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. Providing packet-level quality of service (QoS) is critical to support both rate-sensitive and delay-sensitive applications in bandwidth-constrained, shared-channel, multihop wireless networks. Packet scheduling has been a very popular paradigm to ensure minimum throughput and bounded delay access for packet flows. This work describes a packet scheduling approach to QoS provisioning in multihop wireless networks. Besides minimum throughput and delay bounds for each flow, our scheduling disciplines seek to achieve fair and maximum allocation of the shared wireless channel bandwidth. However, these two criteria can potentially be in conflict in a generictopology multihop wireless network where a single logical channel is shared among multiple contending flows and spatial reuse of the channel bandwidth is possible. In this paper, we propose a new scheduling model that addresses this conflict. The main results of this paper are the following: (a) a two-tier service model that provides a minimum "fair" allocation of the channel bandwidth for each packet flow and additionally maximizes spatial reuse of bandwidth, (b) an ideal centralized packet scheduling algorithm that realizes the above service model, and (c) a practical distributed backoff-based channel contention mechanism that approximates the ideal service within the framework of the CSMA CA protocol. The performance of existing coded caching schemes is sensitive to the worst channel quality, when applied to wireless channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness across the different users. Our online delivery scheme combines (i) joint scheduling and power control for the fading broadcast channel, and (ii) congestion control for ensuring the optimal long-term average performance. By restricting the caching operations to decentralized coded caching proposed in the literature, we prove that our proposed scheme has near-optimal overall performance with respect to the long-term alpha fairness performance. By tuning the coefficient alpha, the operator can differentiate the user performance in terms of video delivery rates achievable by coded caching. We demonstrate via simulations that our scheme outperforms standard coded caching and unicast opportunistic scheduling, which are identified as special cases of our general framework. In this paper, we introduce an approach for application-aware resource block scheduling of elastic and inelastic adaptive real-time traffic in fourth generation Long Term Evolution (LTE) systems. The users are assigned to resource blocks. A transmission may use multiple resource blocks scheduled over frequency and time. In our model, we use logarithmic and sigmoidal-like utility functions to represent the users applications running on different user equipments (UE)s. We present an optimal problem with utility proportional fairness policy, where the fairness among users is in utility percentage (i.e user satisfaction with the service) of the corresponding applications. Our objective is to allocate the resources to the users with priority given to the adaptive real-time application users. In addition, a minimum resource allocation for users with elastic and inelastic traffic should be guaranteed. Every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our scheduling policy exists and achieves the maximum. Therefore the optimal solution is tractable. We present a centralized scheduling algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our scheduling algorithm and compare our results with conventional proportional fairness approaches. The results show that the user satisfaction is higher with our proposed method. This paper studies the problem of congestion control and scheduling in ad hoc wireless networks that have to support a mixture of best-effort and real-time traffic. Optimization and stochastic network theory have been successful in designing architectures for fair resource allocation to meet long-term throughput demands. However, to the best of our knowledge, strict packet delay deadlines were not considered in this framework previously. In this paper, we propose a model for incorporating the quality-of-service (QoS) requirements of packets with deadlines in the optimization framework. The solution to the problem results in a joint congestion control and scheduling algorithm that fairly allocates resources to meet the fairness objectives of both elastic and inelastic flows and per-packet delay requirements of inelastic flows. Point-to-multipoint broadband wireless access (BWA) is an attractive solution to provide advanced data services to users who do not have easy access to wireline data services. However, BWA will need to support delay sensitive services as users and applications are becoming increasingly sophisticated. Wireless packet scheduling is an important part of quality-of-service (QoS) provisioning in BWA. The biggest difficulty in wireless packet scheduling is the location-dependent channel errors. Channel condition independent fair scheme (CIF) (T.S. Eugene, et al, March 1998) uses explicit leading lagging approach to compensate for the erroneous channel state, but is not able to satisfy both rapid convergent guaranteed throughput and fairness. In this paper, we propose to use the token bank fair queuing (TBFQ) scheduling algorithm as a QoS provisioning mechanism for an asymmetric broadband point-to-multipoint wireless LAN. We consider the throughput and fairness performance of TBFQ under location-dependent channel error condition with variable packet length. This work is an extension of our previous work in applying TBFQ in next generation cellular networks. SUMMARY The token bank fair queuing algorithm (TBFQ) is a novel scheduling algorithm that is suitable for wireless multimedia services. The bandwidth allocation mechanism integrates the leaky bucket structure with priority handling to address the problem of providing quality-of-service (QoS) guarantees to heterogeneous applications in the next generation packet-switched wireless networks. Scheduling algorithms are often tightly integrated with the wireless medium access control (MAC) protocol. However, when heterogeneous wireless systems need to be integrated and interoperate with each other, it is desirable from the QoS provisioning standpoint to decouple scheduling algorithm from the MAC protocol. In this paper we propose a framework of seamless QoS provisioning and the application of TBFQ for uplink and downlink scheduling in wireless networks. We study its performance under a generic medium access framework that enables the algorithm to be generalized to provide QoS guarantees under various medium access schemes. We give a brief analysis of the algorithm and compare its performance with common scheduling algorithms through simulation. Our results demonstrate that TBFQ significantly increases wireless channel utilization while maintaining the same QoS, unlike many fair queuing algorithms, TBFQ does not require timestamping information of each packet arrival an impractical feature in an already resource scarce environment. This makes TBFQ suitable for wireless multimedia communication. Copyright # 2004 John Wiley & Sons, Ltd. 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. Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS) requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems. We analyze the sum capacity of multicell wireless networks with full resource reuse and channel-driven opportunistic scheduling in each cell. We address the problem of finding the co-channel (throughout the network) user assignment that results in the optimal joint multicell capacity, under a resource-fair constraint and a standard power control strategy. This problem in principle requires processing the complete co-channel gain information, and thus, has so far been justly considered unpractical due to complexity and channel gain signaling overhead. However, we expose here the following key result: The multicell optimal user scheduling problem admits a remarkably simple and fully distributed solution for large networks. This result is proved analytically for an idealized network. From this constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes. Cognitive radio and Dynamic Spectrum Access (DSA) enable wireless users to share a wide range of available spectrums. In this paper, we study joint spectrum allocation and scheduling problems in cognitive radio wireless networks with the objectives of achieving fair spectrum sharing. A novel Multi-Channel Contention Graph (MCCG) is proposed to characterize the impact of interference under the protocol model in such networks. Based on the MCCG, we present an optimal algorithm to compute maximum throughput solutions. As simply maximizing throughput may result in a severe bias on resource allocation, we take fairness into consideration by presenting optimal algorithms as well as fast heuristics to compute fair solutions based on a simplified max-min fairness model and the well-known proportional fairness model. Numerical results show that the performance given by our heuristic algorithms is very close to that of the optimal solution, and our proportional fair algorithms achieve a good tradeoff between throughput and fairness. In addition, we extend our research to the physical interference model, and propose effective heuristics for solving the corresponding problems. We consider the problem of scheduling data in the downlink of a cellular network, over parallel time-varying channels, while providing quality of service (QoS) guarantees, to multiple users in the network. We design simple and efficient admission control, resource allocation, and scheduling algorithms for guaranteeing requested QoS. Our scheduling algorithms consists of two sets, namely, (what we call) joint KH 2) if the admission control allocates channel resources to the K&H scheduling only, due to loose delay requirements, then there is no need to use the RC scheduler. In designing the RC scheduler, we propose a reference channel approach and formulate the scheduler as a linear program, dispensing with complex dynamic programming approaches, by the use of a resource allocation scheme. An advantage of this formulation is that the desired QoS constraints can be explicitly enforced, by allotting sufficient channel resources to users, during call admission. We investigate the benefits of channel-aware (opportunistic) scheduling of transmissions in ad hoc networks. The key challenge in optimizing the performance of such systems is finding a good compromise among three interdependent quantities: 1) the density of scheduled transmitters; 2) the quality of transmissions; and 3) the long term fairness among nodes. We propose two new channel-aware slotted CSMA protocols opportunistic CSMA and quantile-based CSMA (QT-CSMA) and develop new stochastic geometric models to quantify their performance in terms of spatial reuse and spatial fairness. When properly optimized, these protocols offer substantial improvements in performance relative to CSMA—particularly, when the density of nodes is moderate to high. In addition, we show that a simple version of QT-CSMA can achieve robust performance gains without requiring careful parameter optimization. The quantitative results in this paper suggest that channel-aware scheduling in ad hoc networks can provide substantial benefits which might far outweigh the associated implementation overheads. In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme. We consider the problem of LTE network self organization and optimization of resource allocation. One particular challenge for LTE systems is that, by applying OFDMA, a transmission may use multiple resource blocks scheduled over the frequency and time. There are three key components involved in the resource allocation and network optimization: resource block scheduling, power control, and client association. We propose a distributed protocol that aims to achieve weighted proportional fairness (WPF) among clients by jointly consider them. The cross-layer design includes: (i) an optimal online policy for resource block scheduling, (ii) a heuristic for transmit power control, and (iii) a selfish strategy for client association. The proposed scheme only requires limited local information exchange and thus can be easily implemented for large networks. Simulation results have shown its effectiveness in both the system throughput and user fairness. We consider a multi-channel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the Gilbert- Elliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the state of the chosen channel. The problem is to design a sensing policy for channel selection to maximize the average reward, which can be formulated as a multi-arm restless bandit process. In this paper, we study the structure, optimality, and performance of the myopic sensing policy. We show that the myopic sensing policy has a simple robust structure that reduces channel selection to a round-robin procedure and obviates the need for knowing the channel transition probabilities. The optimality of this simple policy is established for the two-channel case and conjectured for the general case based on numerical results. The performance of the myopic sensing policy is analyzed, which, based on the optimality of myopic sensing, characterizes the maximum throughput of a multi-channel opportunistic communication system and its scaling behavior with respect to the number of channels. These results apply to cognitive radio networks, opportunistic transmission in fading environments, downlink scheduling in centralized networks, and resource-constrained jamming and anti-jamming. Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.