This paper studies the packet scheduling problem in Broadband Wireless Access (BWA) networks. The key difficulties of the BWA scheduling problem lie in the high variability of wireless channel capacity and the unknown...
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This paper studies the packet scheduling problem in Broadband Wireless Access (BWA) networks. The key difficulties of the BWA scheduling problem lie in the high variability of wireless channel capacity and the unknown model of packet arrival process. It is difficult for traditional heuristic scheduling algorithms to handle the situation and guarantee satisfying performance in BWA networks. In this paper, we introduce learning-based approach for a better solution. Specifically, we formulate the packet scheduling problem as an average cost Semi-Markov Decision Process (SMDP). Then, we solve the SMDP by using reinforcement learning. A feature-based linear approximation and the Temporal-Difference learning technique are employed to produce a near optimal solution of the corresponding SMDP problem. The proposed algorithm, called Reinforcement Learning Scheduling (RLS), has in-built capability of self-training. It is able to adaptively and timely regulate its scheduling policy according to the instantaneous network conditions. Simulation results indicate that RLS outperforms two classical scheduling algorithms and simultaneously considers: (i) effective QoS differentiation, (ii) high bandwidth utilization, and (iii) both short-term and long-term fairness. Copyright (C) 2009 Rong Yu et al.
We analyze the performance of SNR-based scheduling algorithms in broadcast ergodic fading channels where multiuser selection diversity is exploited. At each channel state, the user with the highest weighted signal-to-...
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We analyze the performance of SNR-based scheduling algorithms in broadcast ergodic fading channels where multiuser selection diversity is exploited. At each channel state, the user with the highest weighted signal-to-noise ratio is selected to be transmitted. The use of weights associated to the users allows us to control the degree of fairness among users and to arrange them according to a prescribed quality of service. These weights parametrize the scheduling algorithms so each set of weights corresponds to a specific scheduling algorithm. Assuming Rayleigh fading broadcast channel, we derive a closed-form expression for the achievable user's rates as a function of the scheduling algorithm, the channel fading statistics of each user, and the transmit power. With the help of this expression, we solve some interesting inverse problems. For example, for a given arbitrary channel statistics we obtain the optimum scheduling algorithm to achieve a prescribed set of users' rates with minimum transmit power. Copyright (C) 2009 Jesus Perez et al.
Because of the high attenuation, sound is mainly used as the communication medium in underwater sensor networks instead of light or microwave. MAC protocols designed for radio networks aren't suitable for acoustic...
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ISBN:
(纸本)9781479987313
Because of the high attenuation, sound is mainly used as the communication medium in underwater sensor networks instead of light or microwave. MAC protocols designed for radio networks aren't suitable for acoustic channels which feature long propagation delay and limited bandwidth. In this paper, we propose a location-based MAC protocol, called ***-MAC dynamically determines each node's sending time according to the variable offered load, and assigns the time slot to each node based on the minimum waiting time rule. In addition, the protocol combines sending and acknowledgement to overcome low throughput and long end-to-end delay problems. By theoretical analysis and simulation, we show that LT-MAC is a comparatively good solution for single-hop and small-scale underwater sensor networks.
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