text: 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.
  @cite_18: 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.
  @cite_16: 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.
  @cite_4: Abstract We present a method, called opportunistic scheduling, for exploiting the time-varying nature of the radio environment to increase the overall performance of the system under certain quality of service fairness requirements of users. We first introduce a general framework for opportunistic scheduling, and then identify three general categories of scheduling problems under this framework. We provide optimal solutions for each of these scheduling problems. All the proposed scheduling policies are implementable online; we provide parameter estimation algorithms and implementation procedures for them. We also show how previous work by us and others directly fits into or is related to this framework. We demonstrate via simulation that opportunistic scheduling schemes result in significant performance improvement compared with non-opportunistic alternatives.
  @cite_12: We present and power scheduling scheme, i.e., a joint time-slot and power allocation method for wireless cellular systems. We allow multiple transmissions in a time-slot that can interfere with each other. Hence, it is important to not only select the mobiles to he scheduled in a time-slot, hut also important to allocate an appropriate power level for transmission to these scheduled mobiles in order to achieve high system performance and quality of service. We model the time-varying wireless channel as a stochastic process and formulate a stochastic programming problem that attempts to maximize the expected total system utility, with constraints on the minimum expected utility for each mobile. The power scheduling algorithm is obtained by using stochastic duality and implemented via stochastic subgradient techniques.
summary: 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.