WSN powered by non-rechargeable batteries, the energy management policy of an energy harvesting WSN needs to take into account the energy replenishment process. In this paper, we study the energy allocation for sensing and transmission in an energy harvesting sensor node with a rechargeable battery and a finite data buffer. The sensor node aims to maximize the total throughput in a finite horizon subject to time-varying energy harvesting rate, energy availability in the battery, and channel fading. We formulate the energy allocation problem as a sequential decision problem and propose an optimal energy allocation (OEA) algorithm using dynamic programming. We conduct simulations to compare the performance between our proposed OEA algorithm and the channel-aware energy allocation (CAEA) algorithm from [1]. Simulation results show that the OEA algorithm achieves a higher throughput than the CAEA algorithm under different settings.
93 citations
TL;DR: This paper models and evaluates the throughput that can be achieved in a system where nodes compete for bandwidth using a generalized version of slotted-Aloha protocols and reveals that under heavy loads, a greedy strategy reduces the utilization, and that attackers cannot do much better than attacking during randomly selected slots.
Abstract: Aloha and its slotted variation are commonly deployed medium access control (MAC) protocols in environments where multiple transmitting devices compete for a medium, yet may have difficulty sensing each other's presence (the "hidden terminal problem''). Competing 802.11 gateways, as well as most modern digital cellular systems, like GSM, are examples. This paper models and evaluates the throughput that can be achieved in a system where nodes compete for bandwidth using a generalized version of slotted-Aloha protocols. The protocol is implemented as a two-state system, where the probability that a node transmits in a given slot depends on whether the node's prior transmission attempt was successful. Using Markov models, we evaluate the channel utilization and fairness of this class of protocols for a variety of node objectives, including maximizing aggregate throughput of the channel, each node selfishly maximizing its own throughput, and attacker nodes attempting to jam the channel. If all nodes are selfish and strategically attempt to maximize their own throughput, a situation similar to the traditional Prisoner's Dilemma arises. Our results reveal that under heavy loads, a greedy strategy reduces the utilization, and that attackers cannot do much better than attacking during randomly selected slots.
93 citations
TL;DR: A negotiation-based throughput maximization algorithm which adjusts the operating channel and power level among access points automatically, from a game-theoretical perspective is presented and it is shown that this algorithm converges to the optimal channel andPower assignment which yields the maximum overall throughput with arbitrarily high probability.
Abstract: This paper addresses the throughput maximization problem in wireless mesh networks. For the case of cooperative access points, we present a negotiation-based throughput maximization algorithm which adjusts the operating channel and power level among access points automatically, from a game-theoretical perspective. We show that this algorithm converges to the optimal channel and power assignment which yields the maximum overall throughput with arbitrarily high probability. Moreover, we analyze the scenario where access points belong to different regulation entities and hence non-cooperative. The long- term behavior and corresponding performance are investigated and the analytical results are verified by simulations.
92 citations
TL;DR: Simulation results show that the DRL-DCA algorithm can decrease the blocking probability and improve the carried traffic and spectrum efficiency compared with other channel allocation algorithms.
Abstract: Dynamic channel allocation (DCA) is the key technology to efficiently utilize the spectrum resources and decrease the co-channel interference for multibeam satellite systems. Most works allocate the channel on the basis of the beam traffic load or the user terminal distribution of the current moment. These greedy-like algorithms neglect the intrinsic temporal correlation among the sequential channel allocation decisions, resulting in the spectrum resources underutilization. To solve this problem, a novel deep reinforcement learning (DRL)-based DCA (DRL-DCA) algorithm is proposed. Specifically, the DCA optimization problem, which aims at minimizing the service blocking probability, is formulated in the multibeam satellite systems. Due to the temporal correlation property, the DCA optimization problem is modeled as the Markov decision process (MDP) which is the dominant analytical approach in DRL. In modeled MDP, the system state is reformulated into an image-like fashion, and then, convolutional neural network is used to extract useful features. Simulation results show that the DRL-DCA algorithm can decrease the blocking probability and improve the carried traffic and spectrum efficiency compared with other channel allocation algorithms.
92 citations