Transmission Power Control Scheme Research Articles

Spectral Efficiency Improvement in Downlink Fog Radio Access Network With Deep-Reinforcement-Learning-Enabled Power Control

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This paper considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep reinforcement learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is especially suited to scenarios with non-negligible HWIs-induced distortion.

Interference Mitigation Strategy for D2D Communication in 5G Networks

Device-to-Device (D2D) communication is one of the most promising developments in 5G networks. D2D communication can reduce communication latency and increase spectrum efficiency and system capacity. However, implementing D2D communication in reuse mode with a traditional cellular network can result in severe interference that can significantly reduce network performance, as both networks share the same resources. It is essential to mitigate interference to improve network capacity when D2D communication coexists with traditional cellular networks. An effective power control strategy can help mitigate the potential detrimental impact of interference and maximize the potential benefits of D2D communication. In this study, a dynamic transmission power control strategy was proposed for D2D transmitters to allow them to dynamically adjust transmission power, aiming to minimize interference and maximize network capacity. The distance between and the number of D2D pairs that experience interference were the primary parameters considered for the proposed strategy, while its complexity was in polynomial time. A simulation was carried out to evaluate the proposed strategy and compare it with fixed and ranged-based approaches, and the results validated its effectiveness.

Joint load-balancing and power control strategy to maximize the data extraction rate of LoRaWAN networks

LPWAN enabled networks have a dizzying growth and continue to meet an essential need in the Internet of Things market due to their ability to provide low-cost wireless access to geographically spread-out devices. Consequently, an efficient allocation of wireless resources in order to support numerous devices becomes a major concern. In this paper, we propose an SF assignment approach in LoRa networks, paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the end-devices on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations, taking into account the capture effect and the non-orthogonality of SFs. Furthermore, we extended the solution by a transmission power control strategy for overall system energy-efficiency. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model, which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of Date Extraction Rate (DER) while guaranteeing good scalability on the network size and density.

Optimal Achievable Transmission Capacity Scheme for Full-Duplex Multihop Wireless Networks.

Full-duplex (FD) communication has been attractive as the breakthrough technology for improving attainable spectral efficiency since the 5G mobile communication system. Previous research focused on self-interference cancellation and medium access control (MAC) protocol to realize the FD system in wireless networks. This paper proposes an optimal achievable transmission capacity (OATC) scheme for capacity optimization in the FD multihop wireless networks. In this paper, the proposed OATC scheme considers the temporal reuse for spectral efficiency and the spatial reuse with transmit power control scheme for interference mitigation and capacity optimization. OATC scheme controls the transmit power to mitigate interference and optimizes the transmission capacity, which leads to the optimal achievable network capacity. We conduct the performance evaluation through numerical simulations and compare it with the existing FD MAC protocols. The numerical simulations reveal that considering only the concurrent transmissions in the FD system does not guarantee optimal transmission capacity. Moreover, the hybrid mechanism, including the sequential transmissions, is also crucial because of the interference problem. Besides, numerical simulation validates that the proposed OATC scheme accomplishes the optimal achievable network capacity with lower interference power and higher achievable throughput than the existing MAC protocols.

Joint Analog Beamforming and Trajectory Planning for Energy-Efficient UAV-Enabled Nonlinear Wireless Power Transfer

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled multi-user network with nonlinear wireless power transfer (WPT), where multiple user sensors are distributed on the ground. Acting as an energy source, the UAV operates at a fixed height and transfers energy to the multiple sensor nodes (SNs) via wireless signals. For more efficient energy harvesting (EH), an antenna array has been installed on UAV with a structure of three dimensional (3D) uniform linear array (ULA), which enables the UAV to perform analog beamforming for power concentration. Taking into account the UAV energy consumption and considering a practical nonlinear EH model, we characterize the UAV energy efficiency particularly for WPT task and subsequently formulate an efficiency maximization problem, in which the analog beamforming and UAV trajectory planning are jointly determined together with the transmit power control scheme. To deal with the nonconvex joint optimization problem, we first propose a cosine-based approximation for the complicated 3D ULA antenna pattern, in which a convex property is proved. Combining with the proved convexity in nonlinear EH model, through a series of mathematical analysis, we construct a convex subproblem based on any feasible point, solving which guarantees an improvement of the energy efficiency. Afterwards, an iterative algorithm is proposed for iteratively addressing the joint design until a convergence to a suboptimal solution. Via simulations, we verify the convergence and performance advantages of our proposed iterative solution. Among the solution, different beamforming preferences regarding the beam coverage enlargement and power concentration are also observed with respect to different antenna array scales.

On Adaptive Transmission for Distributed Detection in Energy Harvesting Wireless Sensor Networks With Limited Fusion Center Feedback

We consider a wireless sensor network tasked with solving a binary distributed detection problem. Sensors communicate directly with a fusion center (FC) over orthogonal fading channels, with additive Gaussian noise. Each sensor can harvest randomly arriving energy units and store them in a battery. Also, it knows its quantized channel state information (CSI), acquired via a limited feedback channel from the FC. Modeling the randomly arriving energy units during a time slot as a Poisson process and the battery dynamics as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -state Markov chain (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> is the battery size), we propose a channel-dependent transmit power control strategy such that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula> -divergence based detection metric is maximized at the FC, subject to an average transmit power per sensor constraint. The proposed strategy is parametrized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals. This strategy allows each sensor to adapt its transmit power based on its battery state and its qunatized CSI. Finding optimal strategy requires solving a non-convex optimization problem that is not differentiable with respect to the optimization variables. We propose near-optimal strategies based on random search methods that have a low-computational complexity and provide a close-to-optimal performance.

Hybrid of Angular and Distance Protection for Coexistence of 5G Base Stations and Satellite Earth Stations

In this study, we investigated the coexistence of the 5G communication network with a fixed-satellite service (FSS) in the 3.5 GHz and 26 GHz frequency bands. We analyzed an angular protection scheme for the FSS Earth station (ES) and 5G base stations (BSs). In addition, we defined the fixed BS-ES relative location, relative distance, and angular changes. The angular protection was integrated into the exclusion and restricted zones proposed by the distance protection scheme for simulation analysis to develop a transmit power control scheme based on the Citizens Broadband Radio Service. Its performances were extensively analyzed through simulations. The proposed scheme was evaluated in practical scenarios: rural macrocells, urban macrocells, and urban microcells, as defined by the 3rd Generation Partnership Project (3GPP). The influence of antenna type was also researched, and BSs with 4 × 4, 8 × 8 and 16 × 16 antenna arrays, as specified by 3GPP, were considered for 5G networks. Finally, the results prove that the angular protection solution can solve the coexistence of the 5G system and the FSS. In addition, when angular protection and distance protection are used simultaneously, the coexistence effect of the two systems can be strengthened. Moreover, this work provides a quantitative perception for selecting system parameters, including an interference margin for the different scenarios and antenna types, the exclusion size, and the reduction area.

Transmission Power Control and Relay Strategy for Increasing Access Rate in Device to Device Communication

In cellular networks, Device to Device (D2D) communication can improve spectral efficiency by enabling proximity users to communicate directly without traversing the Base Station (BS). However, if not designed properly, the interference generated by D2D transmissions, may deteriorate the communication quality of the existing cellular and D2D users. In this paper, we study transmission power control-based interference management to increase access rate and sum rate while guaranteeing the Quality of Service (QoS) requirements for both D2D and cellular users. A four-step framework is proposed. First candidate D2D groups, potentially admissible for transmission, are arranged in order with respect to their distance from BS and required Signal to Interference and Noise Ratio (SINR). Next, upper and lower bounds on the transmission power of the transmitters are calculated to determine the admissibility of the aspirant D2D group subject to the QoS requirements of the aspirant group, other scheduled D2D groups, and the cellular user. Then, relay-based communication protocol is considered for admitting the D2D groups that cannot be admitted directly. Finally, sum rate of each shared channel is improved through iterative incrementation of transmission powers of cellular and D2D transmitters constraint by the QoS requirements of all the admitted D2D groups and the cellular user and the existing sum rate. Simulation results show that the proposed framework can improve the access rate many folds across all the considered scenarios as compared to the baseline schemes. Moreover, higher access rates of the proposed scheme translate into significantly higher sum rates.

Dynamic uneven clustering protocol for efficient energy management in EH-WSNs

In recent days, the Energy Harvesting Wireless Sensor Networks (EH-WSNs) are designed to reduce the amount of energy used by sensors. In order to reduce the energy consumption of these components, the new algorithm namely Dynamic Uneven Clustering Protocol (DUCP) is proposed that will automatically cluster the harvested energy sources. The protocol aims to improve the energy management of a cluster head (CH) by selecting better performing nodes. Since rechargeable batteries have a limited capacity, a dynamic transmit power control strategy is devised for both CHs and cluster members during the data gathering process to improve collected energy consumption. A number of tests are conducted to verify the performance of the proposed DUCP algorithm, and the results demonstrate that the DUCP technique effectively utilizes energy and outperforms its rivals.

Robust Transmit Power Control With Imperfect CSI Using a Deep Neural Network

In this paper, a robust transmit power control scheme is proposed for multi-channel underlay device-to-device (D2D) communications with imperfect channel state information (CSI). The transmit power of the D2D user equipment (DUE) on each channel is optimized to maximize the average spectral efficiency (SE) whilst maintaining the quality-of-service (QoS) of the cellular user equipment (CUE) in the presence of errors in the CSI. To this end, we propose a novel deep neural network (DNN) structure and training methodology, in which artificially distorted CSI is used to compensate for the effect of imperfect CSI, such that a robust transmit power control strategy against channel error can be derived. Our simulation results show that even when the CSI is inaccurate, in our proposed scheme the degradation of the average SE can be kept low whilst maintaining negligible QoS violation, thereby confirming its effectiveness and robustness.

Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station

In this paper, we investigate the coexistence of the 5G communication network with a fixed-satellite service (FSS) in the 3.5 GHz and 26 GHz frequency bands. We analyze a distance protection scheme for the FSS Earth station (ES) and 5G base stations (BS). Furthermore, we define the exclusion and restriction zones to develop a transmit power control scheme based on the Citizens Broadband Radio Service (CBRS). An interactive power control scheme is also devised for the restriction zone and extensively analyzed through simulations. The proposed scheme is examined for practical scenarios such as the rural macrocells (RMa), urban macrocells (UMa), and urban microcells (UMi) as defined by the 3GPP. The impact of the antenna type is also investigated, and BSs with omnidirectional, 4 × 4, 8 × 8, and 16 × 16 antenna arrays are examined, as defined by 3GPP, for the 5G networks. The results confirm that 5G systems can coexist with the FSS and provide quantitative insights into the selection of the system parameters, including interference margins, exclusion sizes, and reduction zones, for different scenarios and antenna types.

Deep Learning-Aided Distributed Transmit Power Control for Underlay Cognitive Radio Network

In this paper, we investigate deep learning-aided distributed transmit power control in the context of an underlay cognitive radio network (CRN). In the proposed scheme, the fully distributed transmit power control strategy of secondary users (SUs) is learned by means of a distributed deep neural network (DNN) structure in an unsupervised manner, such that the average spectral efficiency (SE) of the SUs is maximized whilst allowing the interference on primary users (PUs) to be regulated properly. Unlike previous centralized DNN-based strategies that require complete channel state information (CSI) to optimally determine the transmit power of SU transceiver pairs (TPs), in our proposed scheme, each SU TP determines its own transmit power based solely on its local CSI. Our simulation results verify that the proposed scheme can achieve a near-optimal SE comparable with a centralized DNN-based scheme, with a reduced computation time and no signaling overhead.

Power Control for Body Area Networks: Accurate Channel Prediction by Lightweight Deep Learning

Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications’ reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.

Achievable Rate Characterization of NOMA-Aided Cell-Free Massive MIMO With Imperfect Successive Interference Cancellation

This paper investigates the throughput improvement of cell-free massive multiple-input multiple-output (MIMO) systems by non-orthogonal multiple access (NOMA) for future cellular networks under stochastic access point and user locations. In this context, the node locations are modeled with Poisson point processes. The time division duplexing mode is employed, and uplink channels are estimated locally using uplink pilots. Furthermore, unique pilot sequences are used between NOMA clusters, while pilot reuse occurs within each cluster to strike a balance between the training overhead and the number of clusters. Matched-filter-based precoding is utilized for downlink transmission. The aggregate received signal is analytically characterized by deriving the moment generating function and approximations via moment matching. Then, the asymptotic achievable rates of the NOMA users are derived, thereby quantifying the adverse impact of error propagation owing to imperfect successive interference cancellation. Special scenarios with prior downlink channel state information and log-distance power control are also considered. We show that NOMA greatly increases the achievable average rate, especially under low path loss exponents and dense networks, while user fairness may be boosted by the adoption of a log-distance transmit power control scheme with proper parameter selection (i.e. lower values for the power control parameter).

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> ) without any cellular support. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> , an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4</i> , in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> outperforms the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> in both urban and highway scenarios especially in highly dense environments.

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.

Outage-Optimal and Suboptimal Power Control for D2D Communications in SWIPT Cellular Networks With Local CSI

In this letter, we consider a downlink cellular network where a cellular user (CU) not only decodes information but also harvests energy. By reusing the spectrum resource of the CU, a device-to-device (D2D) transmitter sends its own signals to a D2D receiver. In order to protect the CU from the interference due to the D2D signals, we propose transmit power control schemes for the D2D transmission without increasing the outage probability of the CU. This is possible since the CU’s information loss due to the D2D signals can be compensated by the CU’s energy harvesting gain from the D2D signals. For asymptotic performance analysis, we provide a lower bound on the D2D outage probability. The numerical results show that the proposed schemes outperform the conventional scheme.

Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

Game Theory on Power Control in Wireless Sensor Networks Based on Successive Interference Cancellation

In infrastructure-less wireless sensor networks, with the requirements of saving energy and improving the network throughput, this paper proposes a non-cooperative game theory power control strategy based on CDMA wireless sensor networks. Based on (successive interference cancellation, SIC) of multi-packet reception theory in sink nodes, transmission power control scheme for sensor nodes is proposed. Specifically, this paper combines SIC algorithm and non-cooperative game theory, it discusses the Nash Equilibrium for the power control strategy, and it also proposes that as long as transmission power of sensor nodes meet transmission power threshold, it can satisfy non-cooperative game theory power control. Compared with other power control strategies in WSN, simulation results prove that the non-cooperative game theory power control scheme is effective; it also saves sensor nodes’ energy, obtains good performance for the whole network and greatly improves the network lifetime.

Adaptive Power Control for D2D Communications in Downlink SWIPT Networks With Partial CSI

In this letter, we consider the device-to-device (D2D) communication in downlink cellular networks, where the cellular user (CU) both decodes information and harvests the energy. Unlike the conventional D2D communications, we figure out that it is possible for the D2D pair to communicate without degrading the performance of the CU. Based on this observation, D2D transmit power control schemes are proposed for D2D rate maximization subject to maintaining the CU performance. In a decentralized way, the proposed power control schemes are performed at the D2D transmitter (DT) with instantaneous channel state information (CSI) of only the link between the D2D pair. For performance analysis, an asymptotic upper bound is provided on the D2D rates of the proposed schemes. Numerical results confirm the effectiveness of the proposed schemes.