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The general use of wireless sensor devices and their importance in terms of deployment cost, size and user-friendliness have given an increase to many different applications of cognitive radio networks. In cognitive radio networks, energy efficiency is a very important problem. In the literature, many different methods are developed by researchers which are not able to produce the best results. Hence, in this article, a hybrid technique is developed to attain energy efficiency deliberation intended for properly maintaining objective signal to interference plus noise ratio (SINR) need for the received interferences and cognitive users at a receiver of primary under a reference value. In the proposed methodology, interference threshold protection margin and protection factor to the SINR target are developed. The hybrid WOFA algorithm can be a combination of the Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA). In the WOA algorithm, updating positions of the whales are achieved with the utilisation of the FA algorithm. The proposed method is designed in the MATLAB platform, and it is compared with the existing approaches of WOA by robustness in addition to energy efficiency under the disturbance of channel gain. The proposed method is analysed with the two different cases such as the target SINR ratio condition of 3.52 dB and the target SINR ratio condition of 5.11 dB. [ABSTRACT FROM AUTHOR]

One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) attains a promising result for the several access problems and fulfils the requirements of fifth-generation (5G) networks by strengthening the service quality, like massive connectivity and energy efficiency. To achieve the power allocation of multiple users with the layered transmission, the NOMA is extended with the multiple input multiple output (MIMO) system. In this work, the allocation of power in MIMO–NOMA is optimally done with the layered transmission by the developed Fractional Salp Particle Swarm Optimisation (FSPSO) algorithm. In the MIMO–NOMA, the implemented FSPSO algorithm attained the better sum rate using allocating the powers at multiple layers of users. Also, the closed-form phrase is formed for the achievable sum rate using the Channel State Information (CSI) existing at the side of transmitter. To increase the achievable sum rate, the CSI permits the users to assign the powers at various layers. The proposed algorithm optimally allocates the power with the lower Bit Error Rate (BER) of 0.00039 and better energy efficiency, spectral power, and achievable sum rate of 20.8134 J, 181.660 dB, and 110.615 dB, respectively. [ABSTRACT FROM AUTHOR]

WSN consists of many sensors, they need high throughput and network lifetime to get involved in communication. Duty Cycle is a key factor in sensor nodes to maintain sleep, active condition in the presence of overhearing, collisions of packets while data transmission between sensor nodes. The key challenge of sensor nodes is to attain high energy efficiency, throughput and low End-End Delay. This paper presents several of existing approaches for MAC protocol. They are (1) Synchronization between nodes (2) Mathematical Analysis (3) TDMA based MAC and (4) Cross-Layer Design. We proposed a new method for MAC protocol with variable duty cycle. Neighboring nodes follow same duty cycle and same active time. Far away nodes doesn't maintain same duty cycle, hence their active time is not same. The duty cycle is maintained in synchronous way and adaptive manner among the nodes within the network. In this paper, a conventional MAC and proposed MAC are simulated using NS-2.35. Variable duty cycle approach in the proposed MAC with sleep and idle states for the nodes yields better throughput and energy efficiency with respect to other conventional technique. These results validate that the proposed variable DC-MAC is feasible for Wireless Sensor Network applications. [ABSTRACT FROM AUTHOR]

5G network plays a vital role in each field. Recently, the number of 5G users are increasing due to their vast merits. But, these users require various resources to operate effectively. Recently, the machine learning approaches are introduced within this 5G network field for attaining better accuracy and reliability during resource allocation. In this work, the resource allocation for multi-users in 5G massive-MIMO (mMIMO) is performed by a deep neural network (DNN). Initially, the objective functions are optimised by Multi-objective Sine Cosine algorithm (MOSCA). The objective functions applied in this optimisation method are data rate, signal-interference noise ratio (SINR), power consumption, and energy efficiency (EE). Next, these optimised objective functions are allocated to the neural network for resource allocation. DNN identifies the requirement level of each user. Based on this level, it allocates the resource to each user by maintaining high throughput and EE. Further, the fairness index for this neural network-based resource allocation process is also identified. The throughput and fairness index of the proposed method for 30 users are 275 bps, and 0.92%, respectively. The outcomes of this proposed method show that this method provides better performance in 5G mMIMO than other existing methods. [ABSTRACT FROM AUTHOR]

Carrier Aggregation (CA) technology, introduced in 3GPP release 10, aims at increasing Spectral Efficiency (SE) only, which may lead to huge undesirable circuit power consumption. Hence, not only SE, but also Energy Efficiency (EE), particularly the tradeoff between both the parameters, is crucial in the design of CA systems. In this work, a novel approach, using Genetic Algorithm (GA) is proposed to solve the SE-EE tradeoff problem for a 5 G heterogeneous network with intra-band contiguous Carrier Aggregation (CA). Complete physical layer simulation is performed for generating the CA waveform, with parameters set in accordance with practical deployment scenarios, as specified in 3GPP TS 38.104. Carrier aggregated waveform is generated in 5 G New Radio (NR) frequency band FR1, using frequency bands in India which are currently not allocated for licenced usage. From simulations, it is observed that the proposed technique ensures that neither SE nor EE is seriously degraded for improving the other, for any given value of transmit power. The proposed technique, which performs Multi Objective Optimisation (MOO) using SPEA-2 GA outperforms Single Objective Optimisation (SOO) methods providing a maximum of 37% improvement in SE and 43% improvement in EE. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient bandwidth allocation strategy for multiclass services in hierarchical cellular networks that consist of an operation controller, several small-cell base stations (SBSs), and a number of mobile users. Each SBS is equipped with a finite-capacity battery that is regularly recharged by a solar harvester. We aims to find the optimal bandwidth allocation policy in order to enhance the network performance in terms of user satisfaction and energy efficiency under energy harvesting and bandwidth sharing constraints. Since the arrivals of harvested energy and traffic requests are unknown due to users' mobility and stochastic request generation, it is necessary to design a learning framework for the controller in order to predict these dynamics through interaction with the environment. For this purpose, we first formulate the resource allocation problem as the framework of a Markov decision process, and then, we employ an actor-critic algorithm to find the optimal policy under which the controller can effectively allocate the limited bandwidth to the SBSs for their data transmissions. We evaluate the performance of the proposed scheme through comprehensive simulations with different settings, and show that the proposed bandwidth allocation scheme can enhance the network's performance in the long run. [ABSTRACT FROM AUTHOR]

To reduce the energy consumption and prolong the battery lifetimes of mobile terminals, the design of energy-efficient association scheme is necessary and meaningful. To this end, we design a type of association mechanism (scheme) to maximize the whole energy efficiency (WEE) for uplink code-division-multiple-access (CDMA)-based heterogeneous cellular networks (HCNs), and formulate it as a WEE maximization problem with users' SINR (signal-to-interference-plus-noise ratio) constraints. Considering that the finally formulated problem is in a fractional mixed-integer form and hard to tackle, we introduce an energy efficiency (EE) parameter to convert it into a parametric subtractive form. At last, we design two two-layer iterative algorithms for the formulated problem, whose primary difference lies in the treatment of SINR constraints. In general, the outer loop utilizes a bisection method to search the optimal EE parameter, and the inner loop alternately optimizes the association index and transmit power. At last, we give the corresponding convergence analyses for some subalgorithms, and also give some detailed complexity analyses for the whole algorithm. In the simulation, we introduce an existing energy-efficient association mechanism for comparison, and finally investigate the impacts of different network parameters on the association performance. [ABSTRACT FROM AUTHOR]

The energy efficiency (EE) of distributed multiple-input single-output (MISO) system is investigated in spatially-correlated Rayleigh channels, where large-scale fading including the path loss and the shadow fading is considered. The target is to maximize the EE which is defined as the ratio of the transmission rate to the total power consumed by the system subject to the maximum transmit power of each remote antenna unit (RAU) constraint. The EE optimization can be formulated as a nonlinear fractional programming problem. An optimal power allocation (PA) scheme for maximizing the EE is proposed. With this scheme, an iterative algorithm using Dinkelbach method is presented. To avoid iterative calculation, a low-complexity simplified PA scheme is developed based on the good channel information for antenna selection; it can provide a computational efficient method to acquire the optimal numbers of active RAUs. Therefore, the multidimensional optimization problem is converted to a single-dimensional optimization problem. As a result, the closed-form PA is attained. The simulation results validate the effectiveness of the proposed two schemes. The results indicate that these two schemes can achieve very similar performance, and the simplified scheme has lower complexity because no iteration is required. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner. [ABSTRACT FROM PUBLISHER]

Minimising energy dissipation and maximising network coverage are the crucial aspects of wireless sensor networks (WSNs). The nodes of a sensor network are grouped into several non-overlapping subset to facilitate the network scalability and design of energy efficient system. Selecting optimal clusters provide significant gain in terms of network lifetime, system scalability, collision avoidance and end-to-end delay. Network coverage is another critical issue which depends on sensing model like deterministic sensing model and probabilistic sensing model. In this paper, we have proposed Tunable Elfes Sensing Model. This model is preferable in case of remote area application in WSNs, where nodes in the core of network closer to base station (BS) are modelled deterministically, whereas peripheral nodes are modelled probabilistically. Thus, we can model energy efficient system with large coverage area. In this paper, we have provided expression for optimal cluster using Tunable Elfes sensing model of BS for square and circular sensing field using single-hop communication between cluster heads (CHs) and BS with consideration of boundary effect. We have also analysed the optimal clusters for square and circular type of sensing field using Tunable sensing model for BS multi-hop communication between CHs and BS. [ABSTRACT FROM AUTHOR]

In this article, a new energy efficient level shifter circuit is described for robust sub-threshold to above-threshold voltage conversion. The design is based on a modification of the Wilson current mirror that minimizes the static current and improves the energy efficiency. The proposed level shifter is capable of converting a 200 mV signal into a 1 V signal and operates correctly across process corners. At the design target (200 mV, input signal frequency = 1 MHz), the proposed level shifter exhibits a propagation delay of 20.17 ns, a static power dissipation of 11.07 nW, and the total energy per transition of 113.2 fJ. We compare our results with the design proposed by Lutkemeier and Ruckert (2010). At the design target, the energy delay product of our design is 81.53% that of the reference design and, over all the voltage range offrom 160 mV to 1 V, the proposed circuit shows an average energy reduction of 14.71% compared to the reference design. Thorough variability analysis was performed using HSPICE Monte Carlo simulations for different values of, which reveals the robustness of the proposed design. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a systematic design methodology in the category of hybrid-CMOS logic style. A huge library of circuits appropriate for low-power and high-speed applications can be obtained by employing the proposed design methodology. The methodology is before used for designing XOR/XNOR and demonstrates the excellence of the new design features. The question of whether the method can be taken advantage to design the function of Carry and its complement (Carry and InverseCarry), as the third important module of a full adder, and what to extend the answer contributes to move towards the general systematic design. All the presented designs as before have high driving capability, balanced full-swing outputs with less glitches and small number of transistors. Also these only consist of one pass-transistor in the critical path, which causes low propagation delay and high drivability. As known, hybrid-CMOS full adders can be divided into three modules, e.g., SUM, Carry and XOR. Optimising these modules has reduced power consumption, delay and the number of transistors of full adders. Therefore by embedding the balanced full-swing circuits in carry module, it can be expected that 11 new full adder circuits will possess high performance. Simulation results show that the proposed circuits exhibit better performances compared to previously suggested circuits in the proposed realistic test bench. These circuits, outperform their counterparts, are showing 24–126% improvement in the power-delay product (PDP) and 57–82% improvement in the area. All simulations have been performed with TSMC 0.13-μm technology in new full adder test bench, using HSPISE to achieve the minimum PDP. [ABSTRACT FROM AUTHOR]

Three-dimensional integrated circuits (3D ICs) present an intriguing challenge for both circuit and system engineers due to their diverse cooling efficiency among the stacked dies. Several recent proposals advocate multiple techniques for thermal management of 3D ICs at different levels of the design, while operating within the confines of thermal heterogeneity. In this article, we analyse for the first time, the role of thermal heterogeneity on the energy efficiency of the system by incorporating temperature dependent leakage power. We develop a novelconvexoptimisation framework to optimise the energy efficiency in 3D ICs incorporating: (a) leakage aware thermal provisioning using temperature dependent full-chip leakage model, (b) heat flow in vertically stacked systems using a grid based compact thermal model and (c) a concrete application for workload provisioning in 3D multicore systems. Detailed simulation-based experiments with our proposed optimisation framework shows 5–17% improvement in the energy efficiency of a typical multicore system organised as 3D stacked dies. [ABSTRACT FROM AUTHOR]