Minimum Transmission Power Research Articles

Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer

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.

Power Minimization through Packet Retention in Cognitive Radio Sensor Networks under Interference and Delay Constraints: An Optimal Stopping Approach

The aim of this article is twofold: First, we study the problem of packets retention in a queue with the aim of minimizing transmission power in delay-tolerant applications. The problem is classified as an optimal stopping problem. The optimal stopping rule has been derived as well. Optimal number o

Robust transceiver designs in multiuser MISO broadcasting with simultaneous wireless information and power transmission

In this paper, we address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission, where a multi-antenna base station (BS) sends energy and information simultaneously to multiple users equipped with a single antenna. Assuming that perfect channel-state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an Euclidean ball-shaped uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. Due to the channel uncertainty, the original problem becomes a homogeneous quadratically constrained quadratic problem, which is NP-hard. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Numerical results are provided to validate the robustness of the proposed algorithms.

Power-Performance Tradeoff Analysis of CML-Based High-Speed Transmitter Designs Using Circuit-Level Optimization

This paper presents comprehensive analyses of power-performance tradeoffs in current-mode logic (CML)-based transmitters via equation-based optimization and provides practical design guidelines that can help achieving optimum power efficiency. An accurate equation-based optimization framework was developed for this purpose, and using this framework, key circuit-level design parameters were found with specific design specifications and process technologies. The proposed optimization framework can determine optimum clock edge-rate (i.e., ratio of rise/fall time to clock period) and inter-stage voltage swings that enable significant power savings without affecting performance. Various transmitter design tradeoffs were analyzed for 45-nm, 65-nm, and 90-nm technologies considering data rate, clock edge-rate, and inter-stage swing optimization. Based on the analysis results, we determined the most energy-efficient data rate that can be achieved with acceptable power overhead for each technology node. Also, we identified the optimal clock edge-rate and inter-stage voltage swings in CML gates that achieved minimum transmitter power dissipation. Our analysis results indicate that, when properly optimized, overall transmitter power consumption can be reduced by up to 43% with inter-stage swing optimization with all other design constraints held constant.

Distributed Estimation in Wireless Sensor Networks With an Interference Canceling Fusion Center

In this paper, we consider Distributed Estimation (DES) in a Wireless Sensor Network (WSN) and assume that the number of sensors in the WSN is larger than the available number of transmission slots. With classic DES, the sensors independently transmit the sampled digitized data. However, the WSN is an uplink multiuser channel where multiple sources share the channel for communicating data to a Fusion Center (FC). To this aim, we adopt the optimal communication scheme for this setup that suggests interfering transmissions and the use of Successive Interference Cancelation (SIC) at the FC. We propose a joint SIC decoder and linear Minimum-Mean-Square-Error (MMSE) estimator for digital interfering transmission of correlated data. We further introduce an optimization framework that schedules and allocates power to the sensors optimally. We formulate the problem in two ways: an expected distortion minimization problem under a total power budget, and a transmission power minimization problem under a distortion constraint. For both cases, we consider the system performance under different operating conditions, and we demonstrate the efficiency of the proposed scheme compared to a system that employs optimized sensor selection under orthogonal transmissions.

NEAR-FAR POWER CONTROL PROBLEM IN CDMA NETWORKS

This paper investigates the near-far power problem in CDMA. Power control is vital in a DS-CDMA system, which is to reduce multi-access and near-far problems. In CDMA, the all the mobile users use a common frequency band simultaneously, therefore causing interference with each other. The role of CLPC is to maintain the received signal strength at the required level. The required values are set by the Base station for the purposes of keeping the bit error rate at the required position while maintaining minimal transmission power. The system functionality is shown with BER comparisons. It is shown that CLPC maximizes transmission power and improves system capacity. The proposed power control scheme focuses on estimated signal strengths.

Power minimization for cooperative MIMO-OFDM systems with individual user rate constraints

We propose a continuous rate and power allocation algorithm for multiuser downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with coordinated multipoint (CoMP) transmission that guarantees to satisfy individual rate target across all users. The optimization problem is formulated as a total transmit power minimization problem subject to per-user rate targets and per-antenna power constraints across multiple cooperating base stations. While the per-antenna power constraint leads to a more complex optimization problem, it is a practical consideration that limits the average transmit antenna power and helps to control the resulting high peak powers in OFDM. Our proposed algorithm uses successive convex approximation (SCA) to transform the non-convex power minimization problem and dynamically allocate power to co-channel user terminals. We prove that the transformed power minimization problem is convex and that our proposed SCA algorithm converges to a solution. The proposed algorithm is compared with two alternative approaches: (1) iterative waterfilling (IWF) and (2) zero-forcing beamforming (ZFB) with semi-orthogonal user selection. Simulation results highlight that the SCA algorithm outperforms IWF and ZFB in both medium- and low-interference environments.

Transmit Power Minimization for Wireless Networks With Energy Harvesting Relays

Energy harvesting (EH) has recently emerged as a key technology for green communications as it can power wireless networks with renewable energy sources. However, directly replacing the conventional non-EH transmitters by EH nodes will be a challenge. In this paper, we propose to deploy extra EH nodes as relays over an existing non-EH network. Specifically, the considered non-EH network consists of multiple source-destination (S-D) pairs. The deployed EH relays will take turns to assist each S-D pair, and energy diversity can be achieved to combat the low EH rate of each EH relay. To make the best of these EH relays, with the source transmit power minimization as the design objective, we formulate a joint power assignment and relay selection problem, which, however, is NP-hard. We thus propose a general framework to develop efficient sub-optimal algorithms, which is mainly based on a sufficient condition for the feasibility of the optimization problem. This condition yields useful design insights and also reveals an energy hardening effect, which provides the possibility to exempt the requirement of non-causal EH information. Simulation results will show that the proposed cooperation strategy can achieve near-optimal performance and provide significant power savings. Compared to the greedy cooperation method that only optimizes the performance of the current transmission block, the proposed strategy can achieve the same performance with much fewer relays, and the performance gap increases with the number of S-D pairs.

Joint relay selection and power control for robust cooperative multicast in mmWave WPANs

We propose an optimization algorithm for joint relay selection and source and relay power allocation under mixed line-of-sight (LoS) and non-LoS path scenarios for both power saving and robustness enhancement of cooperative multicast in millimeter-wave wireless personal area networks. Our aims are to reduce power consumption and enhance the robustness of cooperative multicasts in millimeter-wave wireless personal area networks. First, we describe a novel beam training protocol that is capable of overhearing and information feedback to filter relay candidates with non-LoS links and avoid selecting relays for transceivers with LoS paths. Second, the joint relay selection and power allocation issue is formulated as an optimization problem with the objective of minimizing the maximum combined power consumption of the source and relay under maximum tolerable outage probabilities and transmit powers. By introducing relaxation and Lagrange multiplier methods, a closed-form expression for the joint relay selection and power allocation is obtained. Finally, simulation results indicate significant improvements in terms of both outage probability and power consumption over the conventional combined transmit power minimization algorithm.

Transmit Power Minimization and Base Station Planning for High-Speed Trains with Multiple Moving Relays in OFDMA Systems

A high-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via orthogonal frequency-division multiple-access (OFDMA) technology. In this paper, we first focus on minimizing the total downlink power consumption of the base station (BS) and the moving relays while respecting specific quality-of-service (QoS) constraints. We first derive the optimal resource-allocation solution, in terms of OFDMA subcarriers and power allocation, using the dual decomposition method. Then, we propose an efficient algorithm based on the Hungarian method to find a suboptimal but low-complexity solution. Moreover, we propose an OFDMA planning solution for high-speed trains by finding the maximal inter-BS distance, given the required user data rates to perform seamless handover. Our simulation results illustrate the performance of the proposed resource-allocation schemes in the case of Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) and compare them with previously developed algorithms, as well as with the direct transmission scenario. Our results also highlight the significant planning gain obtained, owing to the use of multiple relays instead of the conventional single-relay scenario.

Weighted Sum Transmit Power Minimization for Full-Duplex System With SWIPT and Self-Energy Recycling

This correspondence considers a full-duplex (FD) point-to-point system consisting of one multi-antenna full-duplex access point (FD-AP) and one two-antenna full-duplex mobile station (FD-MS). We adopt simultaneous wireless information and power transfer (SWIPT) scheme and apply the self-energy recycling at FD-MS. In order to minimize the weighted sum transmit power, we jointly design the transmit beamforming vector of FD-AP, the receive power splitting (PS) ratio of FD-MS, as well as the transmit power value of FD-MS. Since the original problem is non-convex, we apply semidefinite relaxation (SDR) and obtain a new convex problem. We further prove that both problems have exactly the same solutions. Finally, simulations are provided to verify our analysis, and the comparison with a half-duplex (HD) system demonstrates the significant performance gain from self-energy recycling.

A Categorized Resource Sharing Mechanism for Device-to-Device Communications in Cellular Networks

Device-to-Device (D2D) communications are considered one of the key technologies for 5G wireless communication systems. In this paper, a resource sharing mechanism, which applies different policies for different cases (thus being categorized), is proposed. In this scheme, all D2D pairs are divided into three groups by comparing the minimum transmit power with the maximum transmit power of each cellular UE. The proposed mechanism enables multiple D2D pairs in the second group to share the resource with cellular user equipment (UE) simultaneously, by adjusting the transmit powers of these D2D transmitters. At the same time, D2D pairs in the first group and the third group share resource with cellular UE based on the transmit power minimization principle. Simulation results show that the proposed scheme can achieve relatively higher network throughput and lower transmit power consumption of the D2D system.

Achieving High Energy Efficiency and Physical-Layer Security in AF Relaying

For transmitting data in a secret and energy-efficient manner in collaborative amplify-and-forward relay networks, the secure energy efficiency (EE) defined as the secret bits transferred with unit energy is maximized to satisfy each node power constraint and target secrecy rate requirement, based on physical security framework. The secure EE is maximized by joint source and relay power allocation, which is a nonconvex optimization problem. To cope with this difficulty, a solution scheme and corresponding algorithms are developed by jointly applying fractional programming, exact penalty, alternate search, and difference of convex functions programming. The key idea of the scheme is to convert the primal problem into simple subproblems step by step, such that related methods are adopted. It is verified that, compared with secrecy rate maximization, the proposed scheme improves the secure EE significantly yet with a certain loss of the secrecy rate due to the tradeoff between secure EE and secrecy rate. Furthermore, the proposed scheme achieves higher secure EE and secrecy rate than total transmission power minimization does, while with a certain increase of power consumption. These results indicate that a reasonable balance among secure EE, secrecy rate, and power consumption can be reached by the proposed scheme.

Transmit Power Minimization using Fuzzy Rule based System in Relay Assisted Cognitive Radio Networks

power minimization is one of the major research challenges in Relay-Assisted Cognitive Radio Networks. In this process, the transmit power of each individual relay is adjusted in such a way that the overall transmit power consumption at the relay network is minimized while satisfying the minimum Quality-of-Service (QoS) requirements of primary and secondary networks. In this paper, a similar constrained optimization problem is focused in which a secondary source-destination pair is assisted by a potential relay network having Cognitive Radio capabilities. A Fuzzy Rule Based System (FRBS) is proposed for intelligent relay selection such that total transmit power at the relay network is minimized while achieving the desired signal-to-noise ratio (SNR) at the destination and keeping the primary communication undisturbed. The effectiveness of the proposed scheme is highlighted through simulation results. KeywordsRadio Network, Underlay Spectrum Sharing, Cooperative Communication, Amplify-and-Forward, Fuzzy Rule Based System

A multicast delivery approach with minimum energy consumption for wireless multi-hop networks

Multicast delivery in wireless multi-hop networks has become the popular research topic and holds the important applications such as sensor and tactical networks. However, how to minimize multicast energy consumption and prolong the lifetime of multicast connection in wireless multi-hop networks with limited energy is a challenge at present. This paper presents a new approach to solve this problem by considering the cognitive ability of nodes. Above all, we exploit the directional reception antennas to propose a directional reception two-step reconstruction routing scheme to set up multicast tree for wireless multi-hop networks. Different from previous methods, we grant the cognitive ability to each node so that they can obtain the minimum transmission power by sensing, learning, acting, and deciding. We propose two algorithms to find the minimum transmission power of all the nodes in the multicast tree built above. And a global optimal multicast delivery algorithm with the minimum energy consumption is proposed to implement effective multicast communication for wireless multi-hop networks with energy limited. Numerical experiments show that the proposed approach can significantly improve the multicast performance of wireless multi-hop networks with energy limited such as the lifetime of multicast connection and transmission power.

Transmit Power Minimization in Small Cell Networks Under Time Average QoS Constraints

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ≥ 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.

Distributed Power Allocations in Heterogeneous Networks With Dual Connectivity Using Backhaul State Information

Long Term Evolution (LTE) release 12 proposes the use of dual connectivity in heterogeneous cellular networks, where a user equipment (UE) maintains parallel connections to a macrocell base station and to a low-tier node such as a picocell base station or relay. In this paper, we propose distributed multi-objective power control where each UE independently adapts its transmit power on its dual connections, where the uplinks could be of unequal bandwidth and have non-ideal backhaul links. In the proposed optimization, the UEs dynamically switch between data rate maximization and transmit power minimization as the backhaul load varies. To address the coupling between interference and the backhaul load, we propose a low-overhead convergence mechanism which does not require explicit coordination between UEs and also derive a closed-form expression of the transmit power levels at equilibrium. Simulation results show that our scheme achieves higher aggregate end-to-end data rate and significant power saving in comparison to a scheme that employs a greedy algorithm and a scheme that employs only waterfilling.

Joint Power and Admission Control: Non-Convex &lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;$L_{q}$&lt;/tex&gt;&lt;/formula&gt; Approximation and An Effective Polynomial Time Deflation Approach

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal to interference plus noise ratio (SINR) targets while using a minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex $\ell_q$-minimization approximation of an equivalent sparse $\ell_0$-minimization reformulation of the JPAC problem where $q\in(0,1).$ We show that, for any instance of the JPAC problem, there exists a $\bar q\in(0,1)$ such that it can be exactly solved by solving its $\ell_q$-minimization approximation problem with any $q\in(0, \bar q]$. We also show that finding the global solution of the $\ell_q$ approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an $\epsilon$-KKT solution of the NP-hard $\ell_q$-minimization approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support.

A quality of service distributed optimizer for Cognitive Radio Sensor Networks

In Cognitive Radio Sensor Networks (CRSNs), a sensor node is provided with a cognitive radio unit to overcome the problem of frequency spectrum being crowded. Sensor nodes sense frequency gaps for Primary Users (PUs) to work as Secondary Users (SUs). However, Quality of Service (QoS) requirements for sensor nodes such as maximizing throughput and minimizing transmission power conflicts with minimizing interference between sensor nodes and PUs. Existing works have optimized QoS parameters considering frequency interference problem using Genetic Algorithms (GA) and Simulating Annealing (SA). In this paper, a distributed optimizer for CRSNs based on advanced multi-objective evolutionary algorithms named Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed. A set of accurate fitness functions for NSGA-II implementation that fully control evolution of the algorithm have been employed. To the best of our knowledge, there is no published research in CRSN that contains all these intrinsic fitness functions in one system model. Simulation results show that the proposed optimizer can work as a distributed solution for CRSNs because it achieves a minimum number of iterations and minimum coverage time to reach an optimal solution compared to GA and SA. Such minimization matches the energy requirement for the underlying sensor nodes.

Some applications of polynomial optimization in operations research and real-time decision making

We demonstrate applications of algebraic techniques that optimize and certify polynomial inequalities to problems of interest in the operations research and transportation engineering communities. Three problems are considered: (1) wireless coverage of targeted geographical regions with guaranteed signal quality and minimum transmission power, (2) computing real-time certificates of collision avoidance for a simple model of an unmanned vehicle (UV) navigating through a cluttered environment, and (3) designing a nonlinear hovering controller for a quadrotor UV, which has recently been used for load transportation. On our smaller-scale applications, we apply the sum of squares (SOS) relaxation and solve the underlying problems with semidefinite programming. On the larger-scale or real-time applications, we use our recently introduced “SDSOS Optimization” techniques which result in second order cone programs. To the best of our knowledge, this is the first study of real-time applications of sum of squares techniques in optimization and control. No knowledge in dynamics and control is assumed from the reader.