Sriram Prasanth T

Bio: Sriram Prasanth T is an academic researcher from VIT University. The author has contributed to research in topics: Wireless network & Efficient energy use. The author has an hindex of 1, co-authored 1 publications receiving 17 citations.

A survey on techniques related to base station sleeping in Green communication and CoMP analysis

Abstract: Energy efficiency is an important factor in wireless networks which is facing a major concern mainly because of environmental, economic and quality of service considerations. Energy saving methods can be implemented with the adoption of renewable energy resources or by making hardware more energy efficient, but the major concern in these approaches are cost of purchasing, installing and transportation of equipment's can be an economical burden. So to improve energy efficiency various methods have been proposed and these methods can be referred as green cellular networking. These methods are far less costly, also implementations and testing becomes easy as it need not require any change in current network architecture. In this survey, we at first discuss about various facts and figure which highlights the necessity of green communication and we have discussed about various techniques which implements BS switching for green cellular networking. Generally based on the traffic pattern, some of the slightly loaded BS are switched off for reducing energy consumption. So at the end this review will discuss the analysis of coordinated multipoint transmission (CoMP) and its various parameters in BS switching.

Greener RAN Operation Through Machine Learning

Abstract: The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

Abstract: To meet the exponential increasing high data rate demand of mobile users, heterogeneous ultra-dense networks (UDN) is widely seen as an essential technology to provide high-rate transmissions to nearby mobile users. However, the dense and random deployment of small base stations (SBSs) overlaid by macro base stations and their uncoordinated operation lead to important questions about the power consumption and aggressive frequency reuse of heterogeneous UDN. For the problem of huge power consumption and spectrum resource tension in heterogeneous UDN, a joint strategy of SBSs sleep and spectrum allocation is proposed. By using stochastic geometry, the coverage probabilities of base stations and the average ergodic rates of mobile users are derived in each tier and the whole network. In addition, we formulate the coverage probability maximization and power consumption minimization problems, and determine the optimal operating regimes for SBSs, and as well as spectrum allocation. The numerical results show that the SBSs sleep and spectrum allocation can reduce the power consumption and interference of the whole network.

Caching at the edge in high energy-efficient wireless access networks

Abstract: In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.