This paper studies the problem of utility maximization for clients with delay based QoS requirements in wireless networks. We adopt a model used in a previous work that characterizes the QoS requirements of clients by their delay constraints, channel reliabilities, and delivery ratio requirements. In this work, we assume that the utility of a client is a function of the delivery ratio it obtains. We treat the delivery ratio for a client as a tunable parameter by the access point (AP), instead of a given value as in the previous work. We then study how the AP should assign delivery ratios to clients so that the total utility of all clients is maximized. We apply the techniques introduced in two previous papers to decompose the utility maximization problem into two simpler problems, a CLIENT problem and an ACCESS-POINT problem. We show that this decomposition actually describes a bidding game, where clients bid for the service time from the AP. We prove that although all clients behave selfishly in this game, the resulting equilibrium point of the game maximizes the total utility. In addition, we also establish an efficient scheduling policy for the AP to reach the optimal point of the ACCESS-POINT problem. We prove that the policy not only approaches the optimal point but also achieves some forms of fairness among clients. Finally, simulation results show that our proposed policy does achieve higher utility than all other compared policies.
There is also research on utility maximization for both wireline and wireless networks. Kelly @cite_2 and Kelly, Maulloo, and Tan @cite_4 have considered the rate control algorithm to achieve maximum utility in a wireline network. Lin and Shroff @cite_7 has studied the same problem with multi-path routing. As for wireless networks, Xiao, Shroff, and Chong @cite_3 has proposed a power-control framework to maximize utility, which is defined as a function of the signal-to-interference ratio and cannot reflect channel unreliability. Cao and Li @cite_10 has proposed a bandwidth allocation policy that also considers channel degradation. Bianchi, Campbell, and Liao @cite_9 has studied utility-fair services in wireless networks. However, all the aforementioned works assume that the utility is only determined by the allocated bandwidth. Thus, they do not consider applications that require delay bounds.
@cite_4 @cite_7 @cite_9 @cite_3 @cite_2 @cite_10
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In wireless cellular networks and wireless local area networks, nonlinear network utility maximization need to be conducted over both user rates and transmit powers. For each of the three cases considered in this paper, we present an algorithm that converges to the jointly optimal pair of rate vector and power vector. For the simple case when data rates are not limited by interferences, for example in single-cell downlink transmissions, we propose algorithm 1, which is an iterative bidding mechanism between the base station and mobile users, where knowledge about channel conditions and individual user utility functions is only needed locally at each user but not needed at the base station. In the case when data rates are limited by interferences, the utility maximization problem is complicated both by nonlinear coupling between powers and rates, and by interference among powers. Through centralized iterative steps, we propose algorithm 2, which converges to a joint and global optimum over the solution space of rates and powers. We then consider end-to-end transmissions in cellular networks, which traverse both wireless fading channels and many hops of wired links shared by other traffic. There is a tradeoff between attaining air-interface capacity in the wireless hop and controlling congestion in the wired backbone wide area network. We formulate this end-to-end resource allocation problem in such hybrid networks, and present a solution to obtain the Pareto optimal tradeoff between attaining wireless multi-access fading channel capacity and maximizing global network utility. This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multi-rate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with time-varying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dual-based algorithm remains stable and optimal when the constraint set is modulated by an irreducible finite-state Markov chain. This paper thus presents a step toward a systematic way to carry out cross-layer design in the framework of “layering as optimization decomposition” for time-varying channel models. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. In this paper, we study the problem of joint routing, link scheduling and power control to support high data rates for broadband wireless multihop networks. We first address the problem of finding an optimal link scheduling and power control policy that minimizes the total average transmission power in the wireless multihop network, subject to given constraints regarding the minimum average data rate per link, as well as peak transmission power constraints per node. Multiaccess signal interference is explicitly modeled. We use a duality approach whereby, as a byproduct of finding the optimal policy, we find the sensitivity of the minimal total average power with respect to the average data rate for each link. Since the minimal total average power is a convex function of the required minimum average data rates, shortest path algorithms with the link weights set to the link sensitivities can be used to guide the search for a globally optimum routing. We present a few simple examples that show our algorithm can find policies that support data rates that are not possible with conventional approaches. Moreover, we find that optimum allocations do not necessarily route traffic over minimum energy paths. We consider distributed power control in a multicell wireless data system and study the effect of pricing transmit power. Drawing on the earlier work of Goodman and Mandayam (see IEEE Personal Commun. Mag., vol.7, p.48-54, 2000), we formulate the QoS of a data user via a utility function measured in bits per Joule. We consider distributed power control, modeled as a noncooperative game, where users maximize their utilities in a multicell system. Base station assignment based on received signal strength as well as received signal-to-interference ratio (SIR) are considered jointly with power control. Our results indicate that for both assignment schemes, such a procedure results in an inefficient operating point (Nash equilibrium) for the entire system. We introduce pricing of transmit power as a mechanism for influencing data user behavior and our results show that the distributed power control based on maximizing the net utility (utility minus the price) results in improving the Pareto efficiency of the resulting operating point. Variations of pricing based on global and local loading in cells are considered as a means of improving the efficiency of wireless data networks. Finally, we discuss the improvement in utilities through a centralized scheme where each base station (BS) calculates the best SIR to be targeted by the terminals it is assigned. This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional waterfilling interpretations. Next, stochastic data arrivals are addressed. Finally, online and or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations. Abstract In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users’ modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our power allocation optimization problem as a product of utilities of all users and prove that it is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes. Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing policy. This can be used to flatten traffic and decrease the service price for users. We also compare our results with a benchmark algorithm and show that our algorithm performs better in allocating powers fairly to all users without dropping any user in order to maximize performance. Link-layer fairness models that have been proposed for wireline and packet cellular networks cannot be generalized for shared channel wireless networks because of the unique characteristics of the wireless channel, such as location-dependent contention, inherent conflict between optimizing channel utilization and achieving fairness, and the absence of any centralized control. In this paper, we propose a general analytical framework that captures the unique characteristics of shared wireless channels and allows the modeling of a large class of system-wide fairness models via the specification of per-flow utility functions. We show that system-wide fairness can be achieved without explicit global coordination so long as each node executes a contention resolution algorithm that is designed to optimize its local utility function. We present a general mechanism for translating a given fairness model in our framework into a corresponding contention resolution algorithm . Using this translation, we derive the backoff algorithm for achieving proportional fairness in wireless shared channels, and compare the fairness properties of this algorithm with both the ideal proportional fairness objective, and state-of-the-art backoff-based contention resolution algorithms. We believe that the two aspects of the proposed framework, i.e. the ability to specify arbitrary fairness models via local utility functions, and the ability to automatically generate local contention resolution mechanisms in response to a given utility function, together provide the path for achieving flexible service differentiation in future shared channel wireless networks. We consider the problem of designing distributed mechanisms for joint congestion control and resource allocation in spatial-reuse TDMA wireless networks. The design problem is posed as a utility maximization subject to link rate constraints that involve both power allocation and transmission scheduling over multiple time-slots. Starting from the performance limits of a centralized optimization based on global network information,we proceed systematically in the development of distributed and transparent protocols. In the process,we introduce a novel decomposition method for convex optimization,establish its convergence for the utility maximization problem and demonstrate how it suggests a distributed solution based on flow control optimization and incremental updates of the transmission schedule.We develop a two-step procedure for finding the schedule updates and suggest two schemes for distributed channel reservation and power control under realistic interference models. Although the final protocols are suboptimal,we isolate and quantify the performance losses incurred by each simplification and demonstrate strong performance in examples. We consider dynamic routing and power allocation for a wireless network with time-varying channels. The network consists of power constrained nodes that transmit over wireless links with adaptive transmission rates. Packets randomly enter the system at each node and wait in output queues to be transmitted through the network to their destinations. We establish the capacity region of all rate matrices ( spl lambda sub ij ) that the system can stably support-where spl lambda sub ij represents the rate of traffic originating at node i and destined for node j. A joint routing and power allocation policy is developed that stabilizes the system and provides bounded average delay guarantees whenever the input rates are within this capacity region. Such performance holds for general arrival and channel state processes, even if these processes are unknown to the network controller. We then apply this control algorithm to an ad hoc wireless network, where channel variations are due to user mobility. Centralized and decentralized implementations are compared, and the stability region of the decentralized algorithm is shown to contain that of the mobile relay strategy developed by Grossglauser and Tse (2002). Power allocation across users in two adjacent cells is studied for a code-division multiple access (CDMA) data service. The forward link is considered and cells are modeled as one-dimensional with uniformly distributed users and orthogonal signatures within each cell. Each user is assumed to have a utility function that describes the user's received utility, or willingness to pay, for a received signal-to-interference-plus-noise ratio (SINR). The objective is to allocate the transmitted power to maximize the total utility summed over all users subject to power constraints in each cell. It is first shown that this optimization can be achieved by a pricing scheme in which each base station announces a price per unit transmitted power to the users, and each user requests power to maximize individual surplus (utility minus cost). Setting prices to maximize total revenue over both cells is also considered, and it is shown that, in general, the solution is different from the one obtained by maximizing total utility. Conditions are given for which independent optimization in each cell, which leads to a Nash equilibrium (NE), is globally optimal. It is shown that, in general, coordination between the two cells is needed to achieve the maximum utility or revenue. In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. There has been extensive work on network architectures that support multi-path routing to improve performance in wireless mesh networks. However, previous work uses ad-hoc design principles that cannot guarantee any network-wide performance objectives such as conjointly maximizing resource utilization and improving fairness. In parallel, numerous theoretical results have addressed the issue of optimizing a combined metric of network utilization and fairness using techniques based on back-pressure scheduling, routing and flow control. However, the proposed theoretical algorithms are extremely difficult to implement in practice, especially in the presence of the 802.11 MAC and TCP. We propose Horizon, a novel system design for multi-path forwarding in wireless meshes, based on the theoretical results on back-pressure. Our design works with an unmodified TCP stack and on top of the existing 802.11 MAC. We modified the back-pressure approach to obtain a simple 802.11-compatible packet-forwarding heuristic and a novel, light-weight path estimator, while maintaining global optimality properties. We propose a delayed reordering algorithm that eliminates TCP timeouts while keeping TCP packet reordering to a minimum. We have evaluated our implementation on a 22-node testbed. We have shown that Horizon effectively utilizes available resources (disjoint paths). In contrast to previous work, our design not only avoids bottlenecks but also optimally load-balances traffic across them when needed, improving fairness among competing flows. To our knowledge, Horizon is the first practical wireless system based on back-pressure. In this paper, we study utility maximization problems for communication networks where each user (or class) can have multiple alternative paths through the network. This type of multi-path utility maximization problems appear naturally in several resource allocation problems in communication networks, such as the multi-path flow control problem, the optimal quality-of-service (QoS) routing problem, and the optimal network pricing problem. We develop a distributed solution to this problem that is amenable to online implementation. We analyze the convergence of our algorithm in both continuous-time and discrete-time, and with and without measurement noise. These analyses provide us with guidelines on how to choose the parameters of the algorithm to ensure efficient network control. In this paper, we study the resource allocation algorithm design for multiuser orthogonal frequency division multiplexing (OFDM) downlink systems with simultaneous wireless information and power transfer. The algorithm design is formulated as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit Joule delivered to the users). In particular, the problem formulation takes into account the minimum required system data rate, heterogeneous minimum required power transfers to the users, and the circuit power consumption. Subsequently, by exploiting the method of timesharing and the properties of nonlinear fractional programming, the considered non-convex optimization problem is solved using an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation and user selection solution are derived based on Lagrange dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm achieves the maximum energy efficiency of the system and reveal how energy efficiency, system capacity, and wireless power transfer benefit from the presence of multiple users in the system. This paper investigates the optimal rate allocation problem with end-to-end delay constraints in multi-hop wireless networks. We introduce Virtual Link Capacity Margin (VLCM), which is the gap between the schedulable link capacity and the maximum allowable flow rate over a link, for link delay control. We formulate the problem as a utility maximization framework with two sets of constraints: 1) capacity and schedulability constraints and 2) end-to-end delay constraints. By dual decomposition of the original optimization problem, we present a control algorithm that jointly tunes the flow rates and VLCMs, through a double-price scheme derived with regard to the constraints: the link congestion price reflecting the traffic load of a link, and the flow delay price reflecting the margin between the average packet delay and the delay requirement of a flow. We prove that the algorithm converges to a global optimum where the aggregate network utility defined over the flow rate set is maximized, while the delay constraints are satisfied. A key feature of our algorithm is it does not rely on a specific traffic model. The algorithm is implemented distributedly via joint wireless link scheduling and congestion control. Simulation results show that our algorithm outperforms heuristic rate allocation algorithms while satisfying the end-to-end delay constraints. In wireless data networks, the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as transmit powers and bandwidths) to the links. The optimal performance of the network can only be achieved by simultaneous optimization of routing and resource allocation. In this paper, we formulate the simultaneous routing and resource allocation (SRRA) problem, and exploit problem structure to derive efficient solution methods. We use a capacitated multicommodity flow model to describe the data flows in the network. We assume that the capacity of a wireless link is a concave and increasing function of the communications resources allocated to the link, and the communications resources for groups of links are limited. These assumptions allow us to formulate the SRRA problem as a convex optimization problem over the network flow variables and the communications variables. These two sets of variables are coupled only through the link capacity constraints. We exploit this separable structure by dual decomposition. The resulting solution method attains the optimal coordination of data routing in the network layer and resource allocation in the radio control layer via pricing on the link capacities. This paper analyzes the performance of a duty-cycled polling-based access mechanism that exploits the Transmission Opportunity Power Save Mode (TXOP PSM) defined in the IEEE 802.11ac to improve the energy efficiency of Wireless Local Area Networks (WLANs) based on the IEEE 802.11. The basic idea behind the proposed approach, named GreenPoll, is to enable contention free periods, based on polling with beacons, during which wireless stations can save energy by turning off their radio transceivers after exchanging data with the access point. The closed expression of energy efficiency of GreenPoll is formulated in this paper and is used to evaluate the performance of GreenPoll considering important parameters like the traffic load, packet length, data rate, and number of stations in the network. Both analytical and simulation results show the high energy efficiency of GreenPoll with gains of up to 330 and 110 when compared to the legacy Distributed Coordination Function (DCF) and the Point Coordination Function (PCF) defined in the IEEE 802.11, respectively. The throughput of wireless networks can be significantly improved by multi-channel communications compared with single-channel communications since the use of multiple channels can reduce interference influence. In this paper, we study interference-aware topology control and QoS routing in IEEE 802.11-based multi-channel wireless mesh networks with dynamic traffic. Channel assignment and routing are two basic issues in such networks. Different channel assignments can lead to different network topologies. We present a novel definition of co-channel interference. Based on this concept, we formally define and present an effective heuristic for the minimum INterference Survivable Topology Control (INSTC) problem which seeks a channel assignment for the given network such that the induced network topology is interference-minimum among all K-connected topologies. We then formulate the Bandwidth-Aware Routing (BAR) problem for a given network topology, which seeks routes for QoS connection requests with bandwidth requirements. We present a polynomial time optimal algorithm to solve the BAR problem under the assumption that traffic demands are splittable. For the non-splittable case, we present a maximum bottleneck capacity path routing heuristic. Simulation results show that compared with the simple common channel assignment and shortest path routing approach, our scheme improves the system performance by 57 on average in terms of connection blocking ratio. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.