Energy-Efficient Cellular Network Design Based on User's Mobility and Service Characteristics

Deploying low-altitude unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a promising approach to enhance cellular network coverage in dynamic and infrastructure-constrained environments. However, real-world UAV deployment involves complex challenges, including managing partially observable and mobile users, maintaining multi-hop backhaul connectivity to a fixed ground base station, mitigating co-channel interference via minimum UAV separation, and optimizing coverage efficiency, fairness, and energy consumption. This paper proposes a multi-agent deep reinforcement learning (MARL) framework that formulates the dynamic UAV deployment task as a centralized multi-input multi-output policy. Our method employs sequential action masking to guarantee multi-hop connectivity and enforce minimum distance constraints during UAV placement. The policy network integrates graph neural networks, residual convolutional layers, and feature-wise linear modulation to effectively capture spatial relations, inter-UAV interactions, and global and local network states. We validate our approach through extensive simulations across various user density scenarios, demonstrating significant improvements in coverage efficiency, fairness of coverage distribution, and flight energy reduction compared to strong baseline algorithms. Our proposed framework provides a comprehensive solution that simultaneously addresses dynamic user mobility, multi-hop connectivity, interference mitigation, and energy efficiency in UAV-assisted cellular networks.

We formulate the optimum personal paging area problem as an interval estimation problem, with an aim to reduce the paging signaling cost by minimizing the size of the paging area. A paging algorithm is developed to set-up the optimal one-step paging area that contains the locations of a mobile user with a high probability and thus to gain a coverage that is matched to the user's time-varying mobility pattern. With a particular mobility model, the effects of the user's mobility characteristics and the system parameters on the performance of the paging procedure are investigated.

Business case evaluations for LTE network offloading with cognitive femtocells

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) coexist. By optimizing MUs' network access (through traffic-offloading) and system resource-allocation, we aim to minimize the energy consumption of MUs by using the low-energy consumption of the backscatter communications. First, we develop a distributed traffic-offloading and resource-allocation scheme, when the short-range ambient backscatter (AB) communication (generally with communication range being several meters) is employed to assist MUs' data transmission. Based on our developed scheme, MUs choosing to access nearby SAPs can backscatter ambient RF signals for data transmission to reduce energy consumption. Then, we employ the long-range bistatic backscatter (BB) communication (with communication up to 270 meters) to support data transmission between MUs and the BS, and thus MUs can convey data by backscattering RF signals emitted from dedicated carrier emitters. Accordingly, we develop a joint traffic-offloading and resource-allocation scheme for the scenarios when the AB and BB communications are adopted by MUs accessing the SAPs and the BS, respectively. Finally, we validate and evaluate the performance of our developed schemes through numerical analyses.

Small cell deployments are seen as a promising solution for mobile operators due to their potential to improve coverage and increase capacity for indoor areas in a cost-efficient way. Meanwhile, other deployment scenarios are also being sought, such as in public transportation vehicles including buses and streetcars. In this paper, we propose a novel History-based Offloading Framework (HOF) to relieve overburdened macro networks from data traffic generated by mobile users in public transportation vehicles by utilizing small cells and Wi-Fi networks. A small base station (SBS) is installed onboard the vehicle; called a mobile SBS (mobSBS). Mobile users communicate with the mobSBS instead of the distant Macro base station (macroBS). In order to have efficient offloading in terms of bandwidth utilization, mobile data users are prioritized according to different pre-set classes. Our framework takes into account the mobile user's priority and service history to alleviate the effects of non-incessant Wi-Fi availability in order to maximize the offloaded macrocell data traffic. Extensive simulation results have shown that our proposed framework is highly effective in terms of the average offloaded macroBSs data traffic and the total count of offloaded users from the macroBSs.

Video streaming applications and services (VASs) consume an enormous amount of scarce resources in mobile devices and cellular wireless networks due to the demand for high data rates of video streaming. The limited resource of wireless media and unreliable nature of wireless channels in cellular networks make VASs challenging to deliver videos at high quality of experience (QoE). Therefore, in this paper, we propose a femtocaching framework of QoE-oriented resource efficiency optimization for high performance of cooperative VASs over 5G two-tier cellular networks, where the collaboration between macro base stations (BSs) and femtocells are exploited to efficiently deliver videos to mobile users (MUs). Our proposed framework aims at solving two problems. The first problem is how to cache the videos in femtocells to minimize the bandwidth resource consumed at the BSs and wasted at femtocells while guaranteeing high hit rate and utilizing the available storage resource of femtocells. The second one is how to encode the videos into descriptions and assign them to each femtocell for transmission, so as to minimize the reconstructed distortion of received videos for high playback quality at the MUs. The simulation results are further provided to demonstrate the benefits of the proposed framework.

Traditional cellular networks require the downlink (DL) and uplink (UL) of mobile users (MUs) to be associated with a single base station (BS). However, the power gap between BSs and MUs in different transmission environments results in the BS with the strongest downlink differing from the BS with the strongest uplink. In addition, the significant increase in the number of wireless machine type communication (MTC) devices accessing cellular networks has created a DL/UL traffic imbalance with higher traffic volume on the uplink. In this paper, a joint user association and resource partition framework for downlink-uplink decoupling (DUDe) is developed for a tiered heterogeneous cellular network (HCN). Different from the traditional association rules such as maximal received power and range extension, a coalition game based scheme is proposed for the optimal user association with DUDe. The stability and convergence of this scheme are proven and shown to converge to a Nash equilibrium at a geometric rate. Moreover, the DL and UL optimal bandwidth partition for BSs is derived based on user association considering fairness. Extensive simulation results demonstrate the effectiveness of the proposed scheme, which enhances the sum rate compared with other user association strategies.

Performance of TCP can be severely degraded in Mobile IP-based wireless networks where packet losses not related to network congestion occur frequently during inter-subnetwork handoffs by user mobility. To solve such a problem in the networks using Mobile IP, the packet buffering method at a base station(BS) recovers those packets dropped during handoff by forwarding the buffered packets at the old BS to the mobile users. But, when the mobile user moves to a congested BS in a new foreign subnetwork, those buffered packets forwarded by the old BS are dropped and TCP transmission performance of a mobile user degrades severely. In this paper, we propose a Modified BLUE(MBLUE) buffer required at a BS to increase TCP throughput in Mobile IP-based networks. When a queue length exceed a threshold and congestion grows, MBLUE increases its packet drop probability. But, when a TCP connection is added at new BS by a handoff, the old BS marks the buffered packets. And new BS receives the marked packets without dropping. Simulation results show that MBLUE buffer reduces congestion during handoffs and increases TCP throughputs.

An increasing number of mobile users (MUs) share common interests in general information, such as traffic information, weather forecast, and domestic/international news. However, the centralized infrastructure-based system has not been designed for efficiently disseminating the information of common interest (IoCI) to numerous requesters. Thanks to the rapid development of mobile devices equipped with large storage and multiple communication modes, opportunistic communication between a pair of MUs can be readily realized. With the aid of opportunistic networks formed by MUs, we can improve the connectivity of cellular networks in the rural areas, we can offload the tele-traffic from the overloaded cellular networks, and we can efficiently disseminate the IoCI in the densely populated areas. We commence with a detailed survey on the cross-disciplinary research area of social network analysis aided telecommunication networking. We continue by focusing our attention on the family of integrated cellular and large-scale opportunistic networks, whose performance is dominated by the inter-contact duration as well as the contact duration between any pairs of MUs. A continuous-time-pure-birth Markov chain is invoked for analyzing the relevant performance. We demonstrate that the delivery ratio of the IoCI before it expires becomes higher than 90% with the aid of opportunistic networks consisting of 20 MUs. Moreover, our experiments based on the InfoCom 2006 mobility trace show that the opportunistic networks are capable of offloading 58% of the tele-traffic from the cellular networks. Thereafter, we propose a hybrid information dissemination scheme for the integrated cellular and small-scale opportunistic networks, which is comprised of two main stages: 1) the base station-aided single-hop multicast (BSSHM) stage and 2) the cooperative multicast aided spontaneous dissemination stage. In contrast to their large-scale counterparts, in small-scale opportunistic networks, the information dissemination is mainly affected by the mobility of the MUs, by the wireless channel attenuation and by the resource scheduling, where a discrete-time-pure-birth Markov chain is utilized for characterizing the relevant performance. We demonstrate that our hybrid information dissemination scheme outperforms the traditional BSSHM in terms of various delay metrics, despite consuming less energy.

Understanding the Design of Mobile Social Networking

This paper studies the power allocation of an ultra-dense cellular network consisting of multiple full-duplex (FD) base stations (BSs) serving a large number of half-duplex (HD) user equipments (UEs) located in a wide geographical area. Each BS consists of a baseband unit (BBU) that is responsible for signal processing and BS control, and a radio remote unit (RRU) that corresponds to a radio transceiver remotely built closer to the UEs. We consider a wireless-powered cellular network in which the BBU can periodically charge the RRU. We model the energy efficiency and coverage optimization problem for this network as a mean field game. We consider the weighted energy efficiency of the BS as the main performance metrics and evaluate the optimal strategy that can be adopted by the FD BSs in an ultra-dense network setting. Based on the interference and network energy efficiency models of the mean field game theory, Nash equilibrium of our proposed game is derived. Utilizing solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, a new power transmission strategy is developed for the BSs to optimize the energy efficiency of 5G cellular networks with full duplex transmissions. Simulation results indicate that the proposed strategy not only improves the energy efficiency but also ensures the average network coverage probability converges to a stable level.

Performance evaluation of DTN protocols to deliver sms in dense mobile network : Empirical proofs

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.

The fifth-generation (5G), now being developed for use in the millimeter-wave (mm-wave) frequency bands, would enhance the Quality of Service (QoS) for the Mobile User (MU) in the near future. Despite the advantages of using mm-wave communications that are attained through the large bandwidth available in this band, it suffers from high penetration loss and low diffraction, which causes very high attenuation of the signal in free space. The attenuation of the signal can also be due to the blockages, such as buildings, human presence, vehicles, etc. As a result, the signal propagation path could be interrupted by those blockages causing significant fluctuation in the handover (HO) rate. Thus, the foreseen mm-wave communication gains are achieved at the expense of varying HO rates. Therefore, the impact of blockages on the HO rate for user mobility is a crucial performance factor that needs to be analyzed and addressed in mm-wave cellular communication systems. This paper analyses the impact of static blockage on the HO rate for a MU in the 5G mm-wave cellular network. We began by obtaining the impact of blockage with a fixed location in the 2-D plane on the HO rate for a MU moving radially away from the mm-wave base station (BS) at a certain speed. Afterward, we considered the MU moving in a uniform angle with a certain speed in the mm-wave cellular network. The mm-wave BSs, in this study, have been assumed to be distributed according to a homogeneous Poisson Point Process (PPP). The results show that the blockage has a remarkable impact on the HO rate, depending significantly on its location, and MU's direction and speed.

A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at the base station and backhauling network level. Intuitively, an adaptive placement of content and computing resources in the most crowded regions can grant important traffic offloading, improve network efficiency and user quality of experience. In this paper we document the content consumption in the Orange cellular network for the Paris metropolitan area, from spatial and application-level extensive analysis of real data from a few million users, reporting the experimental distributions. In this scope, we propose a hotspot cell estimator computed over user's mobility metrics and based on linear regression. Evaluating our estimator on real data, it appears as an excellent hotspot detection solution of cellular and backhauling network management. We show that its error strictly decreases with the cell load, and it is negligible for reasonable hotspot cell load upper thresholds. We also show that our hotspot estimator is quite scalable against mobility data volume and against time variations.