Power Minimization Problem Research Articles

Full-Duplex Backscatter Communications in Symbiotic Radio Systems

In this paper, we are interested in a symbiotic radio (SR) system, in which a passive full-duplex backscatter device (BD) is parasitic in an active primary transmission. The primary transmitter (PT) with multiple antennas is designed to broadcast common messages to the primary receiver (PR) and the BD, as well as to support passive information transmission from the BD to the PR. To do so, the full-duplex BD absorbs a fraction of the incident signal from the PT to decode the common messages and simultaneously transmits its own information to the PR by backscattering the remaining part of the incident signal. We derive the achievable rates of the BD transmission with Gaussian and quadrature amplitude modulation codewords. We also formulate a transmit power minimization problem by jointly designing the beamforming vector at the PT and the power splitting factor at the BD. This problem is first solved by the semi-definite relaxation technique together with a one-dimensional linear exhaustive search over the power splitting factor. Then, a suboptimal but low-complexity solution with closed-form expressions is proposed. The simulation results have shown that the proposed suboptimal solution achieves almost the same performance as that obtained by the exhaustive search. In addition, our proposed SR system with the full-duplex BD outperforms the half-duplex system with the time-division-multiplexing mode in general.

Energy Optimization Algorithms for MIMO-OFDM Based Downlink C-RAN System

In this paper, we consider a green communication design in a multiple-input-multiple-output orthogonal frequency-division multiplexing-based downlink cloud radio access network system with per-user quality of service constraints and remote antenna units (RAUs) power constraints. Two problems are solved in this paper: a total transmits power minimization problem and a total network consumption minimization problem. The first problem is to minimize the total transmit power of RAUs by designing beamforming. While the other is to minimize the total network consumption by beamforming and switching off unnecessary RAUs, which is a joint design of sparse beamforming and RAU selection. It is a mixed integer nonlinear programming problem and hard to deal with. Approximations are made to convert the problems into convex ones by using the difference of convex programming and a mixed $\ell _{1}/\ell _{2}$ -norm. Then, the iterative algorithms are proposed to solve the problems. The simulation results show that by using proposed algorithms, the transmit power consumption and the network power consumption can be significantly reduced. Meanwhile, the number of active RAUs can be effectively decreased.

Optimal and Robust Power Allocation for Visible Light Positioning Systems Under Illumination Constraints

The problem of optimal power allocation among light emitting diode (LED) transmitters in a visible light positioning system is considered for the purpose of improving localization performance of visible light communication (VLC) receivers. Specifically, the aim is to minimize the Cramer-Rao lower bound (CRLB) on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints, such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches.

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.

Power Minimization-Based Joint Task Scheduling and Resource Allocation in Downlink C-RAN

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

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.

Energy efficient resource matching algorithm for multi-homing services in dynamic wireless environment

This paper investigates the matching problem between radio resources and multi-homing services in a dynamic heterogeneous wireless environment. A mobile terminal with a multi-homing service simultaneously transmits data to multiple radio access networks using multiple air interfaces. This paper proposes an energy-efficient resource matching algorithm to statistically guarantee the service rate requirements of each user with the lowest power consumption. First, according to the concept of resource matching and the dynamic characteristics of wireless channels, this paper defines two matching probability metrics and subsequently selects the most appropriate metric to build a resource matching model to realize the maximum energy efficiency of each user while meeting the statistical guarantee constraints. In particular, the Rayleigh fading channel model is introduced in this paper to accurately reflect the dynamics of the wireless channel, and a complete expression of the matching probability metric is deduced. Second, according to the optimal solution characteristics of the optimization model, the original problem is decomposed into the inner power minimization problem and the outer matching probability maximization problem, and the convex optimization and the equality constrained optimization are separately used to solve the problem. Hence, the two-layer distributed solution algorithm is proposed. In particular, this paper conducts rigorous mathematical analyses on the convergence, optimality and complexity of the algorithm and constructs an improved resource matching model by relaxing the statistical guarantee parameters in the case of no solution to the model. Finally, the simulation results show that this algorithm has better performance than the three existing resource matching algorithms, can achieve lower user power consumption, and can realize the goal of the best match between the network resources and service requirements in a dynamic environment.

Beamforming Techniques for Nonorthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output nonorthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information is investigated to provide the required quality of service for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original nonconvex power minimization problem to a tractable one. Furthermore, a fairness-based beamforming approach is proposed through a max–min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original nonconvex problem is reformulated in a linear matrix inequality form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the nonrobust scheme in terms of the rate satisfaction ratio. Furthermore, simulation results confirm that NOMA consumes a little over half transmit power needed by orthogonal multiple access for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.

Enabling radioprotection capabilities in next generation wireless communication systems: An ecological green approach

AbstractIn future fifth‐generation and beyond radio systems, access points equipped with massive antennas will be deployed to support the increased communication demands. As a result, radio environments will become more dense and users will be exposed to higher electromagnetic field (EMF) radiation from wireless devices than today. This paper proposes to take preemptive action toward protecting the public health from potential EMF‐related ill effects by examining radiation‐aware solutions for future green wireless communication systems from the radio resource scheduling perspective. Our efforts focus on correlating the transmit power levels of the wireless system with the operands used to express the EMF dosimetry metrics known as maximum permissible exposure and specific absorption rate. In addition, we formulate power minimization problems subject to the maximum permissible exposure and specific absorption rate safety standards, and the individual user quality‐of‐service demands to derive convex optimization‐based solution of dynamic subcarrier allocation and adaptive power management. The simulation results confirm that our green solution reduces significantly the user exposure to radiation, while providing the required quality of service. We expect that our findings can kick off new research directions for controlling the public exposure to radiation from wireless devices in dense networks toward safer fifth‐generation communication systems.

NOMA Aided Interference Management for Full-Duplex Self-Backhauling HetNets

The presence of mutual-coupled interference in full-duplex self-backhauling heterogeneous networks raises challenges and difficulties in practical scenarios. In this letter, we address this issue by developing a two-tier non-orthogonal multiple access (NOMA) scheme together with efficient power control to enable aggressive frequency reuse and alleviate co-channel interference. For the considered multi-tier and multi-cell NOMA scenario, we formulate a power minimization problem, and develop an efficient algorithm with guaranteed convergence to enable optimal power control, such that users’ data demand is satisfied and backhauling bottleneck is avoided. Numerical results show the fast convergence of the proposed algorithm, and demonstrate that NOMA is in particular favorable for the high-demand cases in power savings.

Multiple Constrained <inline-formula> <tex-math notation="LaTeX">$\ell_2$ </tex-math> </inline-formula>-Norm Minimization Algorithm for Adaptive Beamforming

This paper presents an effective adaptive beamforming method based on an $\ell _{2}$ -norm minimization problem with multiple constraints, named as MC- $\ell _{2}$ -M algorithm. In the proposed method, the output power minimization problem can be formulated as an $\ell _{2}$ -norm minimization problem of the weight vector, with multiple constraints including distortionless response constraint and zero-forcing response constraints toward the desired signal and the interference signals, respectively. Furthermore, via an interference subspace estimation, the presented $\ell _{2}$ -norm minimization problem can be rewritten as an equivalent constrained convex optimization problem of the weight vector, which is readily to be solved. Finally, the closed-form solution of the proposed optimization problem is given. Compared with the previous works, the proposed MC- $\ell _{2}$ -M approach provides better beamforming performance, for example: 1) it reduces the sensitivity to the variation of signal-to-noise ratio (SNR) so that it has better output array gain, even if in the large dynamic SNR changing circumstances; 2) the better sidelobe and nulling levels are maintained, since it weakens the component of the desired signal in the covariance matrix of the array snapshot vector by a new defined blocking matrix; and 3) it can not only effectively suppress the interference signals but also ensure that the response of the desired signal is distortionless. Simulation results demonstrate the efficiency of the presented approach.

MIMO Radar and Cellular Coexistence: A Power-Efficient Approach Enabled by Interference Exploitation

We propose a novel approach to enable the coexistence between Multi-Input-Multi-Output (MIMO) radar and downlink multiuser multi-input single-output communication system. By exploiting the constructive multiuser interference (MUI), the proposed approach tradeoff useful MUI power for reducing the transmit power, to obtain a power efficient transmission. This paper focuses on two optimization problems: a) Transmit power minimization at the base station (BS), while guaranteeing the receive signal-to-interference-plus-noise ratio (SINR) level of downlink users and the interference-to-noise ratio level to radar; b) Minimization of the interference from BS to radar for a given requirement of downlink SINR and transmit power budget. To reduce the computational overhead of the proposed scheme in practice, an algorithm based on gradient projection is designed to solve the power minimization problem. In addition, we investigate the tradeoff between the performance of radar and communication, and analytically derive the key metrics for MIMO radar in the presence of the interference from the BS. Finally, a robust power minimization problem is formulated to ensure the effectiveness of the proposed method in the case of imperfect channel state information. Numerical results show that the proposed method achieves a significant power saving compared to conventional approaches, while obtaining a favorable performance-complexity tradeoff.

Asynchronous Two-Way MIMO Relaying: A Multi-Relay Scenario

We consider a single-carrier asynchronous relay network where two transceivers exchange information with the help of multiple multi-antenna relays. The network is assumed to be asynchronous , meaning that the signal transmitted by any of the two transceivers arrives at different relays with different delays and also signals transmitted by different relays arrive at any of the two transceivers with different delays. We further assume that each relay obtains the vector of the relay transmit signals via multiplying the vector of the relay received signals by a beamforming matrix. For such an asynchronous two-way network with multi-antenna relays, our goal is to obtain symmetric relay beamforming matrices and the transceivers’ transmit powers such that the total power consumed in the entire network is minimized while guaranteeing given data rates at the two transceivers. To this end, we develop a model for the end-to-end channel and use this model to solve the total power minimization problem. Assuming symmetric relay beamforming matrices, we present a computationally efficient solution to this problem. Our simulation results suggest that for a given total number of antennas, there appears to be an optimal number of antennas per relays which results in the lowest power consumption in the network.

Coordinated Beamforming With Artificial Noise for Secure SWIPT Under Non-Linear EH Model: Centralized and Distributed Designs

This paper investigates the artificial noise (AN)-aided multi-cell coordinated beamforming (MCBF) for secure simultaneous wireless information and power transfer in both centralized and distributed manners. The proposed transmit design is formulated into a power-minimization problem to guarantee the authorized users’ information and energy harvesting (EH) requirements while avoiding the information interception by unauthorized users. Power splitting receiver architecture and the non-linear EH model are employed. Both perfect and imperfect channel state information (CSI) cases are considered. For the perfect CSI case, the non-robust design is presented by applying semi-definition relaxation (SDR). When no user harvests energy, the global optimum is guaranteed, and when some users harvest energy, approximate global optimum is achieved. For the imperfect CSI case, the worst-case robust design under the deterministic uncertainty channel model is studied, where a solving approach based on SDR and S-procedure is proposed, and the statistically robust design under the stochastic uncertainty channel model is also studied, where an upper bound to the global optimum is obtained by using SDR and Bernstein-type inequality. We further propose a distributed AN-aided MCBF design framework by using an alternating direction method of multipliers for the non-robust, worst-case robust, and statistically robust designs, with which each BS is able to optimize its own transmit design with the local CSI. Simulation results demonstrate our theoretical analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It also shows that employing the non-linear EH model is able to avoid false output power and save power consumption at the BSs.

Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach

Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize statistics of extreme events in terms of the maximal queue length. Subsequently, leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.

Optimization Algorithms for Multiaccess Green Communications in Internet of Things

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things (IoT). In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in IoT, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the nonlinear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron–Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.

Joint Transceiver Optimization for MISO SWIPT Systems With Time Switching

This paper considers multiple-input single-output simultaneous wireless information and power transfer (SWIPT) broadcast channels (BCs) where a multi-antenna transmitter serves single antenna receivers each equipped with a time switching (TS) circuit for information decoding (ID) and energy harvesting (EH). To be specific, we study a scheme which jointly determines the time durations allocated for the ID and the EH modes at each receiver and the transmit covariance matrices at the transmitter. Then, we present a general joint TS protocol for the SWIPT BC which includes conventional TS schemes as special cases. In order to fully characterize the performance of the proposed joint TS systems, the achievable rate region is analyzed under EH constraint at the receivers. By applying the rate profile methods, we identify the optimal TS ratios and the optimal transmit covariance matrices which achieve the boundary points of the rate region. Then, the boundary points are obtained by solving the average transmit power minimization problems with individual rate constraints at the receivers. To solve these non-convex problems, the original problems are decoupled into subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed by finding the optimal auxiliary variables via convex optimization techniques. Numerical results demonstrate that the proposed joint TS scheme outperforms conventional TS methods.

Constructive Interference for Generic Constellations

In this letter, we investigate optimal and relaxed constructive interference regions (CIR) for the symbol-level precoding (SLP) problem in the downlink of a multiuser multiple-input single-output (MISO) channel. We define two types of CIRs, namely, distance preserving CIR (DPCIR) and union bound CIR (UBCIR) for any given constellation shape and size. We then provide a systematic way to describe these regions as convex sets. Using the definitions of DPCIR and UBCIR, we show that the SLP power minimization problem, minimizing either sum or peak (per-antenna) transmit power, can always be formulated as a convex optimization problem. Our results indicate that these regions allow further reduction of the transmit power compared to the current state of the art without increasing the computational complexity at the transmitter or receiver.

Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

Energy-Efficient SWIPT: From Fully Digital to Hybrid Analog–Digital Beamforming

Simultaneous wireless information and power transfer (SWIPT) enables the transmission of information symbols and energy simultaneously. In this paper, we study the multiple-input-multiple-output SWIPT systems with limited RF chains at the base station. We focus on the scenario where there is one information decoder with a target signal-to-interference-plus-noise-ratio and several separate energy-harvesting receivers with harvested energy thresholds. To motivate our energy-efficient hybrid analog–digital beamforming strategy, the fully digital power minimization problem is first analyzed, where we mathematically show that the optimal beamformer consists of only the information beamformer, and derive closed-form beamformers for a number of special cases. Based on this result, we further consider hybrid beamforming and propose an iterative scheme where the analog and digital beamformers are alternately updated. For the proposed scheme, in each iteration we design the analog beamformer by minimizing the difference between the fully digital beamformer and the hybrid beamformer. Based on our above-mentioned analysis for fully digital case, the optimal solution for the analog beamformer can be obtained via a geometrical interpretation. We further design the robust beamformers for the proposed schemes, when only imperfect channel state information is available. The numerical results show that the proposed iterative designs achieve a close-to-optimal performance with significant gains in the total power consumption over fully digital SWIPT.