Energy Efficiency of Distributed Signal Processing in Wireless Networks: A Cross-Layer Analysis

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.

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.

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

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.

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.

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.

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).

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.

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

Unmanned aerial vehicles that abbreviates (UAVs) are flying platforms, known as drones, which have features such as mobility, adaptive altitude and flexibility. UAVs admit numerous applications that can be used as aerial base stations to enhance coverage, capacity, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. A Software Defined Network (SDN) Controller is the application that acts as a strategic control point in a software-defined network. It is the ’brain’ of the network. Controller manages flow control to the routers/switches ‘under’ (via southbound APIs), the business logic and applications ’above’ (via northbound APIs) to deploy intelligent networks. Wireless networking with software defined (SDWN) is the use of SDN conceptions in wireless networks by using a controller in the control plane. SDWN facilitates the creation of new adaptive mechanisms according to various applications and user requirements, such as mobility, handover, security and quality of service (QoS). In this paper, simulation work has been conducted to compare and investigate four SDN controllers (Pox, Ryu, Floodlight and OpenDaylight) in order to see which one is suitable to be used. Mininet-Wifi has been selected as the simulation tool to do the experiments and Python script for programming. The results obtained reveals that Ryu controller is the best selection in terms of latency and packet loss.

The advent of network coding promises to change many aspects of networking. Network coding moves away from the classical approach of networking, which treats networks as akin to physical transportation systems. We overview some of the main features of network coding that are most relevant to wireless networks. In particular, we discuss the fact that random distributed network coding is asymptotically optimal for wireless networks with and without packet erasures. These results are extremely general and allow packet loss correlation, such as may occur in fading wireless channels. The coded network lends itself, for multicast connections, to a cost optimization which not only outperforms traditional routing tree-based approaches, but also lends itself to a distributed implementation and to a dynamic implementation when changing conditions, such as mobility, arise. We illustrate the performance of such optimization methods for energy efficiency in wireless networks and propose some new directions for research in the area.

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.

To enhance channel throughput, end to end delay and energy efficiency for wireless networks based on a single-hop topology, this paper proposes a distributed reservation multiple access scheme with adaptive switching requests (DRMA-ASR). The DRMA-ASR scheme uses a repeating frame structure with a reservation period and a data transmission period based on the principles of the ALOHA-reservation scheme. To improve performance, this scheme proposes an adaptive or skipped reservation period with the probability based on a hidden Markov model (HMM). In addition, request packets are transmitted based on a permission probability to increase the probability with which the reservation period can be reduced in duration or skipped, and energy saving is achieved by putting terminals into a low power sleep mode when they are not required to transmit during the data transmission period. The scheme applies a virtual carrier sensing mechanism and does not require physical sensing. Simulation results show that DRMA-ASR provides better performance in terms of channel throughput, end to end delay, and energy efficiency than CSMA/CA with both small and large interference ranges.

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.