Transmission Power Control Research Articles

Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network

In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing. We also propose a form of DPC that can be performed in a distributed manner with local channel state information, allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.

Geometry Distance Estimated MAC Protocol for TV White Space

Regulatory bodies worldwide have quite recently approved the dynamic access of unlicensed networks to the TV white space (TVWS) spectrum. In TVWS, secondary users (SUs) are required to access a database periodically to acquire information on the spectrum usage of primary users (PUs). The spectrum database querying of SUs will solve the spectrum scarcity problem. A key technology in efficiently using the TVWS spectrum is designing an efficient medium access control (MAC) protocol. In this paper, we propose a geometry distance estimated medium access control (GMAC) protocol to mitigate the hidden and exposed terminal problems in TVWS. GMAC provides an efficient means of channel database querying to obtain the list of available channels to overcome interference with PUs. We develop a Markov chain model to characterize the saturation normalized throughput of our proposed GMAC protocol for the TVWS. In this study, transmitter power control is based on the geometrically estimated distance between SU communication pairs; this increases channel spatial reuse and throughput, and reduces PU outage probability. We also compare our proposed scheme with existing MAC protocols for TVWS. We show that the GMAC will improve the channel spatial reuse and normalized throughput, and reduce PU outage probability.

Power Management for Kinetic Energy Harvesting IoT

Human kinetic energy is regarded as a promising sustainable energy source to solve the energy bottleneck of Internet of Things (IoT). The low power harvested from human motion and scarce hardware resource of IoT severely restrain the operation of kinetic energy harvesting IoT and stress the need for power management strategies to improve the energy efficiency. In this paper, we propose a novel power management framework for kinetic energy harvesting IoT, composed of an off-line inertial harvester optimization algorithm and an on-line joint sink selection and transmission power control module. By analyzing the characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize the power generation from human daily motion. The on-line scheme improves energy efficiency by joint consideration of optimal sink selection (i.e., on-body sink or off-body sink) and transmission power control. The real world human motion data set is used to evaluate the proposed framework. The simulation results indicate that, compared with the existing approach, the proposed kinetic harvester optimization algorithm achieves 83.31% to 135.69% improvement in harvested power from the same human motion trace. In addition, the proposed on-line joint sink selection and transmission power control incurs 7.07% to 34.23% improvement in transmission energy efficiency.

STMAC: Spatio-Temporal Coordination-Based MAC Protocol for Driving Safety in Urban Vehicular Networks

In this paper, we propose a spatio-temporal coordination-based media access control (STMAC) protocol for efficiently sharing driving safety information in urban vehicular networks. STMAC exploits a unique spatio-temporal feature characterized from a geometric relation among vehicles to form a line-of-collision graph, which shows the relationship among vehicles that may collide with each other. Based on this graph, we propose a contention-free channel access scheme to exchange safety messages simultaneously by employing directional antenna and transmission power control. Based on an urban road layout, we propose an optimized contention period schedule by considering the arrival rate of vehicles at an intersection in the communication range of a road-side unit to reduce vehicle registration time. Using theoretical analysis and extensive simulations, it is shown that STMAC outperforms legacy MAC protocols especially in a traffic congestion scenario. In the congestion case, STMAC can reduce the average superframe duration by 66.7%, packet end-to-end delay by 68.3%, and packet loss ratio by 88% in comparison with the existing MAC protocol for vehicle-to-infrastructure communication, based on the IEEE 802.11p.

Adaptive Control of IoT Wireless Networks in Shadowed Fading Channels

In this paper, a distributed transmission power control (TPC) scheme that jointly addresses the hidden and exposed terminal problem to enhance the spatial reuse and fairness performance of multi-hop wireless networks is proposed. Unlike other previous transmission power control schemes where the two-ray ground reflection (TRG) model is adopted, the proposed scheme is designed based on a log-normal shadowing (LNS) channel model. In addition, transmission power selection strategies to further compensate the influences of shadowing are presented. Results demonstrate that the proposed scheme outperforms the other TPC schemes in terms of spatial reuse and power consumption, resulting in an improved throughput and energy efficiency.

SINR and throughput improvement for VANET using fuzzy power control

SummaryThis paper proposes transmission power control for vehicular ad hoc network (VANET) using fuzzy system. Despite the potential advantages of VANET especially for safety and intelligent transportation system, some challenges are discovered during the implementation of VANET. Main challenges emerge because of the dynamic environment and high mobility of vehicle. Furthermore, the interference due to the shared‐spectrum usage can significantly decrease the quality of signal. Fuzzy system is implemented to control the transmission power based on the signal to interference and noise ratio (SINR) difference with the targeted value of the receiver vehicle and the interference inflicted by the transmitter vehicle. The algorithm of fuzzy power control for VANET is proposed, and the performance is evaluated through the simulations. The results of simulations show that the proposed algorithm can increase SINR of vehicles especially the vehicles with SINR value below the target. Thus, the average of SINR and the throughput of the system can be increased as well.

5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices

Fog computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods.

Hidden Terminal Aware Clustering for Large-scale D2D Networks

In device-to-device (D2D) environments where a number of wireless nodes are connected in ad hoc mode, the most popular mechanism for multiple access control is CSMA/CA as in IEEE 802.11 networks. Consequently, a considerable number of hidden terminals can be found in large-scale and dense D2D networks, and usually they may cause severe performance problems. Previous studies to improve the hidden terminal problem in dense wireless networks have suggested various solutions: e.g., channel assignment, transmit power control, and grouping schemes. In this paper, we adopt a different approach—a hidden terminal aware clustering mechanism, which detects the hidden terminals and enables the nodes to form clusters based on the detected information. A simple polling-based channel access scheme is also proposed such that all nodes belonging to a same cluster can access channel through their neighbors’ polling, which eliminates the hidden terminal problem. Mathematical analysis and simulations show that our proposed scheme significantly improves the performance of large-scale D2D networks.

A fuzzy approach for power savings in both infrastructure and ad hoc WLANs

Energy efficiency is a main issue in a wireless local area network. Reducing energy consumption lowers the environmental impact of the network and it prolongs the lifetime of the devices, which are usually powered by means of batteries. The basic mechanisms for energy savings specified in the IEEE 802.11 standard are transmit power control, which directly affects the energy cost of the communication, and alternation of awake and sleep states, as the battery consumption in sleep state is negligible. However, low transmission power and long sleeping time reduce the message success rate and the responsiveness of the network. This paper presents a fuzzy logic solution for dynamically adjusting both transmission power and sleeping time in an IEEE 802.11 wireless network. The proposed approach is validated through simulations, which show a battery life 20% longer than other state-of-the-art approaches, and by means of a real testbed, which confirms the simulated results.

Range-Based Scheme for Adjusting Transmission Power of Femtocell in Co-Channel Deployment

In heterogeneous networks, femtocells are introduced to improve the network capacity. However, dense deployment of femtocells leads to undesirable interference, which degrades the performance of the system. The interference can be managed and mitigated based on different approaches such as resource allocation or transmission power control. Controlling the femtocell transmission power is one of the factors that can be considered to alleviate undesirable effect of the interference. Also, it can be used to support auto-configuration conductance of the network. In this work, transmission power auto-configuration approach is proposed. The proposed approach is based on the macrocell transmission power and femtocell coverage. The simulation results depict that the capacity of the network is improved, and the interference is alleviated.

The effects of an Adaptive and Distributed Transmission Power Control on the performance of energy harvesting sensor networks

The design of routing protocols for wireless sensor networks (WSNs) has been traditionally tackled by assuming battery-powered sensors, in which minimizing the power consumption was the main objective. Advances in technology and the ability to harvest energy from the environment has enabled self-sustaining systems and thus diminish the significance of network lifetime considerations in the design of WSNs. Although WSNs operated by energy-harvesting sensors are not limited by network lifetime, they still pose new design challenges due to the unstable and uncertain amount of energy that can be harvested from the environment. In this paper, we propose a new protocol for energy-harvesting sensor networks that uses adaptive transmission power to maintain the network connectivity, and distributes the traffic load on the network. Based on local information, each node dynamically adjusts its transmission power in order to maximize the network’s end-to-end performance. The simulation results indicate that the proposed protocol keeps the network connected at most of the times by using an efficient power management, outperforming greedy forwarding and dynamic duty cycle protocols in terms of packet delivery ratio, delay, and power management.

Performance Characterization of Spatially Random Energy Harvesting Underlay D2D Networks With Transmit Power Control

In underlay device-to-device (D2D) networks, the transmitter nodes can harvest energy from downlink cellular (primary) transmissions to solely power the D2D links, which enhances the overall spectral and energy efficiencies. How will the energy harvest and D2D link performance be affected by spatial randomness, temporal correlations, transmit power control, and channel uncertainties? To investigate these issues, we analyze the energy harvesting process of a random (typical) D2D transmitter node, say $\mathcal D_{t}$ , which needs a sufficient harvest to meet the requirements for receiver sensitivity and channel inversion. This system model consists of: 1) three independent homogeneous Poisson point processes; 2) log-distance path loss and Rayleigh fading; and 3) path loss inversion transmit power control. We derive the ambient radio frequency energy at $\mathcal D_{t}$ , and model the harvest as a Gamma random variable. We propose four schemes: 1) single slot harvesting; 2) multislot harvesting; 3) $\mathcal {N}$ slot harvesting; and 4) hybrid harvesting. We develop a Markov chain model for success probability of these schemes, and derive the D2D coverage. We find that a high density of primary transmitters is unfavorable to multislot harvesting for increased D2D link distances. Moreover, hybrid harvesting always outperforms single and $\mathcal {N}$ slot harvesting, and outperforms multislot harvesting except for very high path-loss conditions.

Transmission power scheduling and control co-design for wireless sensor networks

This paper discusses transmission power scheduling and control co-design in wireless sensor networks (WSNs) under communication energy constraints, in which the dynamics of all mobile sensor nodes (MSNs) are assumed to be nonlinear. A most regular code sequence (MRCS) is introduced as the transmission power scheduling policy to automatically allocate the transmission power levels to MSNs thereby adhering to the given communication energy budget. The effects of the different transmission power levels on data transmission is explicitly characterized in this paper. By using a stochastic system analysis approach and average dwell time technique, a framework for the transmission power scheduling and control co-design is established. The control co-design algorithm is proposed to simultaneously determine the gain of the sampled-data feedback controller and the parameters of the generated MRCS. A numerical simulation is conducted to verify the effectiveness of the proposed algorithm.

Multipath energy balancing for clustered wireless sensor networks

In wireless sensor networks, sensors at different locations in the field use different energy levels to propagate sensing data back to the sink or base station. This causes unbalanced energy usage among sensors and also lowers the network lifetime. Currently there are several techniques to mitigate this problem, such as deploying multiple sinks, adding more sensors on heavy traffic areas, or managing the size of clusters depending on the distance from sensor to sink. In this paper, we propose a distributed algorithm and protocol called Multipath Energy Balancing (MEB) to mitigate unbalanced energy usage in clustered wireless sensor networks using multi-path and multi-hop, with a transmission power control approach. The network field is divided into regions, where the ratio of inter-region transmission traffic from all cluster head sensors in one region to other cluster head sensors in the two regions in front can be pre-computed and pre-programmed into the sensors to ease sensor deployment. To further prolong network lifetime, we also present a simple heuristic algorithm to procrastinate cluster formation and routing. Simulation results show that MEB can balance energy much better than Energy-efficient Clustering (EC) and Balancing Energy Consumption (BEC) solutions. It also has a longer network lifetime than EC and BEC protocols, especially when the required cluster size is small. Procrastinating cluster formation and routing also can further improve the network lifetime.

A Low Traffic Overhead Transmission Power Control for Wireless Body Area Networks

Wireless body area networks are being deployed for an ever increasing number of applications. The main research challenges are still related to the quality of the communication link and to the energy consumption. Since WBAN on-body communication channels’ quality varies over time, it is not appropriate to use a static and pre-defined transmission power level (TPL). Nowadays, the vast majority of WBANs use transmission power control (TPC) mechanisms aiming to minimize energy waste during on-body communications, radio interferences, and the energy specific absorption rate (SAR). These goals are achieved by continuously adjusting and updating the TPL. This paper proposes a novel TPC mechanism that employs a hybrid operation principle (closed-loop control together with posture and motion detection) targeting resource constrained devices. This mechanism uses the on-body communication RSSI values to approximate the fading signal during the user’s gait cycle and, simultaneously, the acceleration signal (from inertial sensors) is used to determine the position within the gait cycle. Experimental results shows that the proposed TPC mechanism is capable to achieve up to 35% energy save per packet transmitted compared with the transmissions performed at maximum TPL. Furthermore, the additional traffic overhead is significantly smaller than those verified in other TPCs addressed in the literature. This research shows that all WBAN application requirements identified by IEEE 802.15.6 TG can be ensured in an energy-efficient way without sacrificing reliability and latency.

Collaborative Link-Aware Protocols for Energy-Efficient and QoS Wireless Body Area Networks Using Integrated Sensors

Integrated sensor systems are the promising solutions to the multiprobe design of biomedical sensors for energy efficient wireless body area networks (WBANs). To improve energy efficiency and communication quality-ofservice (QoS) of integrated sensor hubs, a cross-layered energyaware resource allocation (CLEAR) is proposed to exploit the characteristics of medium access control (MAC) and physical (PHY) layers of WBANs. The proposed MAC protocol applies a twin-token bucket model and real-time scheduling to the active superframe interleaving and beacon shifting techniques defined in the IEEE 802.15.6 standard. In addition, the proposed period transformation controlling task end-to-end delay improves the QoS while reducing packet collisions. The proposed PHY protocol is a hybrid of the transmit power control (TPC) and link adaption (LA) strategies, which coordinates the receiver and transmitter with link parameters including data rate, bit error ratio, receiver sensitivity, transmission power, modulation, etc. It adapts the link quality due to human postures or movements to meet the required QoS while minimizing the energy consumption of the transceiver front-end. Experimental results show that CLEAR outperforms existing methods in terms of packet delivery ratio, delay, bandwidth utilization, and energy consumption. In particular, CLEAR reduces average end-to-end delay by 21% as compared to existing methods, and saves an average of 18% of energy consumption over the TPC and LA-based methods at a power of -25 dBm while achieving a maximum 0.05 packet error ratio.

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles.

Self-Learning Power Control in Wireless Sensor Networks.

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

Transmission power control of wireless sensor networks based on optimal connectivity

To develop a short-term traffic load prediction model for satellite networks, a prediction algorithm based on spatiotemporal correlation and least square support vector machine (STLS-SVM) is presented. The prediction model fully exploits the regularity and periodicity of satellite constellations and uses the lag correlation coefficients to determine which satellite pairs have the highest spatiotemporal correlation. Then, the traffic time sequences of the most highly correlated satellites are taken as input feature vectors for training the LS-SVM for short-term traffic prediction. A simulation test shows that the algorithm has higher network flow prediction accuracy and that using the spatiotemporal correlation improves the predictive performance.

Multi-virtual wireless mesh networks through multiple channels and interfaces

The high flexibility of the wireless mesh networks (WMNs) physical infrastructure can be exploited to provide communication with different technologies and support for a variety of different services and scenarios. Context information may trigger the need to build different logical networks on top of physical networks, where users can be grouped according to similarity of their context, and can be assigned to the logical networks matching their context. When building logical networks, network virtualization can be a very useful technique allowing a flexible utilization of a physical network infrastructure. Moreover, dynamic resource management using multiple channels and interfaces, directional antennas and power control, is able to provide a higher degree of flexibility in terms of resource allocation among the available virtual networks, to enable isolated and non-interfering communications while maximizing the network efficiency. In this paper we propose a resource management approach that uses transmit power control algorithm with both omnidirectional and directional antennas, to determine the resources of each virtual network while minimizing interference between virtual networks, considering the support of multiple services and users. Each virtual network can be extended to include the nodes of the WMN required by new users. The results of the proposed approach show that the support of multiple virtual networks for multiple services highly improves the network performance when compared to the support of the services in only one virtual network, with no interference minimization nor dynamic resource control.