Energy-Efficient Resource Allocation in Wireless Networks with Quality-of-Service Constraints

A game-theoretic model is proposed to study the cross-layer problem of joint power and rate control with quality of service (QoS) constraints in multiple-access networks. In the proposed game, each user seeks to choose its transmit power and rate in a distributed manner in order to maximize its own utility while satisfying its QoS requirements. The user's QoS constraints are specified in terms of the average source rate and an upper bound on the average delay where the delay includes both transmission and queuing delays. The utility function considered here measures energy efficiency and is particularly suitable for wireless networks with energy constraints. The Nash equilibrium solution for the proposed non-cooperative game is derived and a closed-form expression for the utility achieved at equilibrium is obtained. It is shown that the QoS requirements of a user translate into a "size" for the user which is an indication of the amount of network resources consumed by the user. Using this competitive multiuser framework, the tradeoffs among throughput, delay, network capacity and energy efficiency are studied. In addition, analytical expressions are given for users' delay profiles and the delay performance of the users at Nash equilibrium is quantified.Comment: Accpeted for publication in the IEEE Transactions on Communication

Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches

An overview of game-theoretic approaches to energy-efficient resource allocation in wireless networks is presented. Focusing on multiple-access networks, it is demonstrated that game theory can be used as an effective tool to study resource allocation in wireless networks with quality-of-service (QoS) constraints. A family of non-cooperative (distributed) games is presented in which each user seeks to choose a strategy that maximizes its own utility while satisfying its QoS requirements. The utility function considered here measures the number of reliable bits that are transmitted per joule of energy consumed and, hence, is particulary suitable for energy-constrained networks. The actions available to each user in trying to maximize its own utility are at least the choice of the transmit power and, depending on the situation, the user may also be able to choose its transmission rate, modulation, packet size, multiuser receiver, multi-antenna processing algorithm, or carrier allocation strategy. The best-response strategy and Nash equilibrium for each game is presented. Using this game-theoretic framework, the effects of power control, rate control, modulation, temporal and spatial signal processing, carrier allocation strategy and delay QoS constraints on energy efficiency and network capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on Resource-Constrained Signal Processing, Communications and Networking, May 200

multimedia transmission over wireless networks: performance analysis and optimal resource allocation

In recent years, multimedia applications such as video telephony, teleconferencing, and video streaming, which are delay sensitive and bandwidth intensive, have started to account for a significant portion of the data traffic in wireless networks. Such multimedia applications require certain quality of service (QoS) guarantees in terms of delay, packet loss, buffer underflows and overflows, and received multimedia quality. It is also important to note that such requirements need to be satisfied in the presence of limited wireless resources, such as power and bandwidth. Therefore, it is critical to conduct a rigorous performance analysis of multimedia transmissions over wireless networks and identify efficient resource allocation strategies. Motivated by these considerations, in the first part of the thesis, performance of hierarchical modulation-based multimedia transmissions is analyzed. Unequal error protection (UEP) of data transmission using hierarchical quadrature amplitude modulation (HQAM) is considered in which high priority (HP) data is protected more than low priority (LP) data. In this setting, two different types of wireless networks are considered. Specifically, multimedia transmission over cognitive radio networks and device-to-device (D2D) cellular wireless networks is addressed. Closed-form bit error rate (BER) expressions are derived and optimal power control strategies are determined. Next, throughput and optimal resource allocation strategies are studied for multimedia transmission under delay QoS and energy efficiency (EE) constraints. A Quality-Rate (QR) distortion model is employed to measure the quality of received video in terms of peak signal-to-noise ratio (PSNR) as a function of video source rate. Effective capacity (EC) is used as the throughput metric under delay QoS constraints. In this analysis, four different wireless networks are taken into consideration: First, D2D underlaid wireless networks are addressed. Efficient transmission mode selection and resource allocation strategies are analyzed with the goal of maximizing the quality of the received video at the receiver in a frequency-division duplexed (FDD) cellular network with a pair of cellular users, one base station and a pair of D2D users under delay QoS and EE constraints. A full-duplex communication scenario with a pair of users and multiple subchannels in which users can have different delay requirements is addressed. Since the optimization problem is not concave or convex due to the presence of interference, optimal power allocation policies that maximize the weighted sum video quality subject to total transmission power level constraint are derived by using monotonic optimization theory. The optimal scheme is compared with two suboptimal strategies. A full-duplex communication scenario with multiple pairs of users in which different users have different delay requirements is addressed. EC is used as the throughput metric in the presence of statistical delay constraints since deterministic delay bounds are difficult to guarantee due to the time-varying nature of wireless fading channels. Optimal resource allocation strategies are determined under bandwidth, power and minimum video quality constraints again using the monotonic optimization framework. A broadcast scenario in which a single transmitter sends multimedia data to multiple receivers is considered. The optimal bandwidth allocation and the optimal power allocation/power control policies that maximize the sum video quality subject to total bandwidth and minimum EE constraints are derived. Five different resource allocation strategies are investigated, and the joint optimization of the bandwidth allocation and power control is shown to provide the best performance. Tradeoff between EE and video quality is also demonstrated. In the final part of the thesis, power control policies are investigated for streaming variable bit rate (VBR) video over wireless links. A deterministic traffic model for stored VBR video, taking into account the frame size, frame rate, and playout buffers is considered. Power control and the transmission mode selection with the goal of maximizing the sum transmission rate while avoiding buffer underflows and overflows under transmit power constraints is exploited in a D2D wireless network. Another system model involving a transmitter (e.g., a base station (BS)) that sends VBR video data to a mobile user equipped with a playout buffer is also adopted. In this setting, both offline and online power control policies are considered in order to minimize the transmission power without playout buffer underflows and overflows. Both dynamic programming and reinforcement learning based algorithms are developed

Optimization of Discrete Power and Resource Block Allocation for Achieving Maximum Energy Efficiency in OFDMA Networks

Most of the resource allocation literature on the energy-efficient orthogonal frequency division multiple access (OFDMA)-based wireless communication systems assume continuous power allocation/control, while, in practice, the power levels are discrete (such as in 3GPP LTE). This convenient continuous power assumption has mainly been due to either the limitations of the used optimization tools and/or the high computational complexity involved in addressing the more realistic discrete power allocation/control. In this paper, we introduce a new optimization framework to maximize the energy efficiency of the downlink transmission of cellular OFDMA networks subject to power budget and quality-of-service constraints, while considering discrete power and resource blocks (RBs) allocations. The proposed framework consists of two parts: 1) we model the predefined discrete power levels and RBs allocations by a single binary variable and 2) we propose a close-to-optimal semidefinite relaxation algorithm with Gaussian randomization to efficiently solve this non-convex combinatorial optimization problem with polynomial time complexity. We notice that a small number of power levels suffice to approach the energy efficiency performance of the continuous power allocation. Based on this observation, we propose an iterative suboptimal heuristic to further reduce the computational complexity. Simulation results show the effectiveness of the proposed schemes in maximizing the energy efficiency, while considering the practical discrete power levels

Unified clustering and communication protocol for wireless sensor networks

In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service