Optimal Power Allocation Policy Research Articles

Multi-layer coding strategy for multi-hop block fading channels with outage probability

This paper considers communication over a multi-hop Rayleigh block-fading channel, where there is no direct link between the transmission ends, and communication is carried out through the use of multiple cascaded relays, employing the Decode and Forward (DF) strategy. It is assumed that the relays cannot do buffering or apply coding over consecutive transmission blocks, and they can merely re-encode the retried information and forward an encoded version of information to the next hop. In this case, assuming the channel information of each hop is not available at the corresponding transmitter and considering the destination either receives a minimum rate R 0 or declare an outage event, a multi-layer coding strategy in addition to a single-layer code of the minimum rate R 0 are employed, where the optimal power allocation policy across code layers is derived, leading to the maximum average achievable rate for a given outage probability.

Optimal Power Management for Remote Estimation With an Energy Harvesting Sensor

This paper studies the design of an estimation system where a remotely observed source sequence is to be communicated through a noisy channel to an estimator. The remote node is assumed to have the capability of harvesting, and, subject to a capacity limit, storing energy from its ambient environment. The focus is on various transmit power-allocation strategies that minimize the mean square error at the estimator for such an energy harvesting estimation system as the fluctuation of harvested energy presents a unique challenge compared with a traditional battery powered system. We first establish the optimality of uncoded transmission for such a system. Two types of side information (SI) at the transmitter are then considered in this paper: noncausal SI (energy harvested in the past, present, and future) and causal SI (energy harvested in the past). For the case where noncausal SI is available and battery storage is unlimited, it is shown that the optimal power allocation amounts to a simple “staircase-climbing” procedure, where the power level follows a nondecreasing staircase function. For the case where battery storage has a finite capacity, the optimal power-allocation policy can also be obtained via standard convex optimization techniques. Dynamic programming (DP) is used to optimize the allocation policy when only causal SI is available. The issue of unknown transmit power at the receiver is also addressed for both the causal and noncausal SI cases. Finally, to make the proposed solutions practically more meaningful, two heuristic schemes are proposed; these schemes are largely motivated by the structure of the solution to the DP formulation but with much reduced computational complexity. Numerical examples are provided to examine the complexity–performance tradeoff of various power-allocation strategies.

Optimal Recursive Power Allocation for Energy Harvesting System With Multiple Antennas

In this paper, we investigate the optimal recursive power-allocation policies with energy harvesting wireless nodes equipped with multiple antennas in a fading channel. This optimization problem includes several complex matrices as optimization variables. As a difference, existing optimization theory and methods have been designed to solve these problems over real space. Naturally, the optimization variables have been assumed points in the real space. We proposed a transform approach and designed the algorithms for solving the throughput maximization problem and transmission completion time minimization problem for a multiple-input–multiple-output (MIMO) system. The algorithms were further extended to solve the throughput maximization problem of a hybrid system with both harvesting energy and grid power. Numerical results illustrated the algorithm steps and significant efficiency of the proposed algorithms. To the best of the authors' knowledge, there are no existing algorithms reported in the open literature to obtain exact solutions to the proposed problems. Significant features of the proposed algorithms are that 1) they provide the exact optimal solutions via efficient finite computation and that 2) optimality of the proposed algorithms is strictly proven.

On the performance of cognitive full-duplex relaying under spectrum sharing constraints

In this paper, a new full-duplex (FD) relaying scheme for a cooperative cognitive underlay network is proposed. The secondary network is composed of one secondary transmitter, one full-duplex secondary relay, and one secondary destination. The relay employs the selective-decode-and-forward (SDF) protocol. The secondary destination jointly decodes the signals from the secondary transmitter and the FD secondary relay so that the direct link can be seen as useful information rather than interference. The analysis includes the effect of the interference from the primary transmitter and the self-interference at the relay. Under equal power allocation strategy, closed-form expressions for the outage probability are derived for the proposed FD cooperative cognitive scheme, and the feasibility of FD relaying under cognitive constraints is shown. Our results also reveal that the proposed full-duplex joint-decoding (FDJD) cognitive network considerably outperforms the known full-duplex dual-hop (FDDH) scheme. Moreover, we propose an optimal power allocation (OPA) scheme. On the basis of the signal-to-interference-plus noise of the secondary network, the OPA strategy can choose between two modes of operation, cooperation between source and relay or source transmission only. Our results show that the FDJD scheme under the proposed OPA policy presents the best performance among all schemes investigated in this paper.

Linear precoding design for MIMO-PLC systems

In this paper, we consider the design of linear precoding in Multiple Input Multiple Output (MIMO) Power Line Communication (PLC) systems with finite-alphabet input. First, we derive mutual information of MIMO-PLC systems with impulsive noise. Considering the non-concavity of the objective function and the low-cost requirement of PLC systems, we choose the lower bound of the mutual information as the objective function. Subsequently, we propose a novel approach to design the precoding scheme to reduce computational complexity. Specifically, our work primarily includes the following two contributions: (1) We design the right unitary matrix that is a product of two fixed unitary matrices, which only depends on the modulation mode. Hence, the results can be saved and require less computations. (2) For the power allocation matrix, we first reduce the space of power allocation using constraints of the optimal power allocation policy. Consequently, we propose a non-linear search method to obtain the optimal power allocation in small space. In regards to the computational complexity of the analysis, we conclude that the proposed precoding matrix design scheme has low complexity and is easy to implement. Moreover, the numerical results are proven to demonstrate the performance of the proposed precoding design scheme.

Optimal power allocation on discrete energy harvesting model

Abstract This paper studies the power allocation problem in energy harvesting systems with finite battery. We adopt the discretized energy arrival and power allocation model. Hence, the service process can be modeled as a finite state Markov chain. Based on the discretized model, we analyze the stationary distribution of the Markov chain and formulate the utility maximization problem, which is then reformed as a linear programming problem. By analyzing the linear programming problem, we provide some intuition on the structure of the optimal power allocation policy and find the condition in which the greedy power allocation is optimal. Numerical simulations show the influence of the energy arrival process on the optimal power allocation policy, and the results are consistent with our analysis.

Power allocation and transmitter switching for broadcasting with multiple energy harvesting transmitters

With the advancement of battery technology, energy harvesting communication systems attracted great research attention in recent years. However, energy harvesting communication systems with multiple transmitters and multiple receivers have not been considered yet. In this paper, the problem of broadcasting in a communication system with multiple energy harvesting transmitters and multiple receivers is studied. First, regarding the transmitters as a ‘whole transmitter,’ the optimal total transmission power is obtained and an optimal power allocation policy is extended to our system setup, with the aim of minimizing the transmission completion time. Then, a simpler power allocation policy is developed to allocate the optimal total transmission power to the data transmissions. As transmitter switching can provide flexibility and robustness to an energy harvesting communication system, especially when a transmitter is broken or the energy harvested by a transmitter is insufficient, a transmitter switching policy is further developed to choose a suitable transmitter to work whenever necessary. The results show that the proposed power allocation policy performs close to the optimal one and outperforms some heuristic ones in terms of transmission completion time. Besides, the proposed transmitter switching policy outperforms some heuristic ones in terms of number of switches.

Energy-Efficient RSSI-Based Localization for Wireless Sensor Networks

Sensor positioning is a fundamental block in many location-dependent applications of wireless sensor networks. Although the main objective in localization is primarily enhancing the positioning accuracy, the importance of energy consumption and localization accuracy poses new challenges. The localization is usually assisted with some self-known position sensors called anchor nodes. In this letter, optimal power allocation for the anchor nodes in a sense of minimizing the energy consumption considering estimation errors is investigated. To have a better estimation of the relative distance between the anchor and unknown nodes using received signal strength indicator (RSSI), average energy of the received beacon is introduced as a new decision metric. Based on this, a squared position error bound as an accuracy parameter is derived, and an optimization problem is proposed to maximize the localization performance. More specifically, the optimal power allocation policy is first derived for the case that the anchor nodes estimate their own locations with no error. Since there are unavoidable errors in the positions of the anchor nodes, the optimization problem is then modified by including uncertainty in the positions of the anchor nodes. The results show that a substantial reduction in power consumption can be achieved by optimal allocation of the transmission power.

Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time-frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time-frequency power-resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations.

Optimal Power Allocation in an "Off" Spectrum Sensing Interval for Cognitive Radio

Most of the current works about cognitive radio favor a constant power allocation policy during an off spectrum sensing interval, which is the time between two spectrum sensing activities when the outcome of the first spectrum sensing indicates off. However, according to our work, this policy becomes suboptimal with respect to bit error rate minimization, when the traffics of primary user follow an alternating renewal process. Therefore, under this scenario, we suggest the optimal power allocation policy, by solving a convex optimization problem on power allocation of secondary user. Our results provide meaningful insight into the design of transmitting schemes for cognitive radio.

Energy-Efficient Relaying over Multiple Slots with Causal CSI

In many communication scenarios, such as in cellular systems, the energy cost is substantial and should be conserved, yet there is a growing need to support many real-time applications that require timely data delivery. To model such a scenario, in this paper we consider the problem of minimizing the expected sum energy of delivering a message of a given size from a source to a destination subject to a deadline constraint. A relay is present and can assist after it has decoded the message. Causal channel state information (CSI), in the form of present and past SNRs of all links, is available for determining the optimal power allocation for the source and relay. We obtain the optimal power allocation policy by dynamic programming and explore its structure. We also obtain conditions for which the minimum expected sum energy is bounded given a general channel distribution. In particular, we show that for Rayleigh and Rician fading channels, relaying is necessary for the minimum expected sum energy to be bounded. This illustrates the fundamental advantage of relaying from the perspective of energy efficient communications when only causal CSI is available. Numerical results are obtained which show the reduction in the expected sum energy under different communication scenarios.

Optimal Power Allocation for Energy Harvesting and Power Grid Coexisting Wireless Communication Systems

This paper considers the power allocation of a single-link wireless communication with joint energy harvesting and grid power supply. We formulate the problem as minimizing the grid power consumption with random energy and data arrival in fading channel, and analyze the structure of the optimal power allocation policy in some special cases. For the case that all the packets are arrived before transmission, it is a dual problem of throughput maximization, and the optimal solution is found by the two-stage water filling (WF) policy, which allocates the harvested energy in the first stage, and then allocates the power grid energy in the second stage. For the random data arrival case, we first assume grid energy or harvested energy supply only, and then combine the results to obtain the optimal structure of the coexisting system. Specifically, the reverse multi-stage WF policy is proposed to achieve the optimal power allocation when the battery capacity is infinite. Finally, some heuristic online schemes are proposed, of which the performance is evaluated by numerical simulations.

Power Control for Two User Cooperative OFDMA Channels

For a two user cooperative orthogonal frequency division multiple access (OFDMA) system with full channel state information (CSI), we obtain the optimal power allocation (PA) policies which maximize the rate region achievable by a channel adaptive implementation of inter-subchannel block Markov superposition encoding (BMSE), used in conjunction with backwards decoding. We provide the optimality conditions that need to be satisfied by the powers associated with the users' codewords and derive the closed form expressions for the optimal powers. We propose two algorithms that can be used to optimize the powers to achieve any desired rate pair on the rate region boundary: a projected subgradient algorithm, and an iterative waterfilling-like algorithm based on Karush-Kuhn-Tucker (KKT) conditions for optimality, which operates one user at a time and converges much faster. We observe that, utilization of power control to take advantage of the diversity offered by the cooperative OFDMA system, not only leads to a remarkable improvement in achievable rates, but also may help determine how the subchannels have to be instantaneously allocated to various tasks in cooperation. Simulations show that the gain from power allocation is still significant even when the CSI feedback is limited.

动态能量获取下无线通信的能量管理与资源优化

Due to the advantages of environment friendliness, easy and flexible deployment, utilizing renewable energy for wireless communications has attracted extensive attention recently. Since the renewable energy is not stable, and the capacity of batteries is limited, it is hard to predict and optimize the utilization of the energy for transmission. The double-randomness due to energy arrival and channel variation makes it a challenging job to efficiently exploit renewable energy for wireless communications. This article reviews the current research status, based on which the authors provide some insights on how to perform energy management and resource optimization for wireless communications under dynamic energy harvesting. Moreover, the authors introduce some initial results on the power allocation for a wireless communication link driven by both renewable energy and power grid. Particularly, the offline optimal power allocation policy is derived, and the extension to online algorithms is discussed.

Power allocation policies with full and partial inter-system channel state information for cognitive radio networks

This paper investigates several power allocation policies in orthogonal frequency division multiplexing -based cognitive radio networks under the different availability of inter-system channel state information (CSI) and the different capability of licensed primary users (PUs). Specifically, we deal with two types of PUs having different capabilities: a dumb (peak interference-power tolerable) PU and a more sophisticated (average interference-power tolerable) PU. For such PU models, we first formulate two optimization problems that maximize the capacity of unlicensed secondary user (SU) while maintaining the quality of service of PU under the assumption that both intra- and inter-system CSI are fully available. However, due to loose cooperation between SU and PU, it may be difficult or even infeasible for SU to obtain the full inter-system CSI. Thus, under the partial inter-system CSI setting, we also formulate another two optimization problems by introducing interference-power outage constraints. We propose optimal and efficient suboptimal power allocation policies for these four problems. Extensive numerical results demonstrate that the spectral efficiency achieved by SU with partial inter-system CSI is less than half of what is achieved with full inter-system CSI within a reasonable range of outage probability (e.g., less than 10 %). Further, it is shown that the average interference-power tolerable PU can help to increase the saturated spectral efficiency of SU by about 20 and 50 % in both cases of full and partial inter-system CSI, respectively.

Sensing Based Joint Rate and Power Allocations for Cognitive Radio Systems

In this paper, we propose a spectrum sensing based joint rate and power allocation scheme for Cognitive Radio (CR) systems. By exploiting both its sensing result and sensing accuracy, the Secondary User (SU) adaptively determines its transmission policy to maximize its effective throughput which is achievable without requiring any Primary User's (PU's) true state information and thus is more practical than the performance metric of ergodic capacity used by . We propose an efficient algorithm to find the optimal SU rate and power allocation policy by exploiting the special structure of the problem. Based on the optimal policy, we further quantify sufficient conditions under which it is beneficial for the SU to use the PU channel even the SU sensing result indicates that the PU is busy by anticipating the happening of sensing error (but still with the PU interference constraint satisfied). Our results validate the advantages of exploiting both the information on sensing result and sensing accuracy in the overlay secondary spectrum access.

Transmission Strategies in Multiple-Access Fading Channels With Statistical QoS Constraints

Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical queueing constraints, is analyzed in a multiuser scenario. In particular, the effective capacity region of fading multiple-access channels in the presence of quality of service (QoS) constraints is studied. Perfect channel side information is assumed to be available at both the transmitters and the receiver. It is initially assumed that the transmitters send the information at a fixed power level and, hence, do not employ power control policies. Under this assumption, the performance achieved by superposition coding with successive decoding techniques is investigated. It is shown that varying the decoding order with respect to the channel states can significantly increase the achievable throughput region. In the two-user case, the optimal decoding strategy is determined for the scenario in which the users have the same QoS constraints. The performance of orthogonal transmission strategies is also analyzed. It is shown that for certain QoS constraints, time-division multiple access can achieve better performance than superposition coding if fixed successive decoding order is used at the receiver side. In the subsequent analysis, power control policies are incorporated into the transmission strategies. The optimal power allocation policies for any fixed decoding order over all channel states are identified. For a given variable decoding-order strategy, the conditions that the optimal power control policies must satisfy are determined, and an algorithm that can be used to compute these optimal policies is provided.

Spectrum sharing in cognitive radio systems: service-oriented capacity and power allocation

In this study, the authors investigate different capacity notions in cognitive radio systems where the transmission parameters of the cognitive users are adaptively changed based on the availability of (i) channel state information (CSI) pertaining to the cognitive (secondary) user link, and (ii) soft-sensing information (SSI) about the activity of the licensed-band (primary) user as obtained from the sensing detector at the secondary user's transmitter. Using statistics of available SSI and CSI at the secondary transmitter, the cognitive user adopts a transmission policy that maximises the achievable capacity under appropriate system constraints. Assuming above considerations in a cognitive radio system operating under average received-interference and peak transmit-power constraints, the authors study three different capacity notions of spectrum-sharing fading channels – namely, ergodic, delay-limited and service-rate with/without outage – and obtain their corresponding optimal power allocation policies. In particular, in this study, the authors obtain the optimal power allocation policies to achieve the channel capacity of the secondary link for the appropriate transmission policy which satisfies the spectrum-sharing system requirements in each of the above-mentioned scenarios, and further provide expressions for the capacity metrics. They also provide illustrative numerical simulations sustaining our theoretical results.

Capacity and Power Allocation for Cognitive MAC with Imperfect Channel Estimation

This paper presents a lower-bound on the ergodic sum capacity of single-input single-output (SISO) opportunistic spectrum sharing multiple access channel (MAC) in Rayleigh fading channels with imperfect channel estimates. Unlike the previous studies, this paper considerers the channel estimation errors for both sets of SISO channels between the secondary user (SU) transmitters and the SU receiver and between the SU transmitters and the primary user receivers. By considering these channel estimation errors, an optimal power allocation policy for each SU transmitter is derived to achieve a lower-bound of the ergodic sum capacity of opportunistic spectrum sharing MAC, subject to a set of SUs transmit power constraints and a set of interference power constraints at each PU receiver. Furthermore, convex optimization techniques are exploited to derive an efficient algorithm to obtain the optimal power allocation for each SU transmitter. Numerical results are presented to illustrate the influences of the channel estimation errors on the ergodic sum capacity.

Resource Management in Spectrum-Sharing Cognitive Radio Broadcast Channels: Adaptive Time and Power Allocation

In this paper, we consider a primary/secondary spectrum-sharing system, and study adaptive resource management in cognitive radio (CR) fading broadcast channels (BC). Specifically, we propose utilizing spectrum sensing information about the primary's activity at the secondary base station for an efficient allocation of the resources, namely, transmission time and power, to the secondary users. Spectrum sensing information about the primary user, and secondary channel side information, are assumed available at the base station and receivers of the secondary CR network. The sensing information is obtained using spectrum-aware sensors deployed in the secondary network coverage area. Based on this information, we present an optimal time-sharing and power allocation policy to maximize the achievable capacity of fading cognitive radio broadcast channels, where transmission is limited by appropriate constraints on the average received-interference at the primary receiver and peak transmit-power pertaining to the secondary transmitter. Furthermore, considering that availability of full soft-sensing information at the CR transmitter may result in a severe load of feedback data and high system complexity, we present a quantized spectrum sensing mechanism wherein only restricted levels of primary activity are considered for the sensing observations. Our theoretical results are sustained by numerical and simulation analyses, and insightful discussions are provided.