Intelligent cooperation management of multi-radio access technology towards the green cellular networks for the twenty-twenty information society

An unprecedented increase in subscribers and demand for high-speed data are considered a critical step towards the new era of mobile wireless networks, i.e., Fifth Generation (5G), where the legacy mobile communication system will still be operational for a long time in the future. This has subsequently increased the overall energy consumption, operational costs and carbon footprint of cellular networks, due to increase the number of base stations (BSs), which consume the most energy. Switching BSs off/on in accordance with the traffic pattern variations is considered an effective method for improving energy efficiency. However, the main concerns from the network operators are the requirements to switched on/off the BSs, coverage issues and secured the radio service for the affected area. Hence, the main focus of this study is to develop an intelligent cooperation management (switch BSs on/off) within a multi-radio access technology (RAT) environment between a future generation 5G into the existing LTE and UMTS cellular network towards green cellular networks, while guaranteeing maximum cells coverage area during a switch off session. Particle swarm optimisation has been adopted in this study to maximize the cell coverage area under the constraints of the transmission power of the BS \((P_{tx})\), the total antenna gain (G), the bandwidth (BW), the signal-to-interference-plus-noise ratio (SINR), and shadow fading \((\sigma )\). Moreover, the modulation and coding scheme, the data rate, and the energy efficiency are considered. The results have shown that by applying the proposed a dynamic multi-RAT BSs switching off\(\backslash \)on strategy according to the traffic load variations, the daily energy savings of up to 42.3% can be achieved, with guaranteed maximum cells coverage area.     

Downlink Radio Resource Allocation for Multi-Cell OFDMA System

This paper presents a radio resource control (RRC) scheme for OFDMA systems where dynamic resource allocation is realized at both a radio network controller (RNC) and base stations (BSs). The scheme is semi-distributed in the sense that the RRC decision is split between RNC and BSs. RNC makes decision on which channel is used by which BS at super-frame level and BSs then make decision on which user is assigned to which channel at frame-level. Two optimization problems for RNC and BSs are formulated and computationally efficient algorithms that perform the function of interference avoidance and traffic/channel adaptation are developed. Numerical analysis is performed under several cell configurations to show tradeoffs between sector interference suppression and dynamic interference avoidance. The results indicate that with reasonable signaling overhead, the protocol and the associated algorithms yield excellent performance for both real-time and non real-time services, even under fast fading     

Optimized joint cell planning and BS on/off switching for LTE networks

Energy is becoming a main concern nowadays due to the increasing demands on natural energy resources. Base stations (BSs) consume up to 80% of the total energy expenditure in a cellular network. In this paper, we propose and evaluate a green radio network planning approach by jointly optimizing the number of active BSs and the BS on/off switching patterns based on the changing traffic conditions in the network in an effort to reduce the total energy consumption of the BSs. The problem is formulated as an integer optimization problem, which proves to be NP‐complete, and thus it can be efficiently solved for small to medium network sizes. For large network sizes, we propose a heuristic solution with close to optimal performance because the optimal solution becomes computationally complex. Planning is performed based on two approaches: a reactive and a proactive approach. In the proactive approach, planning will be performed starting with the lowest traffic demand until reaching the highest traffic demand, whereas in the reactive approach, the reverse way is considered. Performance results are presented for various case studies and are complemented by testing the proposed approaches using commercial radio network planning tools. Results demonstrate considerable energy savings reaching up to 40% through dynamic adaptation of the number of simultaneously active BSs. Copyright © 2015 John Wiley & Sons, Ltd.     

Small‐cell planning in LTE HetNet to improve energy efficiency

Cell planning is one essential operation in wireless networks, and it significantly affects system performance and cost. Many research efforts consider the cell planning problem with identical base stations (BSs) or to construct a new network on the region without any infrastructure. However, long‐term evolution (LTE) adopts heterogeneous network, which allows operators to tactically deploy small cells to enhance signal coverage and improve performance. It thus motivates us to propose a small‐cell planning problem by adaptively adding low‐powered BSs with the limitation of budget to an existing network to increase its energy efficiency, which is defined by the ratio of network throughput to the amount of energy consumption of BSs. We consider 2 types of LTE small cells, namely, microcells and picocells, and develop different clustering strategies to deploy these cells. Based on the available resource and traffic demand in each cell, we then adjust the transmitted power of the deployed BS with energy concern. Experimental results demonstrate that our small‐cell planning solution can achieve high‐energy efficiency of LTE networks, which means that BSs can better use their transmitted energy to satisfy the traffic demands of user devices. This paper contributes in proposing a practical problem for cell planning with heterogeneous network consideration and developing an efficient solution to provide green communications.     

Energy‐efficient relay deployment in cellular systems using fractional frequency reuse and transmit antenna selection techniques

To cope with the steep surge in mobile traffic, network operators are now expanding their infrastructure for mobile access networks. However, since energy costs and greenhouse gas emissions are a large burden to network operators, they are making great efforts to improve the energy efficiency (EE) of their networks by promising techniques, including relaying and multiple‐input multiple‐output (MIMO). Relaying technique can considerably improve the EE when the interference between base stations (BSs) and relay stations (RSs) is effectively coordinated by an elaborate relay deployment using the fractional frequency reuse (FFR) technique. Also, MIMO techniques improve the radio channel quality through diversity gains, so the system capacity and the EE can be improved. Therefore, a combination of relaying/MIMO techniques can be considered to achieve the higher performance, particularly, if the system complexity and costs of using such techniques are low. Since the transmit antenna selection (TAS), one of MIMO techniques, can provide a sufficient transmit diversity gain and satisfy the requirements for low complexity and cost (thus, practical), the TAS is recommended for use with the relaying technique. In this paper, for an energy‐efficient relay deployment using FFR and TAS techniques, we derive the EE and the resource partitioning according to RS positions and the number of BS antennas by a mathematical analysis. Then, the optimal RS positions according to the number of antennas can be determined by an optimization approach. The numerical and simulation results indicate that the proposed analysis method can efficiently analyze the EE and locate the RSs.     

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.     

Energy analysis using semi-Markov modeling for the base station in 5G networks

With the increasing prevalence of wireless sensor networks, the uninterrupted operation of these networks becomes essential. To ensure continuous functionality, wireless networks rely on available base stations (BSs). However, the persistent operation of BSs comes at the cost of substantial energy consumption. Consequently, given the surge in wireless network traffic and the necessity for uninterrupted BS availability, energy efficiency within the BS becomes a concerning issue. To address this, the study employs a semi-Markov model to depict the availability of the BS, with states corresponding to the failures of its components (baseband unit, remote unit, and software module). The analysis yields a steady-state solution, with reward rates assigned to each state based on the energy consumption of individual BS components. This approach enables the determination of the expected energy consumption within this model. Additionally, the BS's throughput is assessed using an M/G/1 queueing model with server breakdown.     

Wi-Fi offloading between LTE and WLAN with combined UE and BS information

With the increasing popularity of mobile devices, cellular networks are suffering the consequences of a mobile data traffic explosion. Operators and standard development organizations have adopted Heterogeneous Network (HetNet) solutions which offload traffic from cellular networks to other Radio Access Technologies to reduce the overloaded situation. To minimize service blocking ratios, HetNets need to be designed with an efficient approach to allocate heterogeneous channel resources. This work proposes a Combined UE and BS Information scheme (CUBI), which follows the standard architecture proposed by 3rd generation partnership project. We compare the impacts of using the User Equipment (UE) information only, and Base Stations (BS) information only to achieve network selection. We then propose a 2-round solution that benefits from both UE and BS information. The UE-information scheme is based on channel qualities of all available networks, and using it selects the network which provides the highest data rate. On the other hand, the BS-information scheme (BSI) is based on traffic loads of all UEs to allocate bandwidth, and using BSI will prevent BSs from overload situations. The simulation results demonstrate that CUBI can decrease service blocking ratios by 26 and 8.9 %, compared to schemes separately using US and BS information, respectively.     

Self Organized Network Management Functions for Energy Efficient Cellular Urban Infrastructures

Energy efficiency is a significant requirement for the design and management of mobile networks and has recently gained substantial attention from both network operators and the research community. The general concept of energy saving management aims to match the capacity offered by operators to the actual demand at given times and geographic areas. This paper introduces the notion of energy partition, an association of powered-on and powered-off BSs to deliver network-level energy saving. It then elaborates how such concept is applied to perform energy re-configuration to flexibly re-act to load variations encouraging none or minimal extra energy consumption. A simulation-based study evaluates the performance of the proposed algorithms under different network topologies and traffic conditions, highlights the benefits and drawbacks, and provides recommendations for deployment scenarios.     

Green cell association for multimedia transmission in cognitive heterogeneous networks

With the introduction of low‐powered pico/femto‐base stations and relay nodes into the macro‐cell, recent heterogeneous networks provide an attractive approach for future wireless communication. Although it may achieve better coverage and higher capacity, several problems remain unsolved before practical deployment. For example, how to select the proper cell from neighbor low‐powered cells and then occupy the radio resource without interference on macro‐users is both important and challenging, especially for rigorous multimedia applications. The traditional cell access algorithms and quality‐control parameters such as delay or throughput no longer suit well in this complex environment. An effective approach should be pursued. In this paper, we investigate this interesting cell association problem and propose a complete green resolution on the basis of thorough discussions about the multimedia transmission under these concerns. Cognitive radio is introduced to share spectrum between macro‐cell and low‐powered cells while securing the transmission of authorized macro‐users. We also bring forth the concept of ‘interference balance’ to better manage the overall interference and energy consumption in the network. Restless bandit model is formulated on the basis of channel state, data rate, interference control, and the carefully chosen intra‐refreshing rate for multimedia traffic. Then the cell association scheme is designed to be efficient and practical because of the simple index property of our model output. Simulation results have proven the performance of our proposed resolution compared with existing algorithms on interference constraint, multimedia distortion, and overall network energy consumption balance. Copyright © 2013 John Wiley & Sons, Ltd.     

A new handoff management system for heterogeneous wireless access networks

In heterogeneous wireless access networks, each mobile terminal may frequently need to change its base station (BS); this change is called a ‘handoff’. BSs have static parameters, which are related to their radio access technologies (RATs); however, they also contain dynamic parameters such as load and signal quality. Therefore, the problem of handoff decision includes two subproblems of RAT selection and BS selection. In this paper, first a ‘heterogeneous handoff management system’ for gathering all different required parameters is proposed. Second, a RAT Selection algorithm based on analytic hierarchy process and a BS Selection algorithm based on data envelopment analysis are designed. Finally, by means of ‘weight restriction’ technique, we develop a method for studying the impact of RAT Selection parameters on the performance of the network. Simulation results indicate that RAT Selection parameters have significant impact on the bandwidth utilization, energy consumption and the whole satisfaction of the users in heterogeneous wireless access networks.Copyright © 2012 John Wiley & Sons, Ltd.     

Energy-efficient relay selection and optimal relay location in cooperative cellular networks with asymmetric traffic

Energy-efficient communication is an important requirement for mobile relay networks due to the limited battery power of user terminals. This paper considers energy-efficient relaying schemes through selection of mobile relays in cooperative cellular systems with asymmetric traffic. The total energy consumption per information bit of the battery-powered terminals, i. e. , the mobile station(MS)and the relay, is derived in theory. In the joint uplink and downlink relay selection(JUDRS)scheme we proposed, the relay which minimizes the total energy consumption is selected. Additionally, the energy-efficient cooperation regions are investigated, and the optimal relay location is found for cooperative cellular systems with asymmetric traffic. The results reveal that the MS-relay and the relay-base station(BS)channels have different influence over relay selection decisions for optimal energy-efficiency. Information theoretic analysis of the diversity-multiplexing tradeoff(DMT)demonstrates that the proposed scheme achieves full spatial diversity in the quantity of cooperating terminals in this network. Finally, numerical results further confirm a significant energy efficiency gain of the proposed algorithm comparing to the previous best worse channel selection and best harmonic mean selection algorithms.     

Relay Node Deployment Strategies in Heterogeneous Wireless Sensor Networks

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.     

A network-assisted flow mobility architecture for optimized mobile medical multimedia transmission

A large part of mobile Health (mHealth) use-cases such as remote patient monitoring/diagnosis, teleconsultation, and guided surgical intervention requires advanced and reliable mobile communication solutions to provide efficient multimedia transmission with strict medical level Quality of Service (QoS) and Quality of Experience (QoE) provision. The increasing deployment of overlapping wireless access networks enables the possibility to offer the required network resources for ubiquitous and pervasive mHealth services. To address the challenges and support the above use-cases in today’s heterogeneous network (HetNet) environments, we propose a network-assisted flow-based mobility management architecture for optimized real-time mobile medical multimedia communication. The proposed system is empirically evaluated in a Pan-European HetNet testbed with multi-access Android-based mobile devices. We observed that the proposed scheme significantly improves the objective QoE of simultaneous real-time high-resolution electrocardiography and high-definition ultrasound transmissions while also enhances traffic load balancing capabilities of wireless architectures.     

Energy Cooperation with Sleep Mechanism in Renewable Energy Assisted Cellular HetNets

The emerging fifth generation (5G) and beyond radio access networks are expected to be extremely dense and heterogeneous as compared to the current networks, involving a large number of different classes of base stations (BSs), namely macro, micro, femto and pico BSs. Among several performance requirements 5G and beyond systems aim to achieve, energy efficiency is one of the crucial requirements. In order to achieve energy-efficient design in dense heterogeneous 5G networks, various approaches in terms of resource allocation, off-loading techniques, hardware solutions and energy harvesting are being considered. In this regard, this paper develops an energy usage optimization framework in a cellular heterogeneous network (HetNet) consisting of a central macro-BS and a number of micro-BSs, equipped with renewable energy sources (RESs) such as solar panels and wind turbines. The proposed framework incorporates an energy cooperation mechanism along with a sleep mechanism (BS ON/OFF switching), in which the BSs having lean traffic are put into a sleep mode and their traffic load gets shared by the central BS. The surplus harvested energy from RESs of the sleeping BSs can then be sold back to the grid. An optimization problem for maximizing the utilization of RES and minimizing the usage of the traditional sources, such as utility and generator, is formulated and this mixed integer non-linear programming problem is solved through an interior point method. The presented results for various HetNet sizes demonstrate the significant savings in the energy cost with the proposed RES-enabled HetNet sleep mechanism model over the conventional approaches.

     

Cooperation-enabled energy efficient base station management for dense small cell networks

Dense small cell networks are deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, network densification will significantly increase the energy budget and lead to energy inefficiency due to the constant operation of network hardware. In this paper, we consider cooperation-enabled dynamic base station (BS) management for downlink dense small cell networks. By introducing two traffic-aware sleep modes, i.e., deep sleep mode and opportunistic sleep mode which are operating in different time and energy consumption scales, the network hardwares are turned to be the resources that can be occupied and released dynamically. Small cell BSs (SBSs) with zero or low load are completely switched off and reside in deep sleep mode during a predefined time interval. At each time slot, SBS dynamically turn some antennas and associated physical components into opportunistic sleep mode according to the short term traffic distribution, and the users are jointly served by the remaining antennas via cooperative transmission. The corresponding sleep mode decision making are presented respectively to find the optimal number of SBS and antennas that should be switched off. Numerical results are then presented to illustrate the superior performance in terms of energy efficiency gain. In summary, the proposed cooperation-aided sleep strategies for dense small cell networks take both traffic features and optimal cooperative transmission into account, and can achieve great energy saving while maintaining required quality of service.     

AI Based Network and Radio Resource Management in 5G HetNets

The demand for pervasive wireless access and high data rate services are expected to grow significantly in the near future. In this context, the deployment of Heterogeneous Networks (HetNets) will enable important capabilities, such as high data rates and traffic offloading, providing dedicated capacity to homes, enterprises, and urban hotspots. Despite HetNet technology will be beneficial for future wireless systems in many ways, the massive cells diffusion has as a consequence an exponential increase of the backhaul traffic that can create congestion and collapse the backhaul network. Virtualization of networks and radio access allows the implementation of complex and efficient decisional processes for radio and network resource optimization, but the interaction between lower and upper layers during resource allocation decisions is still mostly unexplored. In this paper we propose an artificial intelligence based approach, with two interdependent decisional cores exchanging information, one aware of physical layer aspects and the other controlling pure network resources. The two iterative procedures aim at jointly optimizing the distribution of the traffic in the backhaul network and the users cell association, with the goals of minimizing the unsatisfied users data rate requests and minimizing the energy consumption reducing the number of activated cells, respectively.     

A Study of Densification Management Using Energy Efficient Femto-Cloud Based 5G Mobile Network

Energy efficiency in wireless communication becomes essential. Power optimization of mobile radio systems has gained attention of network operators because energy costs make up a huge part of operational expenditure. In this regard, deployments of low power small cell base stations considerably raise the challenge of energy-efficient cellular networks. Network densification refers to densification over space, for example dense small cell deployment like picocell, femtocell, and frequency utilization of larger segments of radio spectrum in dissimilar bands. In this article we have illustrated the cause factors of densification and described its effects. The deployment layouts of different base stations are studied and compared with conventional macro-femtocell systems from the perspective of area power consumption and signal-to-interference-plus-noise-ratio.