Minimum Transmission Power Research Articles

Long short‐term memory‐based power‐aware algorithm for prompt heterogenous activity

SummaryHuman–computer interaction plays a vital role in wireless body area networks, internet of things, and big data. Wearables are low‐power devices with minimal battery capacity. In general, wearables suffer from energy losses due to changes in the user's body posture, diffraction, reflection, and shadowing of the human body. As a result, many control packets are needed to ensure proper communication in wireless body area network. Hence, this research proposes a long short‐term memory‐based power‐aware (LSTM‐PA) algorithm to ensure burst data transmission during prompt heterogeneous activities in the presence and absence of inter‐WBAN interference. The proposed algorithm predicts the best quality time (BQT) for data transmission by activity classification and robust R2 similarity (RRS) metric. The minimum transmission power is estimated by the critical point classification technique. The activity classification accuracy is 92% in the LSTM‐PA algorithm. The energy consumption in the node is reduced by up to 46.34% compared to benchmark algorithms.

Power Minimization in Dual-Hop Underlay Cooperative Cognitive Radio Relay Networks for Optimal Resource Allocation

In this paper, we investigate a multi-user one-way dual-hop multi-relay cognitive radio underlay spectrum sharing network. .The joint optimal resource allocation of the bandwidth and power allocation in underlay Cooperative Cognitive Radio Relay Network (CCRRN) was studied and a convex optimization analytical framework is presented. A combined optimal power and bandwidth allocation (COPBA) scheme for the minimization of the total network power and blocking probability of the call admission control stage of the CCRRN is investigated. Our goal is to jointly optimize the bandwidth and power allocation such that the total transmission power in the CCRRN is minimized without compromising the Quality of Service (QoS) demands of the secondary users (SUs) and the interference constraint thresholds of the primary users (PUs) in the primary networks. The mathematical model and convex optimization problem with the aim of minimizing the total transmission power of the CCRRN was formulated. The resulting optimization problem was solved in the standard computer based numerical optimization solver package CONOPT in MATLAB/TOMLAB environment. Simulation results obtained is compared the equal-bandwidth optimal power allocation scheme. Keywords—Radio resource allocations; Cognitive radio Networks; Convex optimization; Cooperative relay networks, Transmit power minimization

Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave (<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs&#x2019; phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs&#x2019; trajectories/velocities, RISs&#x2019; phase shifts, and subcarrier allocations for <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.

Stroboscopic ultrafast imaging using RF strip-lines in a commercial transmission electron microscope

The development of ultrafast electron microscopy (UEM), specifically stroboscopic imaging, has brought the study of structural dynamics to a new level by overcoming the spatial limitations of ultrafast spectroscopy and the temporal restrictions of traditional TEM simultaneously. Combining the concepts governing both techniques has enabled direct visualization of dynamics with spatiotemporal resolutions in the picosecond-nanometer regime. Here, we push the limits of imaging using a pulsed electron beam via RF induced transverse deflection based on the newly developed 200 keV frequency-tunable strip-line pulser. We demonstrate a 0.2 nm spatial resolution and elucidation of magnetic spin induction maps using the phase-microscopy method. We also present beam coherence measurements and expand our study using the breathing modes of a silicon interdigitated comb under RF excitation which achieves improved temporal synchronization between the electron pulse-train and electric field. A new RF holder has also been developed with impedance matching to the RF signal to minimize transmission power loss to samples and its performance is compared with a conventional sample holder.

A Modified Method for Beam-Forming Using Covariance Matrix in MIMO Radar System

A MIMO radar provides solution for beamforming, since transmit beampattern synthesis is well proven method for stimulating the antenna array to develop the beampattern. It is close to the desired one which can give minimum error between these two. A covariance matrix in beamforming contains the direction of invariable data and magnitude distribution in multidimensional space which decides the closeness with the desired beampattern. Here, selection of an optimal covariance matrix is constrained optimization problem with minimization of cost function. In high directivity radar systems, the antenna beam is needed to be steered to cover a large area for detection. In this proposed work, minimization of convex function and transmit beampattern synthesis is carried out with modifications of the covariance matrix ‘Rcov’ for minimum and maximum transmission power utilization constraints. The beampattern design problem is reformulated here, as semi-definite programming optimization problem and solved using convex optimization, with MATLAB simulation platform. The results show that the beampattern generated using this modified covariance matrix is optimally close to desired pattern.

Low-Complexity PSO-Based Resource Allocation Scheme for Cooperative Non-Linear SWIPT-Enabled NOMA

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.

Research on anti-jamming beamforming with multi-UAV relays

As the common means of field emergency communication, the unmanned aerial vehicle (UAV) relay plays an important role in both civil and military fields. With the diversification of requirements and the limitations of single operation, multi-UAV cooperative work has attracted extensive attention. However, due to the UAV signal’s universality of broadcast and the limitation of power, information transmission is more likely to be interrupted. Therefore, in the presence of interference, this paper adopts forward amplification protocol under UAV relay transmission model. And two beamforming methods are proposed based on minimizing transmission power and maximizing communication rate of UAV relays. Then, we establish the power-constrained maximum security rate optimization problem with UAV relays, and deduce the corresponding beamforming weight vector and the optimal rate of safety. Finally, the simulation results show the effectiveness and accuracy of the proposed beamforming method.

A Cat Swarm Optimization based transmission power minimization for an aerial NOMA communication system

This article underlines the inclusion of Non-Orthogonal Multiple Access (NOMA) aerial network nodes to rapidly serve a mass deployment of devices in the next-generation networks. The analysis of an aerial NOMA deployment is conducted considering the objective of minimization of the required transmission power in comparison to an aerial deployment employing Orthogonal Multiple Access (OMA). The findings of the study highlight the inter-dependency among the considered optimization variables of user pairing, altitude, and power allocation as well as stress the implication of their joint optimization for improved performance of the aerial NOMA system. The formulated mixed-integer non-linear programming problem is solved utilizing a joint optimization technique. Meanwhile, the employment of Cat Swarm Optimization (CSO) framework for NOMA user pairing optimization marks the first work of its kind in the literature. Subsequently, the altitude of the NOMA Unmanned Aerial Vehicle Base Station (UAV-BS) is computed using tools of convex optimization. The obtained results of the proposed methodology solved iteratively substantiate the better performance of NOMA compared to an equivalent OMA UAV-BS deployment. Subsequently, the presented results demonstrate the efficacy of the proposed CSO approach in reducing the required transmission power as well as the operating altitude attributed to lower flying energy consumption of the UAV-BS compared to random and particle swarm optimization based user pairing techniques.

Power Minimization Using Rate Splitting With Statistical CSI in Cloud-Radio Access Networks

Minimizing the power consumption in mobile communication networks while ensuring a minimum quality of service (QoS) for applications is essential in light of the unprecedented expected increase in the number of connected devices and the associated data traffic beyond the fifth generation of wireless networks (B5G). This paper considers a cloud-radio access network (C-RAN) model where a central processor (CP) is connected to the base stations (BSs) via limited capacity fronthaul links. In the context of our C-RAN setting, we consider the practical case where the CP has only statistical knowledge of channel state information (CSI). While conventional wireless systems adopt the treating interference as noise (TIN) strategy to deal with the interference in the network, this paper instead considers that the CP applies the rate splitting (RS) strategy by dividing each user’s message into two parts: a private part to be decoded by the intended user only and a common part to be decoded by a subset of users, for the sole reason of interference mitigation in the network. To best account for the channel estimation errors, this paper addresses the problem of transmit power minimization under minimum QoS constraints on the achievable ergodic rate per user, so as to determine the beamforming vectors of the private and common messages as well as the rate allocated to all the users. The considered problem is of stochastic, complex, and non-convex nature. This paper addresses the problem intricacies through an iterative approach that leverages both the sample average approximation (SAA) technique and the weighted minimum mean squared error (WMMSE) algorithm to obtain a stationary point of the optimization problem in the asymptotic regime. The numerical results demonstrate the gain achieved with the RS strategy as compared to TIN, especially under high QoS requirements.

Multi-objective robust secure beamforming for cognitive satellite and UAV networks

A multi-objective optimization based robust beam-forming (BF) scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle (UAV) network. Since the satellite network coexists with the UAV network, we first consider both achievable secrecy rate maximization and total transmit power minimization, and formulate a multi-objective optimization problem (MOOP) using the weighted Tchebycheff approach. Then, by supposing that only imperfect channel state information based on the angular information is available, we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones. Next, we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector. Finally, simulation results illustrate that the Pareto optimal trade-off can be achieved, and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System

Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

Joint design of transmit beamforming, IRS platform, and power splitting SWIPT receivers for downlink cellular multiuser MISO

A multiple antenna base station (BS) with an intelligent reflecting surface (IRS) platform, and several single antenna users are considered in the downlink mode. Simultaneous wireless information and power transfer (SWIPT) is utilized by the BS via transmit beamforming to convey information and power to all devices. Each device applies power splitting (PS) to dedicate separate parts of received power to information decoding and energy harvesting. We formulate a total transmit power minimization problem to jointly design the BS beamforming vectors, IRS phase shifts, and PS ratios at the receivers subject to minimum rate and harvested energy quality of service (QoS) constraints at all the receivers. First, we develop a block coordinate descent algorithm, also known as alternating optimization that can decrease the objective function in every iteration with guaranteed convergence. Afterwards, two low-complexity sub-optimal algorithms that rely on well-known maximum ratio transmission and zero-forcing beamforming techniques are introduced. These algorithms are beneficial when the number of BS antennas and/or number of users is large, or coherence times of channels are small. Simulations corroborate the expectation that by deploying a passive IRS, BS transmit power can be reduced by 10–20 dBw while maintaining similar guaranteed QoS. Furthermore, even the proposed sub-optimal algorithms outperform the globally optimal SWIPT solution without IRS for a modest number of IRS elements.

Power allocation technique with soft performance guarantees in hybrid OFDMA–NOMA cognitive radio systems: Modeling and simulation

The integration between orthogonal frequency division multiple access (OFDMA), non-orthogonal multiple access (NOMA), and cognitive radio (CR) technology has been recently configured as promising candidate to support the massive high-speed connectivity requirements of beyond fifth generation (B5G) systems. This combination is referred to in the literature as a hybrid OFDMA–NOMA CR network. In such a system, each primary user’s (PU) idle channel, i.e., sub-band, is opportunistically utilized to serve multiple secondary users (SUs) by exploiting power-domain NOMA. This study soughs to explore the resource allocation technique for a hybrid OFDMA–NOMA CR system to further elaborate the potential capabilities of this system. Specifically, a power minimization resource allocation framework is developed, in which the objective is to serve the contending CR users, i.e., SUs, with minimum possible total transmission power, while achieving a set of relevant NOMA and CR constraints. These constraints include the dynamic nature of PU activities, rate demand, probability of success and imposed CR power mask constraints. However, the developed power minimization framework is non-convex in nature. Therefore, we employ the sequential convex approximation (SCA) along with a second-order cone approach to deal with the non-convexity issues of the developed optimization problem, and thus evaluating the corresponding optimization parameters. Compared to the conventional orthogonal resource allocation techniques, simulation results reveal that our proposed resource allocation technique for the hybrid OFDMA–NOMA CR system has a considerable performance enhancement in terms of the overall network throughput and total required transmit power.

Parameterized Algorithms for Power-Efficiently Connecting Wireless Sensor Networks: Theory and Experiments

We study a problem of energy-efficiently connecting a symmetric wireless communication network: given an n-vertex graph with edge weights, find a connected spanning subgraph of minimum cost, where the cost is determined by each vertex paying the heaviest edge incident to it in the subgraph. The problem is known to be NP-hard. Strengthening this hardness result, we show that even o(log n)-approximating the difference d between the optimal solution cost and a natural lower bound is NP-hard. Moreover, we show that under the exponential time hypothesis, there are no exact algorithms running in 2o(n) time or in [Formula: see text] time for any computable function f. We also show that the special case of connecting c network components with minimum additional cost generally cannot be polynomial-time reduced to instances of size cO(1) unless the polynomial-time hierarchy collapses. On the positive side, we provide an algorithm that reconnects O(log n)-connected components with minimum additional cost in polynomial time. These algorithms are motivated by application scenarios of monitoring areas or where an existing sensor network may fall apart into several connected components because of sensor faults. In experiments, the algorithm outperforms CPLEX with known integer linear programming (ILP) formulations when n is sufficiently large compared with c. Summary of Contribution: Wireless sensor networks are used to monitor air pollution, water pollution, and machine health; in forest fire and landslide detection; and in natural disaster prevention. Sensors in wireless sensor networks are often battery-powered and disposable, so one may be interested in lowering the energy consumption of the sensors in order to achieve a long lifetime of the network. We study the min-power symmetric connectivity problem, which models the task of assigning transmission powers to sensors so as to achieve a connected communication network with minimum total power consumption. The problem is NP-hard. We provide perhaps the first parameterized complexity study of optimal and approximate solutions for the problem. Our algorithms work in polynomial time in the scenario where one has to reconnect a sensor network with n sensors and O(log n)-connected components by means of a minimum transmission power increase or if one can find transmission power lower bounds that already yield a network with O(log n)-connected components. In experiments, we show that, in this scenario, our algorithms outperform previously known exact algorithms based on ILP formulations.

SATPAS: SINR-based adaptive transmission power assignment with scheduling in wireless sensor network

In this work, a centralized metaheuristic algorithm is proposed for assigning minimum transmission power to each sensor node for every transmission link along with a scheduling slot while satisfying the Signal to Interference plus Noise Ratio constraint in the Wireless Sensor Network. Simultaneous communication in all communication links is not possible while maintaining SINR constraints. Thus, the algorithm also tries to maximize the number of concurrent transmission links in each scheduling slot. We formulate the optimization problem with two objective functions as (i) Communication link wise adaptive and minimum transmission power assignment together with scheduling slot number and (ii) Minimization of the required number of scheduling slots. Probabilistic interference model, which is more realistic, is used for calculating the receiver interference, unlike the graph-based interference model mostly described in the literature. Genetic Algorithm with Edge-set and Edge-window-decoder chromosome representation schemes are used for optimization. Extensive simulations are carried out to demonstrate the efficiency of proposed algorithms using different benchmark data sets and special network topologies like spiral, cluster, and mesh. Our proposed algorithm outperforms the existing MST-based algorithm in terms of minimum transmission power requirement, number of scheduling slots required, and rate of convergence.

Low‐cost localization technique for heterogeneous wireless sensor networks

SummaryIn heterogeneous wireless sensor networks (HWSN), the probability‐based node discovery method may result in inaccuracy and time consumption. Also, the adaptive adjustment of transmission range based on overhead and connectivity may result in additional overhead and time. Hence, in this paper, the author proposes to design a low‐cost localization technique using Artificial Bee Colony (ABC) optimization algorithm for HWSN. In this technique, ABC optimization algorithm is applied for deploying the anchor nodes for coverage. Then, the connectivity between the nodes is ensured with minimum transmission power. Since this method involves minimum transmission power level (TPL) and the only minimum set of anchor nodes with strong coverage is involved, it has low‐cost utilizing lower power consumption. By simulation result, it has been shown that the proposed method reduces power consumption leading to low cost.

Node-Level Error Control Strategies for Prolonging the Lifetime of Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), energy-efficiency and reliability are two critical requirements for attaining a long-term stable communication performance. Using error control (EC) methods is a promising technique to improve the reliability of WSNs. EC methods are typically utilized at the network-level, where all sensor nodes use the same EC method. However, improper selection of EC methods on some nodes in the network-level strategy can reduce the energy-efficiency, thus the lifetime of WSNs. In this study, a node-level EC strategy is proposed via mixed-integer programming (MIP) formulations. The MIP model determines the optimum EC method (i.e., automatic repeat request (ARQ), forward error correction (FEC), or hybrid ARQ (HARQ)) for each sensor node to maximize the network lifetime while guaranteeing a pre-determined reliability requirement. Five meta-heuristic approaches are developed to overcome the computational complexity of the MIP model. The performances of the MIP model and meta-heuristic approaches are evaluated for a wide range of parameters such as the number of nodes, network area, packet size, minimum desired reliability criterion, transmission power, and data rate. The results show that the node-level EC strategy provides at least 4.4% prolonged lifetimes and 4.0% better energy-efficiency than the network-level EC strategies. Furthermore, one of the developed meta-heuristic approaches (i.e., extended golden section search) provides lifetimes within a 3.9% neighborhood of the optimal solutions, reducing the solution time of the MIP model by 89.6%.

A Fully Connected Cluster with Minimal Transmission Power for IoT Using Electrostatic Discharge Algorithm

In the emerging age of the Internet of Things (IoT), energy-efficient and reliable connection among sensor nodes gain prime importance. Wireless engineers encounter a trade-off between sensors energy requirement and their reliable full connectivity. Consequently, the need to find the optimal solution draws the attention of many researchers. In this paper, the Electrostatic Discharge Algorithm (ESDA) is proposed, implemented, and applied to minimize energy needs of a sensor node while ensuring the fully-connectedness of each node. The obtained results show that the proposed method achieves better results than those found in the literature using the particle swarm optimization method in terms of energy savings and reliable connectivity.

Soft Computing Approach To Design Energy Efficient Protocol In Wireless Sensor Network

The major limitation of Wireless Sensor Network (WSN) is the efficient utilization of energy resources. Maximum energy is lost during communication in terms of transmission power. The minimum and maximum value of transmission power depends on the node density for any particular network. In this research work the transmission power which is generally considered to be fixed for any node is subjected to be changed by taking random values between minimum and maximum transmission power as directed by the proposed neural network model. The idea cab be used in any simulation technique to enhance the lifetime of WSN. It proves that the network can save the energy considerably.

Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO With Lens Antenna Array

In this paper, we propose a downlink intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) for millimeter-wave (mmWave) massive MIMO with lens antenna array, i.e., IRS-aided mmWave beamspace NOMA, where the single-antenna users without direct-link but connected to the base station (BS) with the aid of the IRS are grouped as one NOMA group. Considering the power leakage problem in beamspace channel and the per-antenna power constraint, we propose two multi-beam selection strategies for the BS-IRS link under two channel models, i.e., 2-dimension (2D) channel model and 3-dimension (3D) channel model, respectively, where two corresponding RF chain configuration strategies are designed, respectively. Then, we formulate and solve the optimization problem for maximizing the weighted sum rate by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, where we propose the alternating optimization (AO) method to solve the above joint optimization problem. Especially, different from the stochastic method, based on the beam-splitting technique, we propose the method to initialize the feasible solution for the proposed AO method, where the conditional transmit power minimization problem is formulated and solved. Through simulations, the weighted sum rate performance of the proposed IRS-aided mmWave beamspace NOMA is verified.