Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Future mobile networks have to be densified by employing small cells to handle the upsurge in traffic load. Although the amount of energy each small cell consumes is low, the total energy consumption of a large-scale network may be enormous. To enhance energy efficiency, we have to adapt the number of active base stations to the offered traffic load. Deactivating base stations may cause coverage holes, degrade the quality of service and throughput while redundant base stations waste energy. That is why we have to adapt the network to an effective density. In this paper, we show that achieving an optimal solution for adapting the density of base stations to the demand is NP-hard. We propose a solution that consists of two heuristic algorithms: a base station density adaptation algorithm and a cell-zooming algorithm that determines which base stations must be kept active and adapts transmit power of base stations to enhance throughput, energy, and spectral efficiency. We employ a multi-access edge cloud for taking a snapshot of the network state in nearly real time with a wider perspective and for collecting network state over a large area. We show that the proposed algorithm conserves energy up to 12% while the spectral efficiency and network throughput can be enhanced up to 30% and 26% in comparison with recent works, respectively

Density-aware power allocation in mobile networks using edge computing

While the main concern in mobile networks was increasing network capacity and expanding coverage in the past, green operation have recently become a major concern. To conserve energy in mobile networks, we employ mobile edge computing to access real-time data and offload processing tasks to the edges for reducing complexity and latency. An edge cloud has a larger view of the network than a base station. Therefore, it can evaluate the probability of coverage by considering the density of base stations to enhance the overall system throughput while preventing coverage holes. This may enhance user experience and decrease response time in the network. We propose a density-adaptive power control algorithm that reduces energy consumption in an LTE network and improves the mean throughput. We show how power allocation and interference may affect system performance. The simulation results show that the proposed algorithm can reduce the energy wastage up to 67% in comparison with the basic model (without any power allocation model) and 28% in comparison with a recent work while the mean throughput can be improved by 22%

Evaluation of Terahertz Channel in Data Centers

Designing data center network topologies with the objective of minimizing cost, increasing bisection bandwidth and decreasing latency is a difficult problem. The solutions in the literature mainly concentrate on wired networks and minimizing wiring costs thereof. Only a few proposals address the benefit of employing wireless communications in data centers due to spectrum and bandwidth limitations of current wireless communication technologies. By using terahertz communication in a data center as a complementary technology, the performance of the data center can be enhanced and substantial savings in cabling costs can be achieved without any throughput concession. Terahertz (THz) band can overcome bandwidth limitations and satisfy a wide range of applications from classical networking to board-to-board communication. In this paper, we evaluate the terahertz channel in data centers by considering atmospheric absorption. Based on the results, we recommend employing 190310 GHz range with a bandwidth of 120 GHz. Keeping the relative humidity level at 40% will reduce atmospheric absorption while proving a healthy environmental regime for the equipment in a data center

Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model

Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning

Open radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processing pools of general-purpose processors that can be placed at different locations. Therefore, it is challenging to choose a proper location for executing network functions (NFs) over these entities by considering propagation delay and computational capacity. In this paper, we propose a Soft Actor–Critic Energy-Aware Dynamic DU Selection algorithm (SA2C-EADDUS) by integrating two nested actor–critic agents in the O-RAN architecture. In addition, we formulate an optimization model that minimizes delay and energy consumption. Then, we solve that problem with an MILP solver and use that solution as a lower bound comparison for our SA2C-EADDUS algorithm. Moreover, we compare that algorithm with recent works, including RL- and DRL-based resource allocation algorithms and a heuristic method. We show that by collaborating A2C agents in different layers and by dynamic relocation of NFs, based on service requirements, our schemes improve the energy efficiency by 50% with respect to other schemes. Moreover, we reduce the mean delay by a significant amount with our novel SA2C-EADDUS approach

Density-aware mobile networks: opportunities and challenges

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We experience a major paradigm shift in mobile networks. The infrastructure of cellular networks is becoming mobile since it is being densified also by using mobile and nomadic small cells to increase coverage and capacity. Furthermore, the innovative approaches such as green operation through sleep scheduling, user-controlled small cells, and dynamic end-to-end slicing will make the network topology and available resources highly dynamic. Therefore, the density of dynamic networks may vary in time and space from sparse to dense or vice versa. This paper advocates that on density-awareness is critical for dynamic mobile networks. Mobile cells, while bringing many benefits, introduce many unconventional challenges that we present in this paper. Novel techniques are needed for adapting network functions, communication protocols, and their parameters to the network density. Especially when cells on wheels or wings are considered, static and man-made configurations will waste valuable resources such as spectrum or energy if the density is not considered as an optimization parameter. In this paper, we evaluate the dynamicity of nomadic cells in density-aware mobile networks in a comprehensive and articulable way. The main challenges we may face by employing dynamic networks and how we can tackle these problems by using a density-oriented approach are discussed in detail. As a key concern in dynamic mobile networks, we treat the density of base stations, which is an indispensable performance parameter. For the applicability of such a parameter we present several potential density estimators. We epochally discuss the impact of density on coverage, interference, mobility management, scalability, capacity, caching, routing protocols, and energy consumption. Our findings illustrate that mobile cells bring more opportunities in addition to some challenges which can be solved, such as adapting mobile networks to base station density.This work was supported by TÜBİTAK, Project 215E127.Dr.Onur is partially supported by the Fulbright Academic Research ScholarshipPeer Reviewe