Energy efficient ultra‐dense networks based on multi‐objective optimisation framework

In this paper, the tradeoff relationship between the spectral efficiency (SE) and energy efficiency (EE) of homogenous cellular networks in which the BSs are arbitrarily distributed is investigated. The network performance metrics of SE and EE are assessed subject to a downlink transmission outage constraint in interference-limited operational environments. The EE is expressed in closed form as a function of SE, based on which the performance bounds of the network are derived. Unlike the traditional inverse relationship between SE and EE, it is found in this paper that there exists an operational regime for which both the SE and EE increase while satisfying the outage requirement, and the density of base stations (BSs) simultaneously sharing the spectrum is optimal. The difference in the performance achieved for the SE when operating in the EE maximizing mode as compared with the SE maximizing mode strongly depends on the received signal-to-interference ratio (SIR) threshold. In the SE-EE tradeoff regime, the analytical tools from microeconomics theory are applied to determine the optimal BS density with respect to the utility achieved by the network operator via balancing the SE and EE objectives. Numerical results show that, by tuning a preference factor toward either the SE or EE metrics, it is feasible to realize Pareto optimal performance.

In this paper, we investigate the energy efficiency and spectral efficiency of heterogeneous networks, where macro-cell and microcell base stations are deployed according to two separate stochastic geometry based processes. Here, micro base stations are placed over the regions for which the received signal strength is below a certain limit. By employing the deployment model we propose, we investigate the effects of certain parameters like macro and micro base station densities, and user density on energy efficiency of heterogeneous networks. The results show that deploying micro base stations considerably improves both energy efficiency and spectral efficiency in a dense user scenario.

Forecasts show that traffic demand will continue to grow exponentially over the foreseeable future and mobile operators will have to upgrade and densify their networks to support this traffic demand. However, due to the spatiotemporal variation of traffic, some base stations (BSs) may remain idle during low traffic conditions. In this paper, we use tools from stochastic geometry to analyze the capacity and energy efficiency (EE) performance of a dense heterogeneous network (HetNet). We derive a multi-user connectivity model based on the LTE standard to determine realistic BS density configurations. We implement conventional sleep mode and investigate the effect of biasing on the capacity and EE performance of the HetNet. Our results show that the prevailing user density influences the HetNet performance, and that conventional sleep mode significantly enhances the EE performance. Appropriate biasing of the HetNet results in further EE performance enhancement.

Research into cutting-edge systems that can satisfy the rising needs for greater data rate and increased reliability is becoming more and more necessary as wireless communication technologies continue to evolve. To enhance the spectral and energy efficiencies, this study examine the performance of massive multiple input multiple output (mMIMO) communication systems using wavelet-order orthogonal frequency division multiplexing (W-OFDM). Also, a communication system was created based on W-OFDM Massive MIMO using MATLAB to model its operation. The simulated system's various stages of operation was examined through detailed analysis and its performance was compare with prior studies and practical networks on OFDM. Key performance metrics, specifically the data throughput, was evaluated to assess the effectiveness and viability of the system. The simulation results indicates that W-OFDM can achieve up to 35% reduction in peak-to-average-power-ratio (PAPR) compared to the traditional OFDM, with a 20% improvement in BER performance under Rayleigh fading conditions. Also, the spectral efficiency of W-OFDM mMIMO outperforms convectional OFDM by 15%, making it a viable technology for future communication networks requiring high data rates and reliability. This implies that, the analytical evaluation confirms that W-OFDM mMIMO provides substantial benefits for improving energy and spectral efficiencies in wireless communication systems, addressing the challenges of increased user demands for high spectrum. This study contributes to the understanding of MIMO systems' performance optimization, highlighting the importance of antenna configuration and cell distance. Comprehensive analysis of spectral efficiency and energy efficiency trade-offs in MIMO systems and identification of optimal antenna configuration for maximum energy efficiency, addressing some of the challenges of future wireless communication systems.

In this paper, we first give an overview of the coexistence of cellular with IEEE 802.11 technologies in the unlicensed bands. We then present a coexistence mechanism for Fifth‐Generation (5G) New Radio on Unlicensed (NR‐U) small cells located within buildings to coexist with the IEEE 802.11ad/ay, also termed as Wireless Gigabit (WiGig). Small cells are dual‐band enabled operating in the 60 GHz unlicensed and 28 GHz licensed millimeter‐wave (mmW) bands. We develop an interference avoidance scheme in the time domain to avoid cochannel interference (CCI) between in‐building NR‐U small cells and WiGig access points (APs). We then derive average capacity, spectral efficiency (SE), and energy efficiency (EE) performance metrics of in‐building small cells. Extensive system‐level numerical and simulation results and analyses are carried out for a number of variants of NR‐U, including NR standalone, NR‐U standalone, and NR‐U anchored. We also analyze the impact of the spatial reuse of both mmW spectra of multiple NR‐U anchored operators with a WiGig operator. It is shown that NR‐U anchored provides the best average capacity and EE performances, whereas NR‐U standalone provides the best SE performance. Moreover, both vertical spatial reuse intrabuilding level and horizontal spatial reuse interbuilding level of mmW spectra in small cells of an NR‐U anchored can improve its SE and EE performances. Finally, we show that by choosing appropriate values of vertical and horizontal spatial reuse factors, the proposed coexistence mechanism can achieve the expected SE and EE requirements for the future Sixth‐Generation (6G) mobile networks.

An optimization framework for RIS-based energy-efficient multi-cell NOMA systems

Maximizing lower bound of energy efficiency in multi-tier heterogeneous cellular network via stochastic geometry

Not only high spectral efficiency (SE) but also high energy efficiency (EE) are required for future wireless communication systems. Radio orbital angular momentum (OAM) provides a new perspective of mode multiplexing to improve SE. However, there are few studies on the EE performance of OAM mode multiplexing. In this paper, we investigate the SE and EE of a misaligned uniform concentric circle array (UCCA)-based multi-carrier multimode OAM and multiple-input multiple-output (MCMM-OAM-MIMO) system in the line-of-sight (LoS) channel, in which two transceiver architectures implemented by radio frequency (RF) analog synthesis and baseband digital synthesis are considered. The distance and angle of arrival (AoA) estimation are utilized for channel estimation and signal detection, whose training overhead is much less than that of traditional MIMO systems. Simulation results validate that the UCCA-based MCMM-OAM-MI-MO system is superior to conventional MIMO-OFDM system in the EE and SE performances.

The scientific and business communities are showing considerable interest in wireless sensor networks (WSN). The availability of low-cost, small-scale components like CPUs, radios, and sensors, which are often combined into a single chip, is crucial. Parallel to the evolution of WSNs, the concepts of the IoT have been evolving in recent years. Wireless communication technologies may play a significant role in the implementation of IoT, despite the fact that IoT does not need or require any particular technology for communication. WSN assisted IoT networks can drive several applications in many industries. The proposed research explores the possibility of enhancing energy efficiency in WSN-assisted IoTN by balancing various challenging sensor network performance metrics. The base station's current placement inside the sensing field is predetermined by the preexisting routing algorithms. Our study examines the impact of base station placement outside and within the prescribed sensing domains on energy consumption and network longevity. In addition, methods for transferring data from the distributed source sensor to the base station while minimizing energy consumption are investigated. In this preliminary study, we focus on developing an algorithm for WSN-Assisted IoTN that can balance network factors such as hop count, communication distance, and residual energy. To further optimize the routing route between local cluster heads and the base station, a novel network architecture is built based on the Ant-optimization model, which uses centroid routing to balance energy consumption among local clusters. An open-source Network Simulator (NS-3) is used to model the behaviour of the proposed routing protocols and compare them to comparable existing network protocols. All of the suggested protocols have the same fundamentals for creating networks, however they vary in terms of routing, optimization, and performance depending on the development effort under consideration.

Massive Multiple-Input Multiple-Output (MIMO) is an extension of the conventional MIMO in the wireless systems which improves both of the access density and the spectral efficiency by adding a massive number of antenna array at the base station (BS). Massive MIMO increases the spectral efficiency by using the beamforming. Besides, the beamforming in massive MIMO improves the energy efficiency by focusing the energy in the desired direction instead of the omnidirectional propagation. In this paper, we propose and discuss different beamforming objectives in both the uplink and the downlink channels. These proposed objectives can be either use the beamforming of the desired signal without nulling the interference or use the beamforming with interference nulling. The beamforming with nulling objectives have better performance than those without nulling but this leads to a higher computational complexity as well. The results of this paper show and compare the performance of these objective including the spectral efficiency and energy efficiency as well as the computational complexity. Index Terms— Beamforming, Interefernce nulling, Energy efficiency, Massive MIMO, Spectral efficiency.

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.

Coordinated Multi-Point (CoMP) reception between base stations (BSs) has shown to be an efficient way to improve user performance in the uplink (UL). However, it comes with an increased network power consumption due to additional signal processing at the base station and information transfer on the backhaul. In this paper we analyze the impact of two UL CoMP schemes on the energy consumption of a heterogeneous network featuring fast BS sleep mode. We show that depending on the load and the UL CoMP scheme applied, a network with UL CoMP may be more energy efficient than without. We also propose a CoMP scheme selection algorithm so that the most energy efficient scheme is selected for a user during its activity time. Results show that the selection algorithm, evaluated in an ideal and a practical variant, leads to the lowest network power consumption for different traffic loads.

The present paper investigates the trade‐offs between the energy efficiency (EE) and spectral efficiency (SE) in the full‐duplex (FD) multiuser multi‐input multioutput (MU‐MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade‐offs between the EE and SE metrics, we first introduce three conventional single‐objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA‐based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm‐based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE‐SE trade‐offs in the considered system.

This paper provides an overview on our research on energy-efficient resource optimization and transmission strategies for hyper-cellular systems over the past five years. It summarizes the fundamental relationship between energy efficiency (EE) and spectral efficiency (SE), as well as the impact of physical resources on the relationship, such as time, frequency, antenna, and cache, and reviews the energy-efficient transmission strategies adapted to dynamic channels and traffic. It is in the context of key technologies in fifth-generation (5G) mobile communication systems, including massive centralized/distributed antenna systems, ultra-dense networks (UDNs), device-to-device (D2D) communications, and coordinated multi-point transmission (CoMP). It is shown that the EE-SE relationship in all representative systems exhibits a “bell-shaped curve”, meaning SE and EE increase simultaneously when SE is low while there is a tradeoff between SE and EE otherwise. Furthermore, the SE loss in EE-optimal policies is low, but the EE loss in SE-optimal policies is high. EE approaches zero with an increase in the number of antennas. Universal frequency reuse can maximize EE when the base station (BS)-to-user density ratio is large while partial frequency reuse is better for EE otherwise. Caching at BSs can improve EE and EE gain is high in scenarios with weak interference, low-capacity backhaul, and large content popularity skewness.

The integration of real-time electrocardiogram (ECG) signal processing into smart health monitoring devices demands high-performance computing platforms that ensure low latency, high accuracy, and energy efficiency. Field-Programmable Gate Arrays (FPGAs) have emerged as a compelling solution, offering customizable architectures capable of executing complex biomedical signal processing tasks with minimal power consumption. This chapter presents a comprehensive study on FPGA-based QRS detection systems tailored for wearable and portable healthcare applications. Key aspects such as noise suppression, real-time feature extraction, algorithm simplification, and hardware optimization are examined with a focus on achieving robust, lowpower, and real-time ECG analysis. A detailed comparison with conventional software-based implementations highlights the advantages of FPGA platforms in terms of processing speed, energy efficiency, and deterministic performance, validation frameworks, benchmarking methodologies, and resource utilization metrics are discussed to guide scalable and clinically viable system development.