Energy-Efficient selective activation in Femtocell Networks

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution

EMB: Efficient Multimedia Broadcast in Multi-tier Mobile Networks

Multimedia broadcast and multicast services (MBMS) in mobile networks has been widely addressed, however an investigation of such a technology in emerging, multi-tier, scenarios is still lacking. Notably, user clustering and resource allocation are extremely challenging in multi-tier networks, and imperative to maximize system capacity and improve quality of user-experience (QoE) in MBMS. Thus, in this paper we propose a clustering and resource allocation approach, named EMB, which specifically addresses heterogeneous networks and accounts for the fact that multimedia content is adaptively encoded into scalable layers depending on the QoE requirements and channel conditions of the heterogeneous users. Importantly, we prove that our clustering algorithm yields Pareto efficient broadcasting areas, multimedia encoding parameters, and re- source allocation, in a way that is also fair to the users. Fur- thermore, numerical results obtained under realistic conditions and using real-world video content, show that the proposed EMB results in lower churn count (i.e., higher number of served users), higher throughput, and increased QoE, while using fewer network resources

Interference management in wireless cellular networks

In wireless networks, there is an ever-increasing demand for higher system throughputs, along with growing expectation for all users to be available to multimedia and Internet services. This is especially difficult to maintain at the cell-edge. Therefore, a key challenge for future orthogonal frequency division multiple access (OFDMA)-based networks is inter-cell interference coordination (ICIC). With full frequency reuse, small inter-site distances (ISDs), and heterogeneous architectures, coping with co-channel interference (CCI) in such networks has become paramount. Further, the needs for more energy efficient, or “green,” technologies is growing. In this light, Uplink Interference Protection (ULIP), a technique to combat CCI via power reduction, is investigated. By reducing the transmit power on a subset of resource blocks (RBs), the uplink interference to neighbouring cells can be controlled. Utilisation of existing reference signals limits additional signalling. Furthermore, cell-edge performance can be significantly improved through a priority class scheduler, enhancing the throughput fairness of the system. Finally, analytic derivations reveal ULIP guarantees enhanced energy efficiency for all mobile stations (MSs), with the added benefit that overall system throughput gains are also achievable. Following this, a novel scheduler that enhances both network spectral and energy efficiency is proposed. In order to facilitate the application of Pareto optimal power control (POPC) in cellular networks, a simple feasibility condition based on path gains and signal-to-noise-plus- interference ratio (SINR) targets is derived. Power Control Scheduling (PCS) maximises the number of concurrently transmitting MSs and minimises their transmit powers. In addition, cell/link removal is extended to OFDMA operation. Subsequently, an SINR variation technique, Power SINR Scheduling (PSS), is employed in femto-cell networks where full bandwidth users prohibit orthogonal resource allocation. Extensive simulation results show substantial gains in system throughput and energy efficiency over conventional power control schemes. Finally, the evolution of future systems to heterogeneous networks (HetNets), and the consequently enhanced network management difficulties necessitate the need for a distributed and autonomous ICIC approach. Using a fuzzy logic system, locally available information is utilised to allocate time-frequency resources and transmit powers such that requested rates are satisfied. An empirical investigation indicates close-to-optimal system performance at significantly reduced complexity (and signalling). Additionally, base station (BS) reference signals are appropriated to provide autonomous cell association amongst multiple co-located BSs. Detailed analytical signal modelling of the femto-cell and macro/pico-cell layouts reveal high correlation to experimentally gathered statistics. Further, superior performance to benchmarks in terms of system throughput, energy efficiency, availability and fairness indicate enormous potential for future wireless networks

Addressing the 5G cell switch-off problem with a multi-objective cellular genetic algorithm

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The power consumption foreseen for 5G networks is expected to be substantially greater than that of 4G systems, mainly because of the ultra-dense deployments required to meet the upcoming traffic demands. This paper deals with a multi- objective formulation of the Cell Switch-Off (CSO) problem, a well-known and effective approach to save energy in such dense scenarios, which is addressed with an accurate, yet rather unknown multi-objective metaheuristic called MOCell (multi- objective cellular genetic algorithm). It has been evaluated over a different set of networks of increasing densification levels. The results have shown that MOCell is able to reach major energy savings when compared to a widely used multi-objective algorithm.TIN2016-75097-P Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Hybrid Access Control Mechanism in Two-Tier Femtocell Networks

The cellular industry is undergoing a major paradigm shift from voice-centric, structured homogeneous networks to a more data-driven, distributed and heterogeneous architecture. One of the more promising trends emerging from this cellular revolution is femtocells. Femtocells are primarily viewed as a cost-effective way to improve both capacity and indoor coverage, and they enable offloading data-traffic from macrocell network. However, efficient interference management in co-channel deployment of femtocells remains a challenge. Decentralized strategies such as femtocell access control have been identified as an effective means to mitigate cross-tier interference in two-tier networks. Femtocells can be configured to be either open access or closed access. Prior work on access control schemes show that, in the absence of any coordination between the two tiers in terms of power control and user scheduling, closed access is the preferred approach at high user densities. Present methods suggest that in the case of orthogonal multiple access schemes like TDMA/OFDMA, femtocell access control should be adaptive according to the estimated cellular user density. The approach we follow, in this work, is to adopt an open access policy at the femtocell access points with a cap on the maximum number of users allowed on a femtocell. This ensures the femto owner retains a significant portion of the femtocell resources. We design an iterative algorithm for hybrid access control for femtocells that integrates the problems of uplink power control and base station assignment. This algorithm implicitly adapts the femtocell access method to the current user density. The distributed power control algorithm, which is based on Yates' work on standard interference functions, enables users to overcome the interference in the system and satisfy their minimum QoS requirements. The optimal allocation of femtocell resources is incorporated into the access control algorithm through a constrained sum-rate maximization to protect the femto owner from starvation at high user densities. The performance of a two-tier OFDMA femtocell network is then evaluated under the proposed access scheme from a home owner viewpoint, and network operator perspective. System-level simulations show that the proposed access control method can provide a rate gain of nearly 52% for cellular users, compared to closed access, at high user densities and under moderate-to-dense deployment of femtocells. At the same time, the femto owner is prevented from going into outage and only experiences a negligible rate loss. The results obtained establish the quantitative performance advantage of using hybrid access at femtocells with power control at high user densities. The convergence properties of the proposed iterative hybrid access control algorithm are also investigated by varying the user density and the mean number of femto access points in the network. It is shown that for a given system model, the algorithm converges quickly within thirty iterations, provided a feasible solution exists

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments