Transmission Power Consumption Research Articles

Massive Uncoordinated Access With Massive MIMO: A Dictionary Learning Approach

Massive machine-type communications (mMTC) or massive Internet of things (IoT) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Since initial access procedures of legacy systems are not suitable for massive connectivity due to increased collision probability and prohibitive overhead, it is of great interest to design an efficient grant-free access protocol. In this paper, we propose an uncoordinated access protocol for massive connectivity, which leverages a massive number of antennas at the base station (BS), which is expected to be widely deployed in cellular networks. With the proposed scheme consisting of a sparse frame structure and receiver processing based on sparse dictionary learning , a massive number of IoT devices can transmit data without any prior scheduling process. Unlike existing schemes that necessitate significant overhead for preamble signals, the overhead required for the proposed scheme is negligible, which is attractive in terms of resource utilization and transmit power consumption. Numerical results verify that the proposed scheme can be utilized to facilitate massive connectivity in realistic cellular-based IoT scenarios.

Optimal power allocation for M-ary distributed detection in the presence of channel uncertainty

We consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with detecting M known signals in Gaussian noise. Sensors make M-ary decisions and transmit their digitally modulated decisions over orthogonal fading channels to the FC. Considering two coherent and non-coherent systems, we study how to distribute the total transmit power among the sensors such that J-divergence at the FC is maximized, subject to cumulative and individual transmit power constraints. To fairly compare the two systems, we consider the cost of channel estimation for coherent systems via introducing trade off between pilot and data transmission. We develop power allocation schemes and provide the optimal fusion rule for both systems. Our numerical results show that, the coherent system does not necessarily outperform the non-coherent system, when the cost of channel estimation (required transmit power for pilot) is considered. In particular, when the total transmit power is low (and the effect of channel estimation is more severe) the non-coherent system outperforms the coherent one. The proposed power allocation schemes for the coherent system allow us to reduce the total transmit power consumption up to 80%, compared with uniform power allocation.

Matching Game-Based Cell Association in Multi-RAT HetNet Considering Device Requirements

Multiple radio access technologies heterogeneous network (multi-RAT HetNet) composed by cellular and WiFi cell base stations (BSs) is considered a promising paradigm for meeting the high traffic demand on mobile networks. In this article, a new cell association algorithm for different wireless devices (WDs) having diverse requirements in multi-RAT HetNet is proposed. The algorithm is designed to consider the WDs’ uplink power consumptions and downlink achieved data rate while taking into account the proportional fairness among them. For this, the WDs association process is formulated as an optimization problem to maximize the WDs downlink achieved data rates considering proportional fairness, and to minimize their uplink transmitted power consumption. To solve this problem, a matching game-based association algorithm is proposed, where WDs are matched to their best cell BS according to well-designed utility functions that consider the rate and power requirements of each WD. Simulation results and performance analysis show that our proposed association strategy outperforms comparable strategies in terms of system throughput, WDs transmit power consumption, and fairness with significant gains.

Quality of Service Provisioning for D2D Users in Heterogeneous Networks

This paper investigates power allocation for D2D communications in a Heterogeneous Network (HetNet) which includes a macro cell and pico cells, whereas femto cells are distributed inside. The objective is to minimize the total power consumption of D2D transmitter while satisfying a set of Quality of Service in D2D receivers and cellular users communication interference. To this end, we first obtain expression for the formula of signal to interference plus noise ratio (SINR) and spectral efficiency of each D2D receiver. Then, an optimization problem is formulated to minimize the total transmit power of D2D transmitters subject to QoS constraints. It is shown that this optimization problem is linear programming and it can be solved in polynomial time with standard tools. In addition, the maximization value of D2D pair transmission rates are determined with fixed cellular users parameters.

Bidirectional Prediction-Based Underwater Data Collection Protocol for End-Edge-Cloud Orchestrated System

The proliferation of advanced underwater technology and the emergence of various cloud services promote the horizon of cloud-based underwater acoustic sensor network (UASN). Sending end data to cloud for analysis is becoming a prominent trend, driving cloud computing as an indispensable computing paradigm. However, UASN bears tremendous burdens with respect to data collection from end to cloud, such as large transmission power consumption and high delay, which makes it difficult to meet the delay-sensitive and context-aware service requirements by using cloud computing alone. To this end, a two-level bidirectional data prediction model for end-edge-cloud orchestration is proposed in this article. The mobility and computing ability of edge elements are exploited to analyze and collect data. Edge elements predict the future data based on historical information and trend to decrease acoustic communication. Moreover, a data collection protocol with mobile edge elements is designed. With this protocol, computing paradigms are shifted from centralized cloud to distributed edge, and the differentiated capability of heterogeneous devices is exploited. After extensive experiments, the results show that the data collection cost is dramatically decreased while the bandwidth utilization is increased, which is critical for underwater acoustic communication. The proposed method and protocol strike a good balance between data accuracy and energy consumption for the new end-edge-cloud orchestrated system.

Demand aware transmission power cost optimization based on game theory and distributed learning algorithm for wireless body area network

This paper studied the issue of communication transmission power cost optimization for the personal body base station in wireless body area network (WBAN). With the limited energy capacity, the energy cost for the data exchange between the personal body base station and the medical surveillance network should be controlled. At the same time, the users’ demand should also be guaranteed. The transmission AI choosing distributed optimization model is established, by taking the transmission power consumption as the optimization goal. In this model, the user’s data transmission requirements, wireless environment, location of the AI, and other users ‘choices are comprehensively analyzed. To achieve the optimal users’ AI choosing result for the transmission power cost optimization, the AI choosing game model for transmission power cost of WBAN is constructed, and the game is proved to be an accurate potential game. A transmission power cost optimization AI choosing distributed decision-making algorithm is designed, and the convergence of the algorithm is proved. Experiment analysis verifies the theoretical analysis of the proposed game model and learning algorithm, and show that the proposed algorithm can effectively optimize the AI choosing results of the WBAN to reduce the energy cost.

A Novel CML Latch-Based Wave-Pipelined Asynchronous SerDes Transceiver for Low-Power Application

In the present technology development billions of transistors are fabricated on a single chip, which improves the performance of circuits in terms of high data transmission speed and power consumption. This requirement of data transmission speed is achieved with the help of high-speed transceivers. In this paper, we present a high-speed asynchronous wave-pipelined serializer and deserializer (SerDes) transceiver implemented using current-mode logic (CML). This asynchronous transceiver circuit does not require a clock and therefore it saves large amount of power which is consumed in the phase locked loop (PLL) and frequency synthesizer circuits. Further, the proposed design is built using CML which saves more power. CML circuit operates at relatively higher speed as compared to CMOS circuits which helps the circuit to operate at higher data rate. Compared to conventional CML latch, a novel CML latch is proposed in our design to increase the speed. The circuit is implemented in standard CMOS 65-nm technology. The total power consumed by the serializer and deserializer is 9.32[Formula: see text]mW, which is very less as compared to published related works. The proposed asynchronous SerDes transceiver operates at 18.1-Gbps data transmission rate with low power dissipation.

Numerical Simulation of the Distribution of the Low-Frequency Field Created by a Transmitting Loop Antenna Installed on Board a Spacecraft

Based on the results of numerical calculations of the intensity of the low-frequency (LF) electric field of an emitter in the form of a loop antenna installed on board an artificial Earth satellite, this quantity was compared with the strength of the field demodulated in the lower ionosphere, perturbed by the radiation of a high-power high-frequency (HF) transmitted HAARP station, Alaska). It is shown that the onboard low-frequency transmitter with a power of ~ 1 kW is able in the lower ionosphere to provide approximately the same field strength as the HAARP demodulator with an average power consumption of the HF transmitter of 3.6 MW.

Dynamic Carrier to MCPA Allocation for Energy Efficient Communication: Convex Relaxation Versus Deep Learning

The rapid increasing demand of wireless transmission has caused mobile broadband to continuously evolve through multiple frequency bands, massive antennas, and other multi-stream processing schemes. Together with the improved data transmission rate, the power consumption for multi-carrier transmission and processing is proportionally increasing, which contradicts with the energy efficiency requirements of 5G wireless systems. To meet this challenge, multi-carrier power amplifier (MCPA) technology, e.g., to support multiple carriers through a single power amplifier, is widely deployed in practice. With so many carriers required for 5G communication and limited number of carriers supported per MCPA, a key question to ask is how to allocate those carriers into multiple MCPAs and whether we shall dynamically adjust this allocation strategy. In this paper, we have theoretically formulated the dynamic carrier to MCPA allocation problem to jointly optimize the traditional separated baseband and radio frequency processing. On top of that, we have proposed both convex relaxation and deep learning-based algorithms. From our simulation results, the proposed algorithms achieve most of the power saving gain compared with the optimal exhaustive search-based algorithm. Furthermore, the deep learning-based approach can greatly reduce computational time, which is of vital importance in the practical deployment.

Multichannel Uplink NOMA Random Access: Selection Diversity and Bistability

This letter investigates uplink random access (RA) systems based on power-domain non-orthogonal multiple access (NOMA) with multichannel. In NOMA RA systems, users transmit their packet with transmit power by inverting their channel gain, i.e., channel inversion, such that the received power of their packet at the base station (BS) can be one of the predefined values. While this facilitates the decoding process based on successive interference cancellation (SIC), it can incur excessive transmit power consumption upon low channel gains. To reduce transmit power (improve energy efficiency), this letter considers multichannel selection diversity, where users choose one channel whose channel gain is the maximum of all. We characterize the stability of multichannel NOMA RA systems with the cusp catastrophe and analyze diversity gain in terms of energy efficiency.

RSS Based Selective Clustering Technique to Improve Power Utilization in WSN

Wireless sensor networks (WSNs) have numerous advantages like easy deployment, scalability, no need of infrastructure etc. Apart these features the main drawback of WSN is limited power capacity of the sensor nodes. Hence nodes are unable to perform communication for long time duration as their limited battery power. Receive signal strength (RSS) is a prime attribute within wireless communication because lower RSS value leads to more power consumption for data transmission. This paper introduces RSS based approach to improve the power utilization of sensor nodes. A Node near to base station transmits data directly without clustering process. A weight parameter calculated for cluster head selection using RSS value of a node and geographic location of the nodes. The propose method works on heterogeneous network like SEP technique. Different power level assign to the sensor node as per distance from base station. The first group of nodes is normal nodes they transmit data directly to the base station. The proposed method balances the energy of all nodes and helps to improve total lifetime of sensor networks. The proposed method performs well compare with existing methods to improve network lifetime and total number of packet transmission.

A Comparative Study of Computational Methods for Compressed Sensing Reconstruction of EMG Signal.

Wearable devices offer a convenient means to monitor biosignals in real time at relatively low cost, and provide continuous monitoring without causing any discomfort. Among signals that contain critical information about human body status, electromyography (EMG) signal is particular useful in monitoring muscle functionality and activity during sport, fitness, or daily life. In particular surface electromyography (sEMG) has proven to be a suitable technique in several health monitoring applications, thanks to its non-invasiveness and ease to use. However, recording EMG signals from multiple channels yields a large amount of data that increases the power consumption of wireless transmission thus reducing the sensor lifetime. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples needed to reconstruct the signal. As a large variety of algorithms have been developed in recent years with this technique, it is of paramount importance to assess their performance in order to meet the stringent energy constraints imposed in the design of low-power wireless body area networks (WBANs) for sEMG monitoring. The aim of this paper is to present a comprehensive comparative study of computational methods for CS reconstruction of EMG signals, giving some useful guidelines in the design of efficient low-power WBANs. For this purpose, four of the most common reconstruction algorithms used in practical applications have been deeply analyzed and compared both in terms of accuracy and speed, and the sparseness of the signal has been estimated in three different bases. A wide range of experiments are performed on real-world EMG biosignals coming from two different datasets, giving rise to two different independent case studies.

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption, and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement that the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL) based approach is employed for the second online power control policy. Via simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared to the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming based online grouped water-filling (GWF) strategy unless the channel is highly correlated.

Uplink Low-Power Scheduling for Delay-Bounded Industrial Wireless Networks Based on Imperfect Power-Domain NOMA

The power-domain non-orthogonal multiple access (NOMA) supports multiple packet reception, which can be leveraged for delay-bounded applications in industrial wireless networks (IWNs). However, it suffers from high power consumption on transmitters, which poses challenges for battery-powered wireless sensors. Given the delay bound for NOMA-based IWNs, the problem of minimizing aggregate power consumption of transmitters is therefore of great value. In a previous paper, we have addressed the problem under the model of perfect $k$ -successive interference cancellation ( $k$ -SIC). In this paper, we study the same problem, however, under the model of imperfect $k$ -SIC, which is more general in theory and more realistic in practice. For the existence of the optimal solution, we first present an explicit sufficient and necessary condition, which correlates three key parameters of network system together. We also propose a polynomial-time optimal algorithm with complexity $O(n^2)$ . We further consider the same problem with discrete transmit powers, and present an approximation algorithm with complexity $O(n^2)$ . Performance evaluation reveals that the delay bound requirement has tremendous impacts on both the aggregate power consumption and the maximum transmit power. Relative to the perfect SIC, the residual error caused by imperfect SIC results in extra power consumption of transmitters. However, the extra power consumption is gradually diminished with the further relaxation of the delay bound.

A distributed Newton algorithm for network utility maximization in wireless ad hoc networks

SummaryThe existing distributed Newton algorithm for network utility maximization cannot be directly applied into the wireless ad hoc networks, as it does not consider the wireless link capacity variation and transmission power consumption. Regarding these, a new joint network utility maximization problem is formulated to optimize the wireless ad hoc network resource utility. The distributed Newton algorithm is implemented to solve for the dual variables and Newton directions by using the local information at each session source and link, respectively. Furthermore, a new iterative method is proposed to improve the convergence rate of the existing matrix‐splitting method. Simulation results validate the efficiency and efficacy of the proposed distributed Newton algorithm for wireless ad hoc network utility maximization.

A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks.

Wireless sensor networks are widely used in many fields. Nodes in the network are typically powered by batteries. Because the energy consumption of wireless communication is related to the transmission distance, the energy consumption of nodes in different locations is different, resulting in uneven energy distribution of nodes. In some special applications, all nodes are required to work at the same time, and the uneven energy distribution makes the effective working time of the system subject to the node with the largest energy consumption. The commonly used clustering protocol can play a role in balancing energy consumption, but it does not achieve optimal energy consumption. This paper proposes to use the power supply line to connect the nodes to fully balance the energy. The connection scheme with the shortest power line length is also proposed. On the basis of energy balance, the method of transmitting data with the best hop count is proposed, which fully reduces the power consumption of the data transmission. The simulation results show that the proposed method can effectively reduce the energy consumption and prolong the lifetime of wireless sensor networks.

Power Efficient Visible Light Communication With Unmanned Aerial Vehicles

A novel approach that combines visible light communication (VLC) with unmanned aerial vehicles (UAVs) to simultaneously provide flexible communication and illumination is proposed. To minimize the power consumption, the locations of UAVs and the cell associations are optimized under illumination and communication constraints. An efficient sub-optimal solution that divides the original problem into two sub-problems is proposed. The first sub-problem is modeled as a classical smallest enclosing disk problem to obtain the optimal locations of UAVs, given the cell association. Then, assuming fixed UAV locations, the second sub-problem is modeled as a min-size clustering problem to obtain the optimized cell association. In addition, the obtained UAV locations and cell associations are iteratively optimized multiple times to reduce the power consumption. The numerical results show that the proposed approach can reduce the total transmit power consumption by at least 53.8% compared with two baseline algorithms with fixed UAV locations.

UAV-IoT for Next Generation Virtual Reality

We investigate UAV-IoT data capture and networking for remote scene virtual reality (VR) immersion. We characterize the delivered immersion fidelity as a function of the assigned UAV-IoT capture/network rates and study the optimization problem of maximizing it, for given system/application constraints. We explore fast reinforcement learning to discover the best dynamic UAV-IoT network placement over the scene of interest to maximize the expected remote immersion fidelity. We design scalable source-channel viewpoint coding to maximize the expected reconstruction fidelity of the data captured at every UAV location at the ground-based aggregation point. Finally, we explore layered directional networking and rate-distortion-power optimized embedded scheduling methods to effectively transmit the encoded data and overcome network transients that lead to packet buffering, which represent the fourth system component of our framework. Experimental results demonstrate considerable performance efficiency gains enabled by each system component over the respective state-of-the-art reference methods, in delivered VR immersion fidelity, application interactivity/play-out latency, and transmission power consumption.

Robust Beamforming for Multibeam Satellite Communication in the Face of Phase Perturbations

A precoding scheme is proposed for the downlink of multibeam satellite communication in the face of phase perturbations. In order to alleviate the signal-to-interference and noise ratio degradation induced by phase uncertainty and to minimize the transmission power consumption, we formulated the beamforming problem as a chance-constrained optimization. Moreover, we provided a large deviation inequality aided conservative approximation for the chance-constraints, followed by conceiving a tractable solution. Our simulation results showed the accuracy and the efficiency of our proposed approximation algorithm compared to the benchmarks.

Low-Energy Data Collection in Wireless Sensor Networks Based on Matrix Completion.

Sparse sensing schemes based on matrix completion for data collection have been proposed to reduce the power consumption of data-sensing and transmission in wireless sensor networks (WSNs). While extensive efforts have been made to improve the recovery accuracy from the sparse samples, it is usually at the cost of running time. Moreover, most data-collection methods are difficult to implement with low sampling ratio because of the communication limit. In this paper, we design a novel data-collection method including a Rotating Random Sparse Sampling method and a Fast Singular Value Thresholding algorithm. With the proposed method, nodes are in the sleep mode most of the time, and the sampling ratio varies over time slots during the sampling process. From the samples, a corresponding algorithm with Nesterov technique is given to recover the original data accurately and fast. With two real-world data sets in WSNs, simulations verify that our scheme outperforms other schemes in terms of energy consumption, reconstruction accuracy, and rate. Moreover, the proposed sampling method enhances the recovery algorithm and prolongs the lifetime of WSNs.