Energy Efficient Distributed Reservation Multiple Access with Adaptive Switching Requests for Wireless Networks

In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: (i) hybrid, where the signal processing operations are shared between nodes and APs, (ii) centralized, where signal processing is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.

Ever increasing user of electronic devices and high-data-rate applications has lead to continuous growing data traffic and hence carbon dioxide emission. Economical and environmental concern needs improved energy efficient network. As an energy efficient solution needs to outperform under various dynamic situations like varying traffic load, mobility, interference conditions etc Keeping in view of energy efficiency in wireless networks this paper aims at providing an extensive survey of the various researches at network architecture level for minimizing energy consumption in wireless mobile networks. A brief overview of the ongoing projects has also been discussed.

Network throughout and energy efficiency are of paramount importance for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal network throughout and energy efficiency simultaneously. So it is very necessary to find a solution based on tradeoff between network throughput and energy efficiency, which have not yet been well studied in existing works. In this paper, we present a cooperative differential game model and find a time-consistency solution by Shapley value which determines a fair distribution of the total cooperative cost among players.

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.

Cell discontinuous transmission (DTX) has been proposed as a solution to reduce the energy consumption of cellular networks. This paper investigates the impact of network traffic load on the spectral and energy efficiency of cellular networks with DTX. The signal-to-interference-plus-noise ratio (SINR) distribution as a function of traffic load is derived first. Then, the sufficient condition for ignoring thermal noise and simplifying the SINR distribution is investigated. Based on the simplified SINR distribution, the network spectral and energy efficiency as functions of network traffic load are derived. It is shown that the network spectral efficiency increases monotonically in traffic load, while the optimal network energy efficiency depends on the ratio of the sleep-mode power consumption to the active-mode power consumption of base stations. If the ratio is larger than a certain threshold, the network energy efficiency increases monotonically with network traffic load and is maximized when the network is fully loaded. Otherwise, the network energy efficiency first increases and then decreases in network traffic load. The optimal load can be identified with a binary search algorithm. The power ratio threshold depends solely on the path loss exponent α, e.g., 56% for α = 4. All these analytic results are further validated by the numerical simulations.

Energy efficiency of wireless networks is becoming more and more important because of the steadily rising energy cost and environmental concerns. In a real network with unequal traffic load distributions among cells, by offloading some edge users in a heavily-loaded cell to neighboring lightly-loaded cell, load balance, aims to utilize the radio resource more efficiently and consequently can improve the end-user performance. In this paper an energy efficient load balancing algorithm is proposed. In the proposed algorithm, the target cell in load balance is selected based on the prediction of the possible performance. Furthermore, an energy efficiency scaling factor is introduced into the criteria when selecting the target cell in load balance to achieve the joint optimization of both load balance and energy saving. Simulation results show that the proposed algorithm can reduce the power consumption in the network and at the same time improve the cell throughput.

Opportunistic spectrum access and adaptive power management are effective techniques to improve throughput, delay performance, and energy efficiency for wireless networks. In this paper, we consider the joint design of opportunistic spectrum access and adaptive power management under the setting of multi-radio nodes and multi-channel wireless local area networks (WLANs) under the distributed coordination function (DCF) mode. This design problem is particularly challenging due to the conflicting nature of the multi-radio capability of a node, i.e., multiple radios improve throughput and delay performance at the cost of increased energy consumption. To address this problem, we propose a power-saving multi-channel MAC protocol (PSM-MMAC), which is capable of reducing the collision probability and the waiting time in the ‘awake’ state of a node, resulting in improved throughput, delay performance, and energy efficiency. The key ideas of PSM-MMAC are the following: we first estimate the number of active links; given this estimation as well as queue lengths and channel conditions, we appropriately select channels, radios, and power states (i.e., awake or doze state); then we optimize the medium access probability in p-persistent CSMA used in the data exchange. Another contribution of this paper is an analytical model that characterizes the throughput performance. Simulation results validate the accuracy of our analytical model and show that our proposed protocol is able to significantly improve both throughput and energy efficiency.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

A heterogeneous cellular network (HCN) is useful to increase the spectral and energy efficiency of wireless networks and to reduce the traffic load from the macro cell.The performance of the secondary user equipment (SUE) is affected by interference from the eNodeB (eNB) in a macro cell.To decrease the interference between the macro cell and the small cell, allocating resources properly is essential to an HCN.This study considers the scenario of a software-defined heterogeneous cellular network and performs the resource allocation process.First, we show the system model of HCN and formulate the optimization problem.The optimization problem is a complex process including power and frequency resource allocation, which imposes an extremely high complexity to the HCN.Therefore, a hierarchical resource allocation scheme is proposed, which including subchannel selection and a particle swarm optimization (PSO)-based power allocation algorithm.Simulation results show that the proposed hierarchical scheme is effective in improving the system capacity and energy efficiency.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

10 - Improving energy efficiency in next-generation IoT networks with beam adjustment technique

The demand for wireless connectivity has increased recently, driven primarily by Internet of Things (IoTs) and 6G applications. Since various wireless networks operate on an unlicensed band and coexist with upcoming IoT networks, undesired interference in IoT networks from other networks is inevitable. In addition, interference heavily affects the lifetime of IoT networks owing to their limited battery capacity. However, the interference of underlay networks in hierarchical coexisting networks has not yet been analyzed and its impact on energy efficiency remains uncharacterized. In this paper, the energy efficiency of the underlay network is statistically analyzed in the presence of interference from a coexisting overlay network. Based on our analyses, a closed-form expression for the average energy efficiency of the underlay network is derived and a packet size that maximizes the average energy efficiency is then proposed. The results of our analyses are consistent with the actual results, confirming that both the average energy efficiency of the underlay network and its optimal packet size can be readily obtained using our analyses without the need for extensive simulations. Simulation results confirmed the validity of the our statistical analyses. The simulation results demonstrate that our analyses can accurately and analytically determine the energy efficiency of the underlay network, and the proposed packet size can achieve more than 99 % of the optimal performance. The feasibility of adaptive packet tuning is also verified using actual overlay packet traces as inputs for our simulation.

This chapter addresses a number of issues related to standardization and regulatory policies aiming at promoting energy-efficient communications and networking, highlighting the need of synergic approach. It encompasses the analysis of various solutions supporting sophisticated energy management within each layer of the OSI stack model in the context of wireless networking, as well as the overview of cross-layer energy-efficient applications. All parts of the chapter have adequate references to the corresponding standardization activities on European and global level collected in the separate section. Finally, the steps towards implementation of energy-efficient networking solutions by means of cognitive radio technologies are presented.

Localization is one of the principal enabling wireless services now penetrating into every aspect of our life. Unfortunately, present localization schemes are often inaccurate (based on signal-distance conversion), or energy inefficient (such as GPS), or labor intensive (requiring previous field signal data collection). In this paper, a novel localization scheme named COAL, for COntext Aware Localization, is put forward to aim at both energy efficiency and accuracy. The core idea is to exploit users' context information such as an ongoing event to facilitate the localization scheme. With the facilitation of these context information, localization frequency can be significantly reduced to conserve energy while preserving high degree of accuracy. Besides that, COAL is complementary to existing location schemes using any wireless signal and can be implemented as an enhancement to them.

This paper presents an initial study on how to model user mobility in a wireless cellular network scenario. The model provides realistic patterns of movement between a number of locations. The aforementioned mobility model is built upon to introduce a base station (BS) energy efficient power state control algorithm (EESC) that is able to provide significant energy savings for the simulated scenario. Erratic subsequent on and off BS behavior is avoided by referring to an average loading curve, generated for each BS during a learning period, when making decisions. Moreover, BSs are turned off only when the users can be handed off to nearby cells with a predefined maximum allowed predicted loss in data rate. The resulting system is able to deliver an energy consumption reduction between 36% and 11% over an always on benchmark for the simulated scenarios. There is negligible loss in terms of achieved data rate per user.