Optimal Transmit Power Allocation Research Articles

Optimal Transmit Antenna Deployment and Power Allocation for Wireless Power Supply in an Indoor Space

Optimal Transmit Antenna Deployment and Power Allocation for Wireless Power Supply in an Indoor Space

Energy efficient resource allocation for re-configurable intelligent surface-assisted wireless networks

This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach’s algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.

Energy-Efficient Wireless System Within Average Delay and With Mixed-Erlang-Distributed Data

Energy efficiency (EE) is a key performance indicator in modern wireless systems. Consider a point-to-point buffer-aided communication system model, where 1) the wireless channel has Shannon capacity and is characterized by the Nakagami-m distribution; 2) application data has an average data rate with mixed-Erlang distributed data sizes and a quality-of-service (QoS) constraint on average delay. In this letter, we first develop a numerical expression of average delay in the above system model, and then propose an optimal transmit power allocation scheme to maximize the EE under the QoS constraint. It is shown in the Monte-Carlo simulation that our numerical expression can accurately approximate the average delay. From the numerical simulation, the maximum EE as a function of data rate is shown to have a bell-shaped curve.

Non-Orthogonal Multiple Access with One-Bit Analog-to-Digital Converters Using Threshold Adaptation.

In digital communication systems featuring high-resolution analog-to-digital converters (ADCs), the utilization of successive interference cancellation and detection can enhance the capacity of a Gaussian multiple access channel (MAC) by combining signals from multiple transmitters in a non-orthogonal manner. Conversely, in systems employing one-bit ADCs, it is exceedingly difficult to eliminate non-orthogonal interference using digital signal processing due to the considerable distortion present in the received signal when employing such ADCs. As a result, the Gaussian MAC does not yield significant capacity gains in such cases. To address this issue, we demonstrate that, under a given deterministic interference, the capacity of a one-bit-quantized channel becomes equivalent to the capacity without interference when an appropriate threshold value is chosen. This finding suggests the potential for indirect interference cancellation in the analog domain, facilitating the proposition of an efficient successive interference cancellation and detection scheme. We analyze the achievable rate of the proposed scheme by deriving the mutual information between the transmitted and received signals at each detection stage. The obtained results indicate that the sum rate of the proposed scheme generally outperforms conventional methods, with the achievable upper bound being twice as high as that of the conventional methods. Additionally, we have developed an optimal transmit power allocation algorithm to maximize the sum rate in fading channels.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

Secrecy Rate Maximization for Multicarrier-Based Cognitive Radio Networks With an Energy Harvesting Jammer

Cognitive radio (CR) is becoming an increasingly significant component of Internet-of-Things technologies because it overcomes the challenge of spectrum resource scarcity for wireless communications. For the cooperative CR network (CRN) based on the multicarrier system, this article investigates the physical layer security (PLS) to guarantee information privacy for the secondary network while guaranteeing the quality of service (QoS) of the primary network. First, we propose one communication scheme for this type of CRN. The secondary transmitter (ST) and secondary jammer (SJ) first harvest RF energy from the primary signal transmitted by the primary transmitter (PT). Then, ST transmits confidential information with the spectrum vacated by the primary network to the secondary receiver (SR), and SJ simultaneously generates the jamming signal to interfere with the eavesdropper (ED). Second, for the scenarios with the instantaneous channel state information (CSI) and the statistical CSI of the ED, respectively, we aim to maximize the secrecy rate of the secondary network while guaranteeing the QoS requirement of the primary network by jointly optimizing the time allocation between the two phases and the transmit power allocation for ST and SJ over subcarriers. For each scenario, the secrecy rate maximization is formulated as the nonconvex optimization problem. Finally, we design efficient algorithms for both two nonconvex problems. The optimal time allocation is obtained by the 1-D search method at the outer layer, and the optimal transmit power allocation is obtained by the difference of concave (DC) programming at the inner layer, where the Lagrange duality method is used to solve the convex approximation problem. Simulation results verify that the proposed scheme essentially improves the secrecy rate of the secondary network compared to benchmark schemes.

Rate-splitting for intelligent reflecting surface-assisted CR-NOMA systems

Intelligent reflecting surface (IRS) has been regarded as promising technique to improve system performance for wireless communications. In this paper, we propose a ratesplitting (RS) scheme for an IRS-assisted cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) system, where the primary user's (PU's) quality of service (QoS) requirements must be guaranteed to be same as in orthogonal multiple access. Assisted by IRS, the threshold for the PU's tolerable interference power is improved, which in turn makes it possible to increase the achievable rate for the secondary user (SU). The optimal transmit power allocation, target rate allocation, and successive interference cancellation (SIC) decoding order are jointly designed for the proposed RS scheme. Taking into account the statistics of the direct link and IRS reflecting channels, closed-form expression for the PU's and SU's outage probabilities are respectively derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed RS scheme over the existing CR-NOMA and IRS-assisted CR-NOMA schemes.

Intelligent reflecting surface-assisted cognitive radio-inspired rate-splitting multiple access systems

Intelligent reflecting surface (IRS) has been widely regarded as a promising technology for configuring wireless propagation environments. In this paper, we utilize IRS to assist transmission of a secondary user (SU) in a cognitive radio-inspired rate-splitting multiple access (CR-RSMA) system in which a primary user's (PU's) quality of service (QoS) requirements must be guaranteed. Without introducing intolerable interference to deteriorate the PU's outage performance, the SU conducts rate-splitting to transmit its signal to the base-station through the direct link and IRS reflecting channels. For the IRS-assisted CR-RSMA (IRS-CR-RSMA) scheme, we derive the optimal transmit power allocation, target rate allocation, and successive interference cancellation decoding order to enhance the outage performance of the SU. The closed-form expression for the SU's outage probability achieved by the IRS-CR-RSMA scheme is derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed IRS-CR-RSMA scheme over the CR-RSMA scheme.

An MEC-Enabled Framework for Task Offloading and Power Allocation in NOMA Enhanced ABS-Assisted VANETs

This letter investigates the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) in air base station (ABS)-assisted vehicular ad hoc networks (VANETs). A tethered ABS acts as the server to provide MEC services for vehicles. Vehicles adopt NOMA to offload their tasks to the ABS. For the considered network, we formulate an average task processing ratio maximization problem by jointly optimizing the offloading decision, computation resource allocation, and power allocation. For tackling this non-convex problem, we first obtain the offloading decision and the computation resource allocation with the aid of the relax-and-round method and the convex problem solver. Then, the optimal transmission power allocation of vehicles is derived by considering the successive interference cancellation decoding threshold. Finally, the simulation results show that the proposed algorithm has a significant performance improvement in the average task processing ratio compared with the current works in different urban scenarios.

The Optimal Power Allocation for Sum Rate and Energy Efficiency of Full-Duplex Two-Way Communication Network.

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.

Optimal Power Allocation for Multiple Channels With One-Bit Analog-to-Digital Converters

In this study, an optimal power allocation over multiple noninterfering channels is derived for the case in which each channel uses two separate one-bit analog-to-digital converters for the real and imaginary parts respectively of the complex received signal. The optimization problem is formulated to determine the optimal transmit power allocation over multiple channels that maximizes the sum capacity. The solution of the optimization problem is explicitly determined by exploiting the Karush-Kuhn-Tucker conditions. The essential strategy for optimal power allocation is described with the corresponding mathematical results. Simulation results demonstrate that the derived power allocation is indeed optimal, as expected, and it achieves a significant gain compared to the case where an appropriate power allocation has not been used.

Secrecy Performance Maximization for Underlay CR Networks with an Energy Harvesting Jammer.

This paper investigates the secrecy communication in an underlay cognitive radio (CR) networks with one primary user (PU) as well as multiple PUs, where the radio frequency (RF) energy-harvesting secondary user (SU) transmits the confidential information to the destination in the presence of a potential eavesdropper. We introduce a RF energy-harvesting secondary jammer (SJ) to secure the SU transmissions. The system works in time slots, where each time slot is divided into the energy transfer (ET) phase and the information transfer (IT) phase. In ET phase, the SU and SJ capture energy from the PU transmissions; in the IT phase, the SU uses the harvested energy to transmit information to the destination without causing the harmful interference to the PU transmissions, while the SJ utilizes the captured energy to generate jamming signals to the eavesdropper to secure the SU transmissions. We aim to maximize the secrecy rate for SU transmissionsby jointly optimizing the time allocation between ET phase and IT phase and the transmit power allocation at the SU and SJ. We first formulate the secrecy rate maximization as non-convex optimization problems. Then, we propose efficient nested form algorithms for the non-convex problems. In the outer layer, we obtain the optimal time allocation by the one dimension search method. In the inner layer, we obtain the optimal transmit power allocation by the DC programming, where the Lagrange duality method is employed to solve the convex approximation problem. Simulation results verify that the proposed schemes essentially improve the secrecy rate of the secondary network as compared to the benchmark schemes.

Optimal Transmit Power Allocation for an Energy-Harvesting Sensor in Wireless Cyber-Physical Systems.

In this article, we investigate optimal transmission power allocation at a sensor equipped with the energy-harvesting technology for remote state estimation in wireless cyber-physical systems. The sensor has access to an energy harvester, which can collect energy from the external environment and is an everlasting but unreliable energy source compared with conventional batteries. For the wireless dropping communication channel, the packet dropout rates depend on both the signal-to-noise ratio and the transmission power used by the sensor. We formulate the problem of the optimal transmission power allocation to minimize the remote estimation error covariances as a Markov decision processes (MDPs) subject to energy constraint of the sensor. By analyzing the MDP algorithm, we show that an optimal deterministic and stationary transmission power policy exists. Moreover, we show that the optimal policy has a threshold-type structure. A numerical simulation is provided to illustrate the performance of the transmission power allocation algorithm.

Specific Quality of Service Constrained Optimal Allocation of Transmit Power in Uplink Cognitive OFDMA System

This paper investigates an optimal allocation of transmit power for uplink cognitive OFDMA system. The aim is to construct two optimization frameworks namely, framework-I and II for uplink cognitive OFDMA system that minimizes it’s transmit power while maintaining Quality of Service (QoS). The measures for QoS include SNR threshold for framework-I whereas, for framework-II, it is measured by minimum rate requirement (bits/sec/Hz) to obtain a certain bit error rate (BER). Simulation results reveal the effectiveness of the proposed frameworks. Additionally, for framework-I, effects of different SNR threshold and users’ power budget are observed on the allocation of transmit power. Whereas, for framework-II, effects of different target BER, users’ power budget and minimum rate requirement are observed on the allocation of transmit power. Results are also compared with the results obtained from conventional capacity maximization based resource allocation approaches in terms of allocated transmit power, energy efficiency (EE) and spectral efficiency (SE). Simulation results reveal that, the proposed frameworks are incredibly successful in terms of utilization of power budget of users and EE compared to conventional capacity maximization based resource allocation approaches.

On the Achievable Max-Min Rates of Cooperative Power-Domain NOMA Systems

The non-orthogonal multiple access (NOMA) schemes, where the users are allowed to share the same time-frequency resource, have recently received a major interest to meet the increasing demands for higher data rates, improved user fairness, and/or lower latency applications in future dense-user wireless networks. Moreover, the achievable user rates of NOMA schemes can be further increased through the integration of these multiple access schemes with other enabling schemes such as full-duplex user cooperative communications. In this paper, the optimization of the achievable max-min rates for full-duplex cooperative NOMA (CNOMA) schemes for the two-user scenario are re-visited and is extended to the three-user scenario where novel expressions for the optimal user cooperation power are derived for both scenarios. The obtained results shed new lights on the optimal transmit power allocation and the optimal user cooperation power allocation in CNOMA systems and the different design trade-offs in such systems.

Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

This work investigates the deployment of unmanned aerial vehicles (UAVs) as access points to provide wireless services to users in a green field. Three fundamental deployment designs are studied under practical air-to-ground (ATG) channel models, namely the minimum number of UAVs, their optimal deployment locations and the optimal transmit power allocation. Since these three design goals are coupled, a particle swarm optimization (PSO)-based scheme is proposed in conjunction with the balanced Signal to Interference plus Noise Ratio (SINR) transmit power allocation. By exploiting the closed-form expressions of the SINR-balanced optimal power allocation and the resulting SINR, the proposed PSO-based scheme iteratively optimizes the UAV number and subsequently, their locations until the resulting SINR of each user meets its required minimum value. To improve the convergence behavior of the proposed scheme, two schemes are devised to provide an initial estimate on the minimum number of UAVs by analyzing the system sum-rate capacity before using a K-means clustering technique to initialize the UAV locations. Finally, a power fine-tuning scheme is developed to further reduce the total transmit power. Extensive simulation is performed to confirm the good performance of the proposed schemes.

Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage, Capacity, and Power Allocation

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as the order of clustering (or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.

Physical layer authentication under intelligent spoofing in wireless sensor networks

Location based access in wireless sensor networks (WSN) are vulnerable to location spoofing attacks. In this paper, we investigate the physical layer (PHY-layer) authentication in the threat of an intelligent location spoofing attack. The intelligent attack can emulate the legitimate channel information and maximize its long-term cumulative reward. First, we analyze the feasibility of this intelligent attack and investigate how it threats to the networks. Specifically, we derive the optimal transmit power allocation and find the worst case for the defenders, namely optimal intelligent attack, in which the attacker can learn the intelligent attack action based on the beamforming with optimal transmit power allocation. To defend against such an intelligent attack with high accuracy and low overhead, we develop a cooperative PHY-layer authentication scheme. Then, we provide an in-depth analysis on the belief and derive the belief bounds and the closed-form expression for the belief threshold. Furthermore, considering the whole computation complexity and the double counting problem in a loopy graph, we propose the cooperative neighbour selection algorithm to accelerate belief convergence and reduce the overhead. Finally, the simulation results reveal that the proposed method can significantly improve the defense performance compared with the state-of-art methods.

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

A Reliable Energy Efficient Dynamic Spectrum Sensing for Cognitive Radio IoT Networks

The Internet of Things (IoT) that allows connectivity of network devices embedded with sensors undergoes severe data exchange interference as the unlicensed spectrum band becomes overcrowded. By applying cognitive radio (CR) capabilities to IoT, a novel cognitive radio IoT (CR-IoT) network arises as a promising solution to tackle the spectrum scarcity problem in conventional IoT network. CR is a form of wireless communication whereby a radio is dynamically programmed and configured to detect available spectrum channels. This enhances the spectrum utilization efficiency of radio frequency while avoiding interference and overcrowding to other users. Energy efficiency in CR-IoT network must be carefully formulated since the sensor nodes consume significant energy to support CR operations, such as in dynamic spectrum sensing and switching. In this paper, we study channel spectrum sensing to boost energy efficiency in clustered CR-IoT networks. We propose a two-way information exchange dynamic spectrum sensing algorithms to improve energy efficiency for data transmission in licensed channels. In addition, the concern of the energy consumption in dynamic spectrum sensing and switching, we propose an energy efficient optimal transmit power allocation technique to enhance the dynamic spectrum sensing and data throughput. Simulation results validate that the proposed dynamic spectrum sensing technique can significantly reduce the energy consumption in CR-IoT networks.