In this paper, we demonstrate the optimal power allocation for QPSK, 16-QAM, and 64-QAM modulation schedules and the role of channel quality indicator (CQI). We used sigmoidal-like utility functions to represent the probability of successful reception of packets at user equipment (UE). CQI as a feedback to the base station (BS) indicates the data rate that a downlink channel can support. With Levenberg-Marquardt (LM) Optimization method, we present utility functions of different CQI values for standardized 15 Modulation order and Coding Scheme (MCS) in @math Generation Partnership Project (3GPP). Finally, we simulate and show the results of the optimal power allocation algorithm.
In @cite_12 -[6], the fairness in resource allocation is considered as a more important issue. The fairness QoS constraints have been set in the optimal resource allocation in @cite_12 and @cite_4 . In @cite_12 , the authors provided an opportunistic power scheduling scheme for "multi-server" wireless systems while meeting the minimum QoS for each user. A stochastic process has been used to present each user's performance value in @cite_16 and they proposed an opportunistic transmission-scheduling policy to maximize the average system performance. In @cite_18 , Multi-channel Fair Scheduler (MFS) has been introduced and analyzed to guarantees both long-term deterministic (MFS-D) and probabilistic (MFSP) fairness over multiple wireless channels. They provided a framework that maximizes total system throughput for opportunistic scheduling over multiple wireless channels.
@cite_18 @cite_16 @cite_4 @cite_12
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We consider the problem of downlink scheduling for multiuser orthogonal frequency-division multiplexing (OFDM) systems. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. Previous work in this area has focused on single-channel systems. Multiuser OFDM allows multiple users to transmit simultaneously over multiple channels. In this paper, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three QoS fairness constraints formultiuserOFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. Our scheduler decides not only which time slot, but also which subcarrier to allocate to each user. Implementing these optimal policies involves solving a maximal bipartite matching problem at each scheduling time. To solve this problem efficiently, we apply a modified Hungarian algorithm and a simple suboptimal algorithm. Numerical results demonstrate that our schemes achieve significant improvement in system performance compared with nonopportunistic schemes. Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives. An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network?s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes. We present an "opportunistic" transmission scheduling policy that exploits time-varying channel conditions and maximizes the system performance stochastically under a certain resource allocation constraint. We establish the optimality of the scheduling scheme and also that every user experiences a performance improvement over any nonopportunistic scheduling policy when users have independent performance values. We demonstrate via simulation results that the scheme is robust to estimation errors and also works well for nonstationary scenarios, resulting in performance improvements of 20 -150 compared with a scheduling scheme that does not take into account channel conditions. Last, we discuss an extension of our opportunistic scheduling scheme to improve "short-term" performance.