Energy Efficiency In Wireless Networks Research Articles

Assessment of energy-efficient wireless network using autoencoders with unsupervised deep learning

Assessment of energy-efficient wireless network using autoencoders with unsupervised deep learning

Energy Efficiency in Wireless Networks

The pervasive integration of wireless devices across diverse sectors has experienced an unprecedented surge in recent years [...]

Enhancing the Wireless Network's Energy Efficiency to Reduce Security Challenges in 5G Systems: A Review

The desire for faster data speeds and increased Energy Efficiency has prompted the development of femtocells, which are short-range, low-cost, customer cellular access points. However, in a situation of Distributed Denial of Service (DDoS) which is caused by inefficient energy, distributed attack sources could be employed to amplify the assault and increase the attack's impact. By flooding the network with packets and creating malicious traffic, Distributed Denial of Service (DDoS) attacks try to deplete the network's communication and processing capability. A DDoS assault must be identified and neutralized quickly before a valid user can reach the attacker's target for 5G network to have an effective Energy Efficient service. For the next Fifth Generation (5G) Wireless Network, there is a pressing need to build an effective Energy Efficient mobile network solution. Despite their evident promise in assisting the development and deployment of the complicated 5G environment. The physical product, the digital product, and the relationship between both the physical and virtual goods are said to make up Digital Twin (DT). On the other hand, DT allows real-time communication with both the physical twins. The synergy of energy efficiency and security improvements in this research contributes to a more holistic optimization of 5G networks. This approach seeks to minimize energy consumption while fortifying the network against evolving security threats. Integrating energy-efficient practices with robust security measures enhances the overall resilience and sustainability of 5G systems. This is crucial for ensuring continuous, reliable, and secure communication in the face of dynamic challenges.

Physical layer security analysis of IRS-based downlink and uplink NOMA networks

In recent years, the development of intelligent reflecting surface (IRS) in wireless communications has enabled control of radio waves to reduce the detrimental impacts of natural wireless propagation. These can achieve significant spectrum and energy efficiency in wireless networks. Non-orthogonal multiple access (NOMA) technology, on the other hand, is predicted to improve the spectrum efficiency of fifth-generation and later wireless networks. Motivated by this reality, we consider the IRS-based NOMA network in the downlink and uplink scenario with a pernicious eavesdropper. Moreover, we investigated the physical layer security (PLS) of the proposed system by invoking the connection outage probability (COP), secrecy outage probability (SOP), and average secrecy rate (ASR) with analytical derivations. The simulation results reveal that (i) it is carried out to validate the analytical formulas, (ii) the number of meta-surfaces in IRS, transmit power at the base station, and power allocation parameters all play an essential role in improving the system performance, and (iii) it demonstrates the superiority of NOMA to the traditional orthogonal multiple access (OMA).

Data-Driven Spectrum Allocation and Power Control for NOMA HetNets

Towards a green communication discipline, more energy-efficient wireless networks are needed. In this paper, an energy-efficient interference management technique is proposed for heterogeneous networks (HetNets) with downlink nonorthogonal multiple access (NOMA) operating in millimeter (mm) waves. The proposed technique utilizes data-driven-based power and resource blocks (RBs) allocation scheme to maximize the energy efficiency of the entire network while managing inter-user, co-tier, and cross-tier interference. The studied interference management problem is mathematically formulated as maximizing the energy efficiency of NOMA-HetNets under constraints; quality of service (QoS), successive interference cancelation condition (SIC), maximum power allocation, and the maximum capacity of each RB, which is a non-convex and combinatorial NP-hard problem that needs exhaustive search for an optimum solution. The formulated problem is relaxed into a minimization of a linear <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm problem. Then, a lowcomplex model-free data-driven based approach is proposed to find a near-optimum allocation for power and RBs based on a dynamic linearization representation of a time-varying pseudogradient parameter estimation procedure. Numerical analyses show that the proposed scheme achieves higher network energy efficiency with lower computational complexity compared to the benchmark schemes.

Cross layer protocol architecture for spectrum‐based routing in cognitive radio networks

AbstractNew cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use.

Optimal Deployment Strategy for Reconfigurable Intelligent Surface under LoSD via Joint Active and Passive Beamforming.

A reconfigurable intelligent surface (RIS) is a new and revolutionizing technology to achieve spectrum-efficient (SE) and energy-efficient (EE) wireless networks. In this paper, we study an optimal deployment strategy of RIS in a line-of-sight domain (LoSD) based on an actual deployment scenario, which jointly considers path loss, transmit power and the energy efficiency of the system. Furthermore, we aim to minimize the transmit power via jointly optimizing its transmit beamforming and the reflect phase shifts of RIS, subject to the quality-of-service (QoS) constraint, namely, the signal-to-noise ratio (SNR) constraint at the user. However, this optimization problem is non-convex with intricately coupled variables. To tackle this challenge, we first apply proper transformation on the QoS constraint and then propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that compared to a conventional endpoint deployment strategy that simply deploys RIS at the transceiver ends, our proposed LoSD deployment strategy significantly reduces the transmit power by optimizing the available LoS links when a single RIS is relayed. The impact of the number of reflect elements on the system EE is also unveiled.

From Liquid Crystal on Silicon and Liquid Crystal Reflectarray to Reconfigurable Intelligent Surfaces for Post-5G Networks

This communication aims to address the recent surge of interest in reconfigurable intelligent surfaces (RISs) among both academic and industrial communities, which has largely neglected the historical developments of two other underpinning technologies, i.e., liquid crystal on silicon (LCOS) and liquid crystal reflectarray antenna (LCRA). Specifically, this communication focuses on the state of the art of LC-RIS, highlighting the unique features of this newly raised enabling technology for post-5G (6G) networks and comparing it to LCOS, which operates at different frequencies and is suited to different use cases. Drawing on insights from existing knowledge of LCOS and LCRA, opportunities and challenges are explored for LC-RIS’s technical advancements in enhancing the coverage, capacity, and energy efficiency of wireless networks. In particular, the development status and roadmap of LC-RIS in China is reviewed.

From Massive IoT Toward IoE: Evolution of Energy Efficient Autonomous Wireless Networks

From Massive IoT Toward IoE: Evolution of Energy Efficient Autonomous Wireless Networks

On the Physical Layer Security Performance Over RIS-Aided Dual-Hop RF-UOWC Mixed Network

Since security has been one of the crucial issues for high-yield communications such as 5G and 6G, researchers continuously come up with newer techniques to enhance the security and performance of these progressive wireless communications. Reconfigurable intelligent surface (RIS) is one of those techniques that artificially rearrange and optimize the propagation environment of electromagnetic waves to improve both spectrum and energy efficiency of wireless networks. Besides, in underwater communication, underwater optical wireless communication (UOWC) is a better alternative/replacement for conventional acoustic and radio frequency (RF) technologies. Hence, mixed RIS-aided RF-UOWC can be treated as a promising technology for future wireless networks. This work focuses on the secrecy performance of mixed dual-hop RIS-aided RF-UOWC networks under the intercepting effort of a probable eavesdropper. The RF link operates under generalized Gamma fading distribution; likewise, the UOWC link experiences the mixture exponential generalized Gamma distribution. The secrecy analysis subsumes the derivations of closed-form expressions for average secrecy capacity, exact and lower bound of secrecy outage probability, strictly positive secrecy capacity, and effective secrecy throughput, all in terms of Meijer's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> functions. Capitalizing on these derivations, the effects of heterodyne and intensity modulation/direct detection systems, underwater turbulence resulting from air bubble levels, temperature gradients, and salinity gradients, are measured. Unlike conventional models that merely deal with thermally uniform scenarios, this proposed model is likely to be unique in terms of dealing with secrecy analysis of a temperature gradient RIS-aided RF-UOWC network. Lastly, the derivations are validated via Monte-Carlo simulations.

A Novel Energy Efficiency Metric for Next-Generation Green Wireless Communication Network Design

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Channel Power Gain Estimation for Terahertz Vehicle-to-Infrastructure Networks

The use of terahertz (THz) frequencies has been recommended to achieve high-speed and ultra-low latency transmissions. Although there exist very large bandwidths in the THz frequency bands, THz channels are significantly dynamic and complicated, which is challenging for channel estimation. To improve the energy efficiency of wireless networks, THz channel power gains need to be precisely evaluated for determining optimal THz transmission frequencies and power control. Therefore, this work presents a novel conditional generative adversarial networks (GAN) based channel power gain estimation solution in the THz vehicle-to-infrastructure (V2I) networks with leaky-wave antennas, where the THz frequency has a big effect on the antenna gain, path loss and atmospheric attenuation. Simulation results confirm that our solution can accurately estimate the channel power gains versus the THz frequencies at a fast speed.

Double Auction Offloading for Energy and Cost Efficient Wireless Networks

Network infrastructure sharing and mobile traffic offloading are promising technologies for Heterogeneous Networks (HetNets) to provide energy and cost effective services. In order to decrease the energy requirements and the capital and operational expenditures, Mobile Network Operators (MNOs) and third parties cooperate dynamically with changing roles leading to a novel market model, where innovative challenges are introduced. In this paper, a novel resource sharing and offloading algorithm is introduced based on a double auction mechanism where MNOs and third parties buy and sell capacity and roam their traffic among each other. For low traffic periods, Base Stations (BSs) and Small Cells (SCs) can even be switched off in order to gain even more in energy and cost. Due to the complexity of the scenario, we adopt the multi-objective optimization theory to capture the conflicting interests of the participating entities and we design an iterative double auction algorithm that ensures the efficient operation of the market. Additionally, the selection of the appropriate time periods to apply the proposed algorithm is of great importance. Thus, we propose a machine learning technique for traffic load prediction and for the selection of the most effective time periods to offload traffic and switch off the Base Stations. Analytical and experimental results are presented to assess the performance of the algorithm.

Enhancement of the performance of wireless sensor networks using the multihop multiantenna power beacon path selection method in intelligent structures.

Sensor nodes are the building blocks of wireless sensor networks (WSNs), which may gather, analyze, and transmit various types of information to a certain destination. Data collection and transmission to the destination are the main responsibilities of sensor nodes at specified time intervals. However, one of the biggest issues with WSNs is the creation of energy-efficient wireless network algorithms. In this paper, a multi-hop multi-antenna power beacon path selection (MMPS) protocol is proposed. The proposed approach consists of a source, a destination, relays, power beacons generating radio frequency (RF) signals for energy harvesting, and eavesdroppers. We used physical layer security associated with energy harvesting to protect data from eavesdroppers without requiring higher layer data encryption and enhance the energy consumption of wireless networks. The signal's broadcast strength must be high enough to allow for energy harvesting while being low enough to prevent eavesdropping. The process continues until the data reaches the destination. Comparing the performance of MMPS with those of conventional methods, MMPS enhanced the wireless network outage probability (OP) up to 99.7%, life time, energy consumption, protection from eavesdroppers, and more resistant to hardware impairments which increased the immunity up to 95%.

Unsupervised Learning-Inspired Power Control Methods for Energy-Efficient Wireless Networks Over Fading Channels

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks.

Synergistic Benefits in IRS- and RS-Enabled C-RAN With Energy-Efficient Clustering

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.

Performance Analysis of Wireless Sensor Network by Variation in Cluster Head Selection through mLEACH-CS Algorithm

Abstract: The energy depletion at the nodes due to large volume of data transmission and data reception for large scale Wireless Sensor Networks (WSN) is one of the important areas of concern. Unbalanced energy consumption due to data traffic overhead at the nodes near the base station (BS) results in early node death and reduction in network lifetime. To balance the load at the wireless nodes during clustering, a novel hybrid clustering and routing protocol has been proposed based on mLEACH (modified Low-Energy Adaptive Clustering Hierarchy) and Cuckoo Search (CS) algorithm. Initially, mLEACH protocol is utilized to solve a novel equation designed to select the cluster heads by rotation at every round of data transmission based on the maximum permissible cluster heads in the network. In the second phase, CS algorithm is implemented for uniform distribution of the cluster heads in the network for efficient multi-hop data routing in the network based on several weighted factors. The performance of the proposed algorithm is compared with other algorithms in literature in terms of energy consumption and network lifetime for different positions of the base station and varied number of cluster heads in the model application area. The proposed mLEACH-CS algorithm shows a significant improvement by 60% considering 300 nodes in the WSN architecture while it decreased to 40% for denser network architecture. Optimal choice of the number and position of the cluster heads in certain network architecture has proved beneficial for designing an energy efficient wireless network with improved network lifetime.

Enhanced Lion Optimization with Efficient Path Routing Equalization Technique against DoS Attack in Wireless Sensor Network

In WSN, DoS (denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that, all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks, network performance can be improved. Therefore, security is a fundamental requirement for these networks. Effective routing is necessary in order to overcome the issued faced by the crosslayer in the DOS attack of the WSN network for the purpose of good transmission. This research work mainly focuses on performance evaluation using optimization methods. To establish the efficient path in the crosslayer against DoS attack, this paper has proposed enhanced lion optimization with an efficient path routing equalization technique (LOEPRE). If any failure node occurs in the network, then the node is recognized and the transfer of the data packet is done to the other node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcomes these issues by improving the path efficiently with robust security. It consists of three phases: the initial phase includes the route discovery in the network. In the second phase, the transfer of data is done in the high router path for security purposes. Finally, the efficient path routing equalization technique is used for minimizing the overload in the network; it provides the equalized path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolonged network lifetime and minimum packet latency and minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method is highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR, and TSTP. It achieves better performance than existing algorithms in comparing metric connectivity ratio, end-to-end delay, overhead, throughput, and packet delivery ratio.

Energy and spectral efficient resource allocation in 5G HetNet using optimized deep bi‐BRLSTM model

AbstractThe heterogeneous deployment of ultra‐dense femto cell dispenses an enhanced coverage and capacity for the users in the 5G wireless communication system. Due to the substantial growth in wireless data traffic and the inflation of energy consumption, the design of energy and spectrum efficient wireless network is seems to be an extreme concern. Therefore, there is a need to develop an energy and spectrum efficient resource allocation model. The joint contribution of the maximization of energy efficiency (EE) and spectral efficiency (SE) with its constraints is formulated as a multiobjective optimization problem. The resource allocation (RA) issue in the network is handled by the hybrid deep bidirectional battle royale long short term memory (Deep bi‐BRLSTM) model. The loss function in the network model is taken as a consideration to optimize the SE and EE. Fuzzy based energy efficient adaptive atom search optimizer (FEEAAS) is instigated to optimize the trade‐off of EE and SE consecutively. It is embedded with high convergence rate with maximum efficiency which is suitable for power optimization with maximum resource allocation. The performance of the proposed model is implemented in Matlab Simulink platform. The simulation result reveals that the presented model attains an optimal trade‐off between the EE and SE and also improves the overall throughput and delay when compared with existing solutions.

Achieving energy efficiency for 5G at base stations level

As the era of 5th Generation (5G) networks is dawning, several pertinent issues associated with the improvements that have to be achieved in future communications are attracting increasing research attention. This work, considered achieving energy efficiency for 5G at base stations level. Energy efficiency simply means using less energy to perform the same task thereby, eliminating energy waste. The objective of this paper is to examine the ways of deploying energy efficient hardware at the base stations in order to make the base stations more green energy based. Considering the current energy concerns, base stations in emerging wireless networks range from low-energy BSS to high-energy BSS with entirely different constraints in either case. In view of these extremes, this paper examines the major components behind energy-efficient wireless communication network design during the peak and off-peak traffic periods. It was discovered that the Power amplifier consumes up to 59% of the energy at the base stations. Improving a Power amplifier by bringing linearity could improve the efficiency of the BS and hence reduce the energy crunch at the BS level from 59% to 51%.