Minimum Total Transmission Power Research Articles

Simultaneous Lightwave Information and Power Transfer in Visible Light Communication Systems

In this paper, we investigate a novel simultaneous lightwave information and power transfer (SLIPT) in visible light communication (VLC) systems, where a photo diode (PD) and a solar panel are utilized as the information receiver and the energy harvester, respectively. By systematically analyzing both the information receiver and the energy harvester, we obtain the explicit expressions to characterize the illumination-rate-energy region. Based on the derived expressions, we investigate the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and study the total transmit power minimization problem under the rate requirements, the minimum energy harvesting requirements, and dimming control constraints. To solve such non-convex problem, we exploit the semidefinite relaxation (SDR) technique and relax the problem into a convex problem, which can be efficiently solved via interior-point methods. Moreover, for the sake of users’ fairness, we further investigate the beamformer design to maximize the minimal rate under both minimum energy harvesting and dimming control constraints. Finally, the numerical results are provided to evaluate the proposed SLIPT system.

On Total Transmission Power Minimization Approach to Decentralized Beamforming in Single-Carrier Asynchronous Bidirectional Relay-Assisted Communication Networks

Considered in this paper is a single-carrier asynchronous bidirectional cooperative network with amplify-and-forward relays helping two transceivers to exchange information. The network is asynchronous in the sense that the delays of propagation along different relaying paths are significantly different from each other. As a result, the channel between the two transceivers is best modeled with a multi-tap impulse response, which can cause interference between adjacent symbols at the end nodes. In a block-wise communication scheme, a cyclic prefix can be appended to each transmitted block of information symbols to avoid interference between adjacent blocks. To suppress interference within each block, however, the network parameters, namely, the relay complex weights and the transceivers’ transmit powers should be judiciously chosen. To do so, we herein minimize, over these parameters, the total transmission power consumption throughout the network, under two constraints which ensure that the transceivers’ data rates are above two given thresholds. It is herein proved that solving this total transmission power minimization problem leads to the impulse response of end-to-end channel having only a single non-zero tap. Indeed, our analysis shows that only the relays associated with this non-zero tap have to be selected to participate in the information exchange between the two transceivers. We present a simple 1-D integer search algorithm for optimally determining the index of the non-zero tap of the channel impulse response of the end-to-end channel. We also present computationally simple semi-closed-form expressions for the optimal values of the design parameters. Our numerical results show that under identical rate thresholds, in our power allocation scheme, for any channel realization , half of the power budget is allocated to the two transceivers and the remaining half is shared among all the relay nodes.

Multi-Quality Multicast Beamforming With Scalable Video Coding

In this paper, we consider multi-quality multicast beamforming of a video stream from a multi-antenna base station to multiple single-antenna users receiving different qualities of the same video stream, via scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding as well as successive interference cancelation, we propose a layer-based multi-quality multicast beamforming scheme. To reduce the computational complexity, we also propose a quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality information of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding optimal beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the layer-based power minimization problem is the same as that of the quality-based power minimization problem, although the latter incurs a lower computational complexity. Next, we consider the optimal joint layer selection and quality-based multi-quality multicast beamforming design to maximize the total utility representing the satisfaction with the received video quality for all users under a given maximum transmission power budget, which is NP-hard in general. Based on the optimal solution of the quality-based power minimization problem, we develop a greedy algorithm to obtain a near optimal solution. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

SDQ-6WI: Software Defined Quadcopter-Six Wheeled IoT Sensor Architecture for Future Wind Turbine Placement

Although wind-generated power was estimated to be 4% of the entire world electricity usage, wind turbines are considered to be a growing technology with many experts considering new approaches to wind turbine design and farmland selection to increase the wind turbine output efficiency. When implementing a new wind turbine install on a farm, many concerns are taken into consideration such as environmental challenges and cost. Thus, the power of the wind turbine can be increased at least 10 times when the most efficient wind power location is selected before the wind turbine is placed. In this paper, we propose a novel software-defined quadcopter–6 wheeled industrial IoT (SDQ-6WI) architecture that is based on a developed quadcopter system to collect wind speed data from a mobile IoT vehicle base station that is based on a developed open flow (DOV) protocol operation. The mobile ground vehicle act as a wind speed measurement system that travels on a given set of waypoints to measure the best optimal wind speed quality and send the collected information to the quadcopter-based SDN controller then to the cloud for further processing. Our proposed system can handle a heterogeneous environment that lacks Wi-Fi and cellular coverage and uses the minimum total transmission power when sending data. The experiential results showed that the measured wind speed data could be collected in a time-efficient manner compared to a traditional process which is considered to be costly, time wasting, and non-effective. Our extensive testing showed that about 23.19% in power was reduced in the wind measurement process in comparison with the fixed sensor nodes. In essence, the proposed architecture help reduce the high cost of relocating wind turbines to an efficient location and increase the generated power by selecting the best optimal windy location.

Relay selection and power allocation for energy-efficient cooperative cognitive radio networks

In this paper, we apply the innovative multi-objective optimization methods to the challenge posed by rate maximization, total transmission power minimization and relay selection in cooperative cognitive radio networks. The proposed methods which are based on amplify and forward relaying strategy optimize the three conflicting objectives and, at same time, they maximize the rate quality, minimize the total transmission power allocated to the network relays and make the best relay node selection. The multi-objective optimization studied is a non-convex non-linear combinatorial algorithm which is converted to its convex smooth equivalent through two efficient approximation methods. We apply the multi-objective lexicographic method to overcome the challenge posed by these conflicting objectives simultaneously. The proposed relay node selection method is based on zero-norm principle which provides an effective technique to obtain a minimum node selection. Simulation results confirm that the proposed approaches offer superior performance over known schemes in terms of throughput gain and number of active relays.

Multicast Beamforming Design in Multicell Networks With Successive Group Decoding

We consider a generic problem of multicast beamforming design in multicell networks where each base station (BS) has multiple independent messages to multicast and each user intends to decode an arbitrary subset of messages sent from all BSs using successive group decoding (SGD). We first formulate the total transmit power minimization problem subject to the constraints that a target rate vector is achievable by the SGDs at all receivers. This problem is a non-convex quadratically constrained quadratic program and NP-hard. We propose a new method based on solving a sequence of linearly regularized semi-definite programming (SDP) relaxation of the original problem that yields feasible and near-optimal solutions with high probability. Moreover, we propose a decentralized algorithm based on the alternating direction method of multipliers to solve each linearly regularized SDP, which consists of solving a quadratic program at the central controller, and closed-form analytic computations at each BS. Finally, we propose an iterative procedure for joint beamformer and rate optimization under the SGD framework. Numerical results confirm the superiority of the proposed beamformer design in both performance and complexity. It is also demonstrated that, compared with the traditional linear receivers, the SGD receivers achieve both significant rate improvement and energy savings.

A Two-Way Network Beamforming Approach Based on Total Power Minimization With Symmetric Relay Beamforming Matrices

We study the total transmit power minimization problem for a two-way relay network under two constraints on the transceivers’ received signal-to-noise-ratios. The network considered herein consists of multiple multi-antenna relay nodes and two single-antenna transceivers. Each relay transforms the vector of its received signals, by multiplying this vector with a complex beamforming matrix, thereby obtaining a new vector whose entries are transmitted over different antennas of that relay. Assuming the relay beamforming matrices and the transceivers’ transmit powers as the design parameters, we first study the total power minimization problem under the assumption that the relay beamforming matrices are symmetric. Under such an assumption, we show that the total power minimization problem is amenable to a semi-closed-form solution, and thus, it can be solved efficiently. We then consider the case, where the relay beamforming matrices may not be symmetric and show that in this case, the total power minimization problem can be solved using a computationally prohibitive algorithm which involves a 2-D search over a grid in the space of the transceivers’ transmit powers and semi-definite programming at each vertex of this grid. Our numerical results show that the symmetric assumption on the relay beamforming matrices incurs only insignificant loss, while this assumption allows us to significantly reduce the computational burden of solving the total power minimization problem.

Adaptive Resource Allocation Algorithm Based on Minimize Average Bit-Error-Rate for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier modulation method for wireless systems. However, the OFDM communication system is sensitive to inter-carrier interference (ICI), which arises because of Doppler spread and carrier frequency offset. This study addresses the problem of resource management in the presence of ICI and multipath fading channels. This study developed a minimized average bit error rate-based method that adaptively allocates the subcarrier, bit, and power in OFDM systems to obtain minimum total transmission power under certain quality of service requirements (i.e., the minimum data rate and the bit error rate). The simulation results demonstrated that the proposed suboptimal algorithms can substantially improve the performance of the OFDM systems compared with the uniform power allocation algorithm or conventional water-filling algorithm. Furthermore, applying the proposed resource allocation algorithm was demonstrated to distribute the sum capacity more fairly and flexibly among users than the sum capacity maximization method did.

Robust Power Allocation for OFDM Based Underlay Cognitive Radio Networks with Channel Uncertainties

In consideration of all possible channel uncertainties without the assumption of perfect channel state information (CSI), a robust power allocation algorithm for underlay orthogonal frequency division multiple cognitive radio networks is presented. This algorithm can assign transmission power of each secondary user (SU) on each sub-carrier based on total transmission power minimization of SUs under the constraints corresponding to signal-to-interference-noise ratio of SUs and the interference power constraint to guarantee the quality of service of primary users (PUs). In addition, the CSI errors are assumed to be bounded with ellipsoid and interval sets. Through the worst case approach, the original optimization problem is converted into a convex one solved by Lagrange dual decomposition method. The proposed robust algorithm provides a trade-off between robustness and system performance. Simulation results prove that the suboptimal solution can achieve a satisfactory performance for both SUs and PUs at the same time.

Distributed Beamforming in Two-Way Relay Networks With Interference and Imperfect CSI

This paper studies the problem of optimal beamforming and power allocation for an amplify-and-forward (AF)-based two-way relaying network in the presence of interference and channel state information (CSI) uncertainty. In particular, we obtain the beamforming vector as well as the users’ transmit powers under two assumptions on the availability of the CSI of the interfering links, namely norm-bounded uncertainty model and the second-order statistics scenario. To do so, we develop two design approaches. The first approach is based on the total transmit power minimization technique. We start with the norm-bounded uncertainty model and derive the optimal solution to the corresponding problem. To reduce the computational complexity, we also develop a low-complexity algorithm which offers performance that is very close to the optimal one. In the second approach, we apply a signal-to-interference-plus-noise ratio (SINR) balancing technique. We propose another low-complexity algorithm based on the SINR balancing criteria. Next, we consider the scenario where the second-order statistics of the CSIs are available. Again we start with the total power minimization method and derive both optimal and suboptimal algorithms. Finally, we apply the SINR balancing technique to this scenario and develop another low-complexity algorithm, which is suitable for practice.

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.

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 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.

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.

Joint Power and Admission Control: Non-Convex <formula formulatype="inline"><tex Notation="TeX">$L_{q}$</tex></formula> 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.

Minimum Power Multicast Beamforming With Superposition Coding for Multiresolution Broadcast and Application to NOMA Systems

Multicast beamforming with superposition coding (SC) is studied for multiresolution broadcast where both data streams of high priority (HP) and low priority (LP) are to be transmitted for a user close to a base station (BS), while only data stream of LP is to be transmitted to a user not close to the BS (e.g., a cell-edge user). Using SC, a minimum total transmission power beamforming problem has been formulated to find beamforming vectors and powers for both users. For given normalized beamforming vectors, a closed-form expression for the optimal power allocation is derived from which an iterative algorithm is considered to find beamforming vectors. The proposed multicast beamforming with SC is applied to nonorthogonal multiple access (NOMA) systems to support multiple users as a two-stage beamforming method.

SEER: spectrum- and energy-efficient routing protocol for cognitive radio ad hoc networks

Cognitive radio ad hoc networks (CRAHNs) were developed to improve the utilization ratio of licensed spectrum. Since spectrum opportunities for users are varied over time and location, enhancing the spectrum effectiveness is a goal and also a challenge for CRAHNs. Besides, due to the energy limitation of the devices in CRAHNs, energy efficiency is another goal to be achieved. Therefore, the objective of this paper is to design a spectrum- and energy-efficient routing (SEER) protocol for CRAHNs, which improves transmission efficiency and balances energy consumption. The design of the protocol uses the techniques of expected transmission power, expected residual lifetime and utility indifference curves. The protocol will select the optimal route from all candidate paths. We present a performance comparison with two existing protocols for CARHNs: the minimum total transmission power (MTTP) and the min---max battery cost (MMBC) protocols. Simulation results show that SEER can reduce energy consumption by almost 40 % than that of MMBC, and increase network lifetime by almost 200 % than that of MTTP. Besides, SEER can improve the network throughput by at least 50 % and 150 % than that of MTTP and MMBC respectively.

Performance Analysis of Efficient Energy Routing Protocols in MANET

The rapid evolution in the field of mobile computing is driving a new alternative way in which mobile devices form a self-creating, self-administering and self-organizing wireless networks called Mobile Ad hoc Networks (MANETs). In MANET, the aware of power heterogeneity is an important technical challenging problem to increase the energy efficiency of each node. The mobile nodes in MANETs have different transmission power and power heterogeneity. This paper analyzes the performance evaluation of three Efficient Energy Routing Protocols such as EPAR (Efficient Power Aware Routing Protocol), MTPR (Minimum total Transmission Power Routing) and DSR (Dynamic Source Routing). The Efficient Power Aware Routing protocol (EPAR) mainly considers the node capacity by its remaining battery power and the expected energy spent for forwarding data packets reliably. EPAR uses mini- max formulation method for the selection of the route that has maximum packet delivery ratio at the smallest Residual Battery Power. With different network scenarios, EPAR is dominating in terms of Residual Battery Power, Power Consumption, Network lifetime and Throughput with respect to time and routed data packets.

Distributed Joint Resource and Routing Optimization in Wireless Sensor Networks via Alternating Direction Method of Multipliers

We consider a distributed total transmit power minimization in a multi-hop single-sink data gathering wireless sensor network by jointly optimizing the resource allocation and the routing with given source rates. An inherent coupling in optimal routing and resource allocation is taken into account via cross-layer optimization to increase the energy efficiency of the network. Instead of distributing the solution process horizontally by commonly used dual decomposition, we apply consensus optimization in conjunction with the alternating direction method of multipliers (ADMM). By duplicating flow variables, the problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Numerical examples show that the proposed algorithm converges significantly faster as compared to the state of the art methods based on the dual decomposition. Additionally, the algorithm is appealing for practical implementation due to its low local communication overhead, robust operation in slightly changing channel conditions and scalability to large networks.

Precoding Design for MIMO Relay Multicasting

In this paper, we consider a two-hop multicasting multiple-input multiple-output (MIMO) relay system where one transmitter multicasts common message to multiple receivers with the aid of a relay node, and all nodes are equipped with multiple antennas. Joint transmit and relay precoding design problems are investigated for multicasting multiple data streams based on two design criteria. In the first scheme, we aim at minimizing the maximal mean-squared error (MSE) of the signal waveform estimation among all receivers subjecting to power constraints at the transmitter and the relay node. This problem is highly nonconvex with matrix variables and the exactly optimal solution is very hard to obtain. We develop an iterative algorithm to jointly optimize the transmitter, relay, and receiver matrices through solving convex subproblems. By exploiting the optimal structure of the relay precoding matrix, we then propose a low complexity solution which decouples the optimization of the transmitter and relay matrices under the (moderately) high first-hop signal-to-noise ratio (SNR) assumption. In the second scheme, we propose a total transmission power minimization strategy subjecting to quality-of-service (QoS) constraints. By using the optimal structure of the relay precoding matrix and the (moderately) high first-hop SNR assumption, we show that this problem can be solved using the semidefinite programming (SDP) technique. Numerical simulations demonstrate the effectiveness of the proposed algorithms. Interestingly, we show that for the special case of single data stream multicasting, the relay precoding matrix optimization problem can be equivalently converted to the transmit beamforming problem for single-hop multicasting systems.