Transmission Power Control Algorithm Research Articles

Joint Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Networks and Beyond.

Spectral efficiency is a crucial metric in wireless communication systems, as it defines how much information can be transmitted over a given amount of spectrum resources. Non-orthogonal multiple access (NOMA) is a promising technology that has captured the interest of the wireless research community because of its capacity to enhance spectral efficiency. NOMA allows multiple users to share the same frequency band and time slot by assigning different power levels and modulation schemes to different users. Furthermore, channel assignment is a critical challenge in OFDMA-NOMA systems that must be addressed to achieve optimal performance. In this context, we propose a solution for both channel and power assignment based on channel condition by splitting the problem into two parts: first, we introduce a novel algorithm to solve the channel user allocation problem, which we refer to as Channel User Sorting and Filling (CUSF). Then, we solve the power allocation problem in two steps: we apply the water filling algorithm at the power assignment and then we implement the Fractional Transmit Power Control (FTPC) algorithm in the NOMA power assignment.

Distributed and Secure Uplink Power Control in Dynamic Spectrum Access

In dynamic spectrum access (DSA), secondary users (SU) should only be allowed to access a licensed band belonging to incumbent users (IU) when the quality-of-service (QoS) requirements of both IUs and SUs can be satisfied at the same time. However, IU’s location and its received interference strength are considered sensitive in many DSA systems which should not be revealed, making it very challenging to optimize the network utility subjected to satisfying the operation and security requirements of SUs and IUs. In this paper, we develop a secure and distributed SU transmit power control algorithm to solve this challenge. Our algorithm achieves optimal SU power control to maximize the sum of SU rates. The SINR-guaranteed coexistence between SUs and IUs are enabled to maintain effective communication, while no information is directly required from IUs. Local measurements of IU signals provided by Environmental sensing capability (ESC) also undergo a security masking process to ensure that IU location cannot be derived from its outputs. Convergence and stability properties of our algorithm and its privacy-protection strength are both theoretically analyzed and experimentally evaluated through simulations.

DNN-Based Dynamic Transmit Power Control for V2V Communication Underlaid Cellular Uplink

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES).

Lower boundary based nonlinear model predictive control of transmission power for smart grid WSNs

In the application of data collection and communication in smart grid based on wireless sensor networks (WSNs), communication reliability is the key technical index of WSNs. Theoretically, adjusting the transmission power can control the signal-to-noise ratio (SNR), thereby improving the reliability of wireless communication. However, wireless signal is random changes caused by external device interference or environmental factors. Hence, we study a control of transmission power to meet reliability requirements while maximizing the overall efficiency of the network. Considering the random characteristics of wireless links, we introduce the predictive lower boundary with probability guarantees, and establish a nonlinear state-space model that chooses the SNR as the state variable. Subsequently, we adopt the lower boundary based nonlinear model predictive control (LB-NMPC) algorithm to solve the optimal transmission power, and creatively prove the feasibility and stability. Furthermore, we compare the proposed LB-NMPC algorithm with the adaptive transmission power control (ATPC) algorithm, the potential feedback control (PFC) algorithm and the confidence interval based model predictive control (CI-MPC) algorithm by simulation, and the comparative results show that our LB-NMPC algorithm has lower transmission power and less bad controls. Finally, we also test the LB-NMPC algorithm in real-world indoor substation and outdoor substation to verify its practicability and accuracy.

Decentralized Multi-Agent Power Control in Wireless Networks With Frequency Reuse

Many of the existing optimization-based transmit power control algorithms suffer from high computational complexity and require instantaneous global channel state information (CSI), both of which hinder their practical implementation. In this paper, we consider a wireless network with multiple transmitter-receiver pairs, where each transmitter only has access to its local CSI fed back from its intended receiver and does not require local CSI exchange with its neighboring transmitters. In such a network scenario, we propose a deep reinforcement learning based decentralized multi-agent power control (DEC-MAPC) algorithm for sum-rate maximization, where each transmitter acts as an intelligent agent. By leveraging the value decomposition technique, we establish a nonlinear mapping from the local reward of each agent to the global reward. Such a design allows each agent to independently control its transmit power based on its local CSI while enabling global collaboration among the agents. The proposed algorithm is scalable to large-scale networks as only local CSI is required, and is robust to the channel and interference variations via interacting with the environment. Simulation results show that the proposed DEC-MAPC algorithm with local CSI achieves comparable sum-rate performance with the centralized optimization algorithms with global CSI, while significantly reducing the computational complexity.

Cooperative UAV Enabled Relaying Systems: Joint Trajectory and Transmit Power Optimization

Unmanned aerial vehicles (UAVs) can be used as aerial relays to provide quick and on-demand wireless connections for the area without network coverage. Since a UAV’s onboard energy is limited, how to prolong the duration of UAV relaying communication is challenging. In this paper, we consider applying multiple UAVs to extend the duration of communication via UAV collaboration. We first propose a UAV collaboration scheme, namely heuristic UAV substitution, which lets the UAV relays work one by one individually. Then, we propose another scheme called spectral efficient UAV substitution to improve the spectrum efficiency. Furthermore, under these two proposed schemes, we jointly design the trajectories of the UAV relays and the transmit power of the source and the UAV relays, with the goal of end-to-end throughput maximization. Although the considered optimization problem is non-convex and difficult to solve, we propose an efficient algorithm to find a suboptimal solution to it by applying the block coordinate ascent and successive convex approximation methods. Simulation results show that the proposed UAV substitution schemes can effectively extend the relaying communication duration, and the throughput performance of the proposed joint trajectory optimization and transmit power control algorithm outperforms some benchmark algorithms without considering joint design.

Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing.

A High Altitude Platform Station (HAPS) can facilitate high-speed data communication over wide areas using high-power line-of-sight communication; however, it can significantly interfere with existing systems. Given spectrum sharing with existing systems, the HAPS transmission power must be adjusted to satisfy the interference requirement for incumbent protection. However, excessive transmission power reduction can lead to severe degradation of the HAPS coverage. To solve this problem, we propose a multi-agent Deep Q-learning (DQL)-based transmission power control algorithm to minimize the outage probability of the HAPS downlink while satisfying the interference requirement of an interfered system. In addition, a double DQL (DDQL) is developed to prevent the potential risk of action-value overestimation from the DQL. With a proper state, reward, and training process, all agents cooperatively learn a power control policy for achieving a near-optimal solution. The proposed DQL power control algorithm performs equal or close to the optimal exhaustive search algorithm for varying positions of the interfered system. The proposed DQL and DDQL power control yields the same performance, which indicates that the actional value overestimation does not adversely affect the quality of the learned policy.

Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control Algorithms

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.

Energy-Efficient Secure Communications for Wireless-Powered Cognitive Radio Networks.

In this study, we investigate energy-efficient secure communications for wireless-powered cognitive ratio networks, in which multiple secondary users (SUs) share the same frequency band with primary users (PUs) and energy harvesting (EH) nodes harvest energy from the transmitted signals, even though information decoding is not permitted. To maximize the average secrecy energy efficiency (SEE) of SUs while ensuring acceptable interference on PUs and the required amount of energy for the EH nodes, we propose an energy-efficient transmit power control algorithm using dual decomposition, wherein suboptimal transmit powers are determined in an iterative manner with low complexity. Through extensive simulations in various scenarios, we verify that the proposed scheme has a higher average SEE than conventional schemes and a considerably shorter computation time than the optimal scheme.

Low-Complexity Transmit Power Control for Secure Communications in Wireless-Powered Cognitive Radio Networks.

In this study, wireless-powered cognitive radio networks (WPCRNs) are considered, in which N sets of transmitters, receivers and energy-harvesting (EH) nodes in secondary networks share the same spectrum with primary users (PUs) and none of the EH nodes is allowed to decode information but can harvest energy from the signals. Given that the EH nodes are untrusted nodes from the point of view of information transfer, the eavesdropping of secret information can occur if they decide to eavesdrop on information instead of harvesting energy from the signals transmitted by secondary users (SUs). For secure communications in WPCRNs, we aim to find the optimal transmit powers of SUs that maximize the average secrecy rate of SUs while maintaining the interference to PUs below an allowable level, while guaranteeing the minimum EH requirement for each EH node. First, we derive an analytical expression for the transmit power via dual decomposition and propose a suboptimal transmit power control algorithm, which is implemented in an iterative manner with low complexity. The simulation results confirm that the proposed scheme outperforms the conventional distributed schemes by more than 10% in terms of the average secrecy rate and outage probability and can also considerably reduce the computation time compared with the optimal scheme.

Adaptive transmit power control algorithm for dynamic LoRa nodes in water quality monitoring system

Energy scarcity is a key concern in wireless sensor systems due to the limited energy budget of batteries. In a sensor system, transmit power is one of the most significant sources of energy drain. In such scenarios, effective power management strategy is crucial as it determines the lifetime of the sensor node and the system. This paper proposes an adaptive transmission power control algorithm (ATPCA) for GPS (Global positioning system) enabled LoRa (long range) nodes to reduce power consumption. This power reduction is achieved by varying the transmission power of the dynamic end node with respect to the distance between transmitter and receiver. The algorithm is implemented in a water quality monitoring system, that gives a maximum energy saving of 40%.

Distributed Transmit Power Control for Energy-Efficient Wireless-Powered Secure Communications.

In this study, we consider energy-efficient wireless-powered secure communications, in which N sets of transmitter, receiver, and energy harvesting (EH) nodes exist; each EH node is allowed only to harvest energy from the transmitted signals but is not to permitted to decode information. To maximize the sum secrecy energy efficiency (SEE) of the node sets while ensuring minimum EH requirement for each EH node, we propose a distributed transmit power control algorithm using a dual method, where each transmitter adjusts its transmit power iteratively until convergence without sharing information with the other node sets. Through simulations under various environments, we show that the proposed scheme surpasses conventional schemes in terms of the sum SEE and has significantly reduced computation time compared with the optimal scheme, which suggests the effectiveness and applicability of the proposed distributed method.

Game theory based adaptive transmit power control algorithm for IoT wireless sensor networks

Objectives: To design a transmission power control (TPC) algorithm for the Internet of Things (IoT) based Wireless Sensor Networks (WSN) such that the energy cost is reduced and the expected throughput is increased. Method: In this study, a Game theory based adaptive transmit power control (GT-ATPC) algorithm for IoT-WSN is proposed. In this algorithm, for each sender, the initial transmission power level (TPL) is determined from the difference of Received Signal Strength (RSS) measurements at the base station (BS). The TPL is adaptively adjusted by means of Game theory. For each sender, the expected throughput, energy cost and connectivity values are estimated from which a utility function is derived. Findings: By simulation results, it has been shown that GT-ATPC attains 37% higher throughput with 57% reduced energy cost and 30% lesser overhead when compared with GTS-PM approach. Novelty: The TPL of a node is adjusted based on the estimated values of utility function such that the throughput and connectivity is high and energy cost is less. Keywords: Internet of Things (IoT); wireless sensor network (WSN); transmission power; Energy cost; game theory; utility function

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

Energy Harvesting Powered Sensing in IoT: Timeliness Versus Distortion

We consider an Internet of Things (IoT)-based sensing system, in which an energy harvesting powered sensor observes the phenomenon of interest and transmits its observations to a remote monitor through a Gaussian channel. Based on the received signals, the monitor makes estimations of source signals with some distortion requirement. We measure the timeliness of the recovered signals using the Age of Information (AoI), which could be reduced by transmitting observations more frequently (i.e., with shorter intervals). We evaluate the recovery distortion with the mean-squared-error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor is quite limited, however, the frequency and power of transmissions cannot be increased at the same time. Thus, we shall investigate the timeliness-distortion tradeoff of the system by minimizing the average weighted sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we investigate the offline optimization of the system and propose a backward water-filling-based power allocation scheme, as well as a genetic-based joint transmission scheduling and power control algorithm. Third, we formulate the online power control as a Markov decision process (MDP) and solve the problem with an iterative algorithm, which closely approaches the tradeoff limit of the system. We show that the optimal transmit power is a monotonic and bivalued function of current AoI and distortion. Finally, we present our results via simulations and extend the results on the save-and-transmit policy to fading sensing systems.

Analysis of Data Rate and Transmission Power Hybrid Control in WSN IoT

The relationship between data rate and transmission power is analyzed in this paper. Moreover, an algorithmcombined with adaptive data rate (ADR) and transmission power control (TPC) is proposedin this paper, and it improves battery life significantly on IoT devices. In the premise that withoutprejudicing the accuracy of wireless transmission, the faster data rate and lower transmission powercan be used to reduce power consumption. The results of a longtime test show that the nodes withthe proposed algorithm not only achieve power saving when good transmitting environment, but alsoaccomplish stable communication in the harsh environment. In the best case, compared with the noalgorithmnodes, the nodes with the ADR and TPC algorithms can be reduced power consumptionup to 50%.

Adaptive Transmit Power Control Algorithm for Sensing-Based Semi-Persistent Scheduling in C-V2X Mode 4 Communication

For cellular-based vehicle-to-everything (C-V2X) communication, vital information about status and intention is periodically broadcasted by each vehicle using the cooperative awareness message (CAM) service. In C-V2X, the task of resource allocation can either be carried out in a centralized manner by the network, termed Mode 3, or by the vehicles themselves in a distributed manner without any core network support, termed Mode 4. Mode 4 scheduling is accomplished by employing sensing-based semi-persistent scheduling (SB-SPS), where the vehicles sense the medium and identify the best time-frequency resource combination for transmission of the CAM. Focusing on Mode 4 in this paper, we present a comprehensive analysis of the impact of variations in the transmit power of the vehicle on the performance of SB-SPS for C-V2X communications in various traffic scenarios through simulations. An adaptive-transmit power control (A-TPC) algorithm is presented to improve the quality of service for various large-scale traffic scenarios, where each vehicle uses real-time channel-sensing information to adjust the transmit power in order to avoid interference with neighbouring vehicles. The results demonstrate that our proposed algorithm outperforms the conventional TPC schemes.

A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect

Capture effect has been shown as a physical layer (PHY) phenomenon of modern wireless devices that improves the performance of wireless local area networks (WLANs) in terms of throughput. In this paper, however, we explore the effect of PHY capture in the domain of energy efficiency. Analysis model that takes into account the effect of PHY capture is backed up by ns-2 simulations show that capture effect improves energy efficiency of WLAN by 20%. This improvement, however, results in unfairness, i.e, a group of nodes located far away from the Access Point (AP) is three times less energy efficient than the group of nodes located closer to the AP. To resolve the unfairness caused by the capture effect, furthermore, this paper proposes a joint strategy of adaptive transmission power control (ATXPR) and contention window adjustment (CWADJ). Namely, a node that suffers transmission failure due to another node capturing the channel steps up its transmission power according to the transmission power control algorithm and refrains from increasing its contention window according to contention window adjustment mechanism, respectively. Our proposed joint strategy is 99% fair while maintaining overall energy efficiency of the network.

Non-Cooperative Beacon Power Control for VANETs

In vehicle-to-vehicle communication, every vehicle broadcasts its status information periodically in its beacons to create awareness for surrounding vehicles. However, when the wireless channel is congested by beaconing activity, many beacons are lost due to packet collision. This paper presents a distributed beacon transmission power control algorithm to control channel usage at a desired level. The algorithm is based on game theory, for which the existence and uniqueness of the Nash equilibrium is proven. The proposed algorithm is then compared with other congestion control mechanisms using simulation. The results of the simulations indicate that the proposed algorithm is stable and performs better than the others in terms of fairness, bandwidth usage, and the ability to meet application requirements.

Massive MIMO Relay Networks With Underlay Spectrum Sharing

The achievable rates of multicell/multiuser massive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing are investigated for imperfect/perfect channel state information (CSI). The transmit power constraints of the secondary user nodes and relays are derived. A max–min fairness optimal secondary transmit power control algorithm is formulated, and thereby, optimal power allocation coefficients and the maximum common achievable user rates are derived. The detrimental effects of intracell and intercell pilot contamination are studied for three pilot sequence designs (PSDs). Thereby, it is shown that for some PSDs and CSI assumptions, the secondary network can be operated at the peak average transmit power without degrading the performance of the primary network when the base-stations are equipped with massive MIMO. Consequently, the asymptotic achievable rates become independent of the primary interference temperature. For the worst-case scenario of full pilot reuse in the primary and secondary networks as well as in the co-channel cells, the achievable rates are severely degraded by intra-cell and inter-cell pilot contamination. Counter-intuitively, a significant portion of the achievable rate gains observed under the perfect CSI assumption can still be obtained by using a PSD with a fundamental trade-off between system performance and practical viability.