Spatial Spectrum and Energy Efficiency of Random Cellular Networks

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

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

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 has gained its significance when service providers' operational costs burden with the rapidly growing data traffic demand in cellular networks. In this paper, we propose an energy efficiency model for Poisson-Voronoi tessellation (PVT) cellular networks considering spatial distributions of traffic load and power consumption. The spatial distributions of traffic load and power consumption are derived for a typical PVT cell, and can be directly extended to the whole PVT cellular network based on the Palm theory. Furthermore, the energy efficiency of PVT cellular networks is evaluated by taking into account traffic load characteristics, wireless channel effects and interference. Both numerical and Monte Carlo simulations are conducted to evaluate the performance of the energy efficiency model in PVT cellular networks. These simulation results demonstrate that there exist maximal limits for energy efficiency in PVT cellular networks for given wireless channel conditions and user intensity in a cell.

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.

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.

Energy efficiency in wireless sensor network is a well studied research problem. A recent research study on IoT based precision agriculture has reported that designing the energy efficient data aggregation at the base station is an open research problem. The paper is motivated by recent developments in the energy efficient models and algorithms to maintain energy requirement at the base station. The base station is energy constrained device and it has to survive maintaining the energy requirements from various sensors and gateway modules. Therefore base station uses energy harvesting to maintain energy neutrality. There are two important research questions to study; how to estimate the solar energy to maintain the energy neutrality at the base station having solar backup for energy harvesting and how to estimate the energy requirements at the base station at random point of time. The first question has been answered quite extensively. In this paper, we are trying to address the second question of how to estimate the energy requirements at the base station of IoT enabled precision agriculture. In precision agriculture, numerous types of sensors including soil, moisture, temperature, wind direction, wind speed, camera, drone etc. are used to continuously monitor the field and connect to the base station. Hence base station has different modes of power requirements such as low power, medium power, and high power modes based on different types of wireless communication medium at random point of time. The paper proposes a novel product density model to estimate the energy requirements at the base station. Moreover, an Improved Duty Cycling algorithm is proposed using residual energy parameter. The performance of the proposed Improved Duty Cycling is compared with two other algorithms and the proposed algorithm shows an improvement in terms of average energy consumption, residual energy performance, throughput etc.

In this paper, we analyze the signal-to-interference-plus-noise ratio (SINR) performance at a mobile station (MS) in a random cellular network. The cellular network is formed by base stations (BSs) placed in a one-, two-, or three-dimensional space according to a possibly non-homogeneous Poisson point process, which is a generalization of the so-called shotgun cellular system. We develop a sequence of equivalence relations for the SCSs and use them to derive semi-analytical expressions for the coverage probability at the MS when the transmissions from each BS may be affected by random fading with arbitrary distributions as well as attenuation following arbitrary path-loss models. For homogeneous Poisson point processes in the interference-limited case with power-law path-loss model, we show that the SINR distribution is the same for all fading distributions and is not a function of the base station density. In addition, the influence of random transmission power, power control, and multiple channel reuse groups on the downlink performance is also discussed. The techniques developed for the analysis of SINR have applications beyond cellular networks and can be used in similar studies for cognitive radio networks, femtocell networks, and other heterogeneous and multi-tier networks.

Another paradigm shift that is in the process of being introduced in the telecommunication sector is the sixth generation (6G) wireless networks whereby a low degree of latency of operation, high spectral performance and energy-conscious design are being introduced to meet the constantly increasing number of devices and data requirements of the air. Although it has been revealed that there are different papers on the utilization of AI in 6G, the majority of them remark on two phenomena that cannot be correlated with one another and these include spectrum and energy efficiency. The systematic and mutual execution of AI-based resource allocation will be used to address the gap in the paper through the joint maximization of the energy efficiency and spectral efficiency of 6G networks.The paper proposes a new taxonomy of AI techniques that is determined by the deployment layer (edge, core, and THz frontier), the most popular known architectural and algorithmic operators of the joint optimization, and a comparative analysis of the known available methods of the technological fields, e.g., adaptive modulation, handover optimization, meta-learning, federated learning and explainable AI in comparison with the available surveys. It further possesses an offer of synthesis of digital twins on a resource-centric approach, blockchain-based Internet of Things, and secure communication protocols.Key contributions include: (1) a unified classification of AI approaches targeting co-optimization of spectrum and energy; (2) a comparative evaluation of techniques across key 6G scenarios (UDN, IoT, THz, RIS); and (3) identification of open research challenges and practical deployment constraints. The paper further outlines how AI fosters sustainability, scalability, and transparency in next-generation wireless systems.

The station density is believed to be a crucial factor that affects the energy efficiency of relay-assisted cellular networks dramatically, which can not be neglected for practical network design and planning. In this paper, we propose an analytical model to investigate how the station density affects the energy efficiency of relay-assisted cellular networks. With the aid of stochastic geometry theory, we derive the coverage probabilities and mean achievable rates of both cooperative users and non-cooperative users, and based on these, we further derive the analytical expression of energy efficiency and verify that by simulation. Numerical results show the significant improvement on energy efficiency brought by relays and that the energy efficiency presents a convex trend as the station density varies, which sheds key insights into the optimal densities of both base stations and relays with respect to energy efficient network deployment.

In order to support orders of traffic explosion with sustainable energy consumption, besides conventional Spectrum Efficiency (SE), Energy Efficiency (EE) is proposed as another critical metric for the next generation mobile networks (5G). This paper proposes to evaluate the potential EE gain of 5G on the basis of EARTH Energy Efficiency Evaluation Framework (E3F). As E3F indicated, the large-scale network traffic over the long term is definitely heterogeneous, which is still the trend in 5G. In this paper, relationship is deduced between the global EE metric and the snapshot EE metrics, which are obtained from small scale and short term. To be specific, this paper considers two typical 5G scenarios as different snapshots: the indoor office and the outdoor open space gathering. Each snapshot scenario has its own combinations of service/applications, hence distinct traffic model and resulted network deployment, power consumption and EE. The power model used for EE evaluation considers the different bandwidth and antenna configurations for different types of Base Stations (BSs) in different scenarios, meanwhile, it identifies the long term hardware evolution as a key factor to predict future power consumptions. Numerical analysis reveals that up to 1000x EE gain can be achieved by 5G and gain of hundreds times can be realized in 5G era, when 4G coexist with 5G.

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

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to enhance the energy efficiency of cellular networks. Meanwhile, the mobile data traffic is expected to increase exponentially. To accommodate the increasing data traffic with the limited radio frequency, enhancing the spectrum efficiency is critical for next generation cellular networks. In this paper, we propose an auction-based energy-spectrum trading scheme which exploits the cooperation between primary base stations (PBSs) and the secondary base stations (SBSs) to enhance the energy as well as spectrum efficiency of cellular networks. In the cooperation, by leveraging cognitive radio, PBSs share the licensed spectrum with SBSs, and the SBSs provide data service to the primary users under its coverage utilizing the shared bandwidth. The cooperation between PSBs and SBSs can significantly improve the energy and spectral efficiency of cellular networks. However, optimizing the bandwidth sharing between PBSs and SBSs is an NP-hard problem. Solving such problem using centralized algorithms is not computationally efficient, especially when considering a large number of PBSs and SBSs. Thus, we design an auction-based decentralized mechanism to enable the cooperation between PBSs and SBSs. Simulation results that demonstrated the performance and viability of the proposed decentralized mechanism.

The heterogeneous deployment of ultra dense small cells such as femtocells in the coverage area of the traditional macrocells is seen as a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the fifth generation wireless network. However, the unplanned and ultra-dense deployment of femtocells will lead to increase in total energy consumption, cross-tier interference (interference between macrocells and femtocells), co-tier interference (interference between neighbouring femtocells) and inadequate QoS provisioning. Therefore, there is a need to develop a radio resource allocation algorithm that will jointly maximize the energy efficiency (EE) and spectrum efficiency (SE) of the overall networks. Unfortunately, maximizing the EE results in low performance of the SE and vice versa. This paper investigates how to balance the trade-off that arises when maximizing both the EE and the SE simultaneously. The joint EE and SE maximization problem is formulated as a multi-objective optimization problem, which is later converted into a single-objective optimization problem using the weighted sum method. An iterative algorithm based on the Lagrangian dual decomposition method is proposed. Simulation results show that the proposed algorithm achieves an optimal trade-off between the EE and the SE with fast convergence.

Green Small Cell Networks aim at achieving high rates and low powers by offloading users with low signal-to-noise-ratios from macrocell to the pico base station. In this work, we propose to jointly optimise energy efficiency (EE) and spectrum efficiency (SE) such that the network providers can dynamically tune the trade-off parameter for different design requirements. This paper formulates the EE-SE trade-off as a multi-objective optimisation problem (MOP) in the uplink of multi-user two-tier Orthogonal Frequency Division Multiplexing Heterogeneous Networks. Using the weighted sum method, the MOP can be transformed into a single-objective optimisation problem (SOP). The proposed EE and SE trade-off optimisation problem is strictly quasi-concave. Hence, using Dual Decomposition approach, we derive the unique optimal solution. Numerical results demonstrate the effectiveness of the proposed approach and illustrate the fundamental tradeoff between EE and SE for different tradeoff parameters such as maximum transmission power and circuit power.