The Experts below are selected from a list of 46287 Experts worldwide ranked by ideXlab platform
Demessie Girma - One of the best experts on this subject based on the ideXlab platform.
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a hopfield neural network based dynamic Channel Allocation with handoff Channel reservation control
IEEE Transactions on Vehicular Technology, 2000Co-Authors: Oscar Lazaro, Demessie GirmaAbstract:As Channel Allocation schemes become more complex and computationally demanding in cellular radio networks, alternative computational models that provide the means for faster processing time are becoming the topic of research interest. These computational models include knowledge-based algorithms, neural networks, and stochastic search techniques. This paper is concerned with the application of a Hopfield (1982) neural network (HNN) to dynamic Channel Allocation (DCA) and extends previous work that reports the performance of HNN in terms of new call blocking probability. We further model and examine the effect on performance of traffic mobility and the consequent intercell call handoff, which, under increasing load, can force call terminations with an adverse impact on the quality of service (QoS). To maintain the overall QoS, it is important that forced call terminations be kept to a minimum. For an HNN-based DCA, we have therefore modified the underlying model by formulating a new energy function to account for the overall Channel Allocation optimization, not only for new calls but also for handoff Channel Allocation resulting from traffic mobility. That is, both new call blocking and handoff call blocking probabilities are applied as a joint performance estimator. We refer to the enhanced model as HNN-DCA++. We have also considered a variation of the original technique based on a simple handoff priority scheme, here referred to as HNN-DCA+. The two neural DCA schemes together with the original model are evaluated under traffic mobility and their performance compared in terms of new-call blocking and handoff-call dropping probabilities. Results show that the HNN-DCA++ model performs favorably due to its embedded control for assisting handoff Channel Allocation.
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dynamic Channel Allocation based on a hopfield neural network and requirements for autonomous operation in a distributed environment
Vehicular Technology Conference, 1999Co-Authors: Oscar Lazaro, Demessie GirmaAbstract:This paper presents a distributed architecture that provides a platform for performance study of cellular radio dynamic Channel Allocation (DCA) schemes based on a Hopfield neural network (HNN). HNN-based DCA schemes have been studied over the recent years but these techniques are invariably centralised. Operation in a distributed environment necessitates certain system level requirements for accessing system-wide Channel Allocation information used by the algorithm. Distributed operation also suggests that the HNN-based DCA schemes operate autonomously and it would be of interest to evaluate how such schemes compare with the centralised schemes. The paper describes a suitable network architecture for a distributed HNN and highlights some of its key benefits through simulation.
Oscar Lazaro - One of the best experts on this subject based on the ideXlab platform.
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a hopfield neural network based dynamic Channel Allocation with handoff Channel reservation control
IEEE Transactions on Vehicular Technology, 2000Co-Authors: Oscar Lazaro, Demessie GirmaAbstract:As Channel Allocation schemes become more complex and computationally demanding in cellular radio networks, alternative computational models that provide the means for faster processing time are becoming the topic of research interest. These computational models include knowledge-based algorithms, neural networks, and stochastic search techniques. This paper is concerned with the application of a Hopfield (1982) neural network (HNN) to dynamic Channel Allocation (DCA) and extends previous work that reports the performance of HNN in terms of new call blocking probability. We further model and examine the effect on performance of traffic mobility and the consequent intercell call handoff, which, under increasing load, can force call terminations with an adverse impact on the quality of service (QoS). To maintain the overall QoS, it is important that forced call terminations be kept to a minimum. For an HNN-based DCA, we have therefore modified the underlying model by formulating a new energy function to account for the overall Channel Allocation optimization, not only for new calls but also for handoff Channel Allocation resulting from traffic mobility. That is, both new call blocking and handoff call blocking probabilities are applied as a joint performance estimator. We refer to the enhanced model as HNN-DCA++. We have also considered a variation of the original technique based on a simple handoff priority scheme, here referred to as HNN-DCA+. The two neural DCA schemes together with the original model are evaluated under traffic mobility and their performance compared in terms of new-call blocking and handoff-call dropping probabilities. Results show that the HNN-DCA++ model performs favorably due to its embedded control for assisting handoff Channel Allocation.
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dynamic Channel Allocation based on a hopfield neural network and requirements for autonomous operation in a distributed environment
Vehicular Technology Conference, 1999Co-Authors: Oscar Lazaro, Demessie GirmaAbstract:This paper presents a distributed architecture that provides a platform for performance study of cellular radio dynamic Channel Allocation (DCA) schemes based on a Hopfield neural network (HNN). HNN-based DCA schemes have been studied over the recent years but these techniques are invariably centralised. Operation in a distributed environment necessitates certain system level requirements for accessing system-wide Channel Allocation information used by the algorithm. Distributed operation also suggests that the HNN-based DCA schemes operate autonomously and it would be of interest to evaluate how such schemes compare with the centralised schemes. The paper describes a suitable network architecture for a distributed HNN and highlights some of its key benefits through simulation.
S. Sun - One of the best experts on this subject based on the ideXlab platform.
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Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Systems: Tractability and Computation
IEEE Transactions on Wireless Communications, 2016Co-Authors: L. Lei, C K Ho, D Yuan, S. SunAbstract:A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration for 5G systems. NOMA allows more than one user to simultaneously access the same frequency-time resource and separates multi-user signals by SIC. These render resource optimization in NOMA different from orthogonal multiple access. We provide theoretical insights and algorithmic solutions to jointly optimize power and Channel Allocation in NOMA. We mathematically formulate NOMA resource Allocation problems, and characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming to deliver near-optimal solutions. To gauge the performance of the solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource Allocation scheme.
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Joint Optimization of Power and Channel Allocation with Non-Orthogonal Multiple Access for 5G Cellular Systems
2015 IEEE Global Communications Conference (GLOBECOM), 2015Co-Authors: L. Lei, C K Ho, D Yuan, S. SunAbstract:Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is considered as a candidate multi-user access scheme for 5G cellular systems. In this paper, we provide theoretical insights and solution algorithm for optimizing multi- user power and Channel Allocation in NOMA systems. We mathematically formulate the NOMA resource Allocation problem and prove its NP-hardness. For solving the problem, we propose an algorithm combining Lagrangian duality and dynamic programming to deliver a competitive suboptimal solution. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance over orthogonal frequency division multiple access (OFDMA) as well as over other existing NOMA resource Allocation scheme.
Vincent W S Wong - One of the best experts on this subject based on the ideXlab platform.
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joint logical topology design interface assignment Channel Allocation and routing for multi Channel wireless mesh networks
IEEE Transactions on Wireless Communications, 2007Co-Authors: Amirhamed Mohsenianrad, Vincent W S WongAbstract:A multi-Channel wireless mesh network (MC-WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each NIC operates on a distinct frequency Channel. Two neighboring routers establish a logical link if each one has an NIC operating on a common Channel. Given the physical topology of the routers and other constraints, four important issues should be addressed in MC-WMNs: logical topology formation, interface assignment, Channel Allocation, and routing. Logical topology determines the set of logical links. Interface assignment decides how the logical links should be assigned to the NICs in each wireless router. Channel Allocation selects the operating Channel for each logical link. Finally, routing determines through which logical links the packets should be forwarded. In this paper, we mathematically formulate the logical topology design, interface assignment, Channel Allocation, and routing as a joint linear optimization problem. Our proposed MC-WMN architecture is called TiMesh. Extensive ns-2 simulation experiments are conducted to evaluate the performance of TiMesh and compare it with two other MC-WMN architectures Hyacinth [1] and CLICA [2]. Simulation results show that TiMesh achieves higher aggregated network throughput and lower end-to-end delay than Hyacinth and CLICA for both TCP and UDP traffic. It also provides better fairness among different flows.
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joint Channel Allocation interface assignment and mac design for multi Channel wireless mesh networks
IEEE International Conference Computer and Communications, 2007Co-Authors: Vincent W S WongAbstract:In a wireless mesh network (WMN) with a number of stationary wireless routers, the aggregate capacity can be increased when each router is equipped with multiple network interface cards (NICs) and each NIC within a router is assigned to a distinct orthogonal frequency Channel. In this paper, given the logical topology of the network, we formulate the joint Channel Allocation, interface assignment, and media access control (MAC) problem as a cross-layer non-linear mixed-integer network utility maximization problem. An optimal joint design, based on exact binary linearization techniques, is proposed which leads to a global maximum. A near-optimal joint design, based on approximate dual decomposition techniques, is also proposed which is of more interest in terms of practical deployment. Performance evaluation is given through a number of numerical examples in terms of network utility maximization and aggregate network throughput.
Weidong Wang - One of the best experts on this subject based on the ideXlab platform.
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deep reinforcement learning based dynamic Channel Allocation algorithm in multibeam satellite systems
IEEE Access, 2018Co-Authors: Shuaijun Liu, Weidong WangAbstract: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.