Abstract
This paper proposes a learning policy to improve the energy efficiency (EE) of heterogeneous cellular networks. The combination of active and inactive base stations (BS) that allows for maximizing EE is identified as a combinatorial learning problem and requires high computational complexity as well as a large signaling overhead. This paper aims at presenting a learning policy that dynamically switches a BS to ON or OFF status in order to follow the traffic load variation during the day. The network traffic load is represented as a Markov decision process, and we propose a modified upper confidence bound algorithm based on restless Markov multi-armed bandit framework for the BS switching operation. Moreover, to cope with initial reward loss and to speed up the convergence of the learning algorithm, the transfer learning concept is adapted to our algorithm in order to benefit from the transferred knowledge observed in historical periods from the same region. Based on our previous work, a convergence theorem is provided for the proposed policy. Extensive simulations demonstrate that the proposed algorithms follow the traffic load variation during the day and contribute to a performance jump-start in EE improvement under various practical traffic load profiles. It also demonstrates that proposed schemes can significantly reduce the total energy consumption of cellular network, e.g., up to 70% potential energy savings based on a real traffic profile.
Highlights
The increasing popularity of portable smart devices has flared up rising traffic demand for radio access network and has been arousing massive energy consumption, which leads to the exhaustion of energy resources and causes a potential increase of CO2 emissions
We focus on a restless upper confidence bound policy which has been proven to be efficient for opportunistic spectrum access (OSA) problem [8,9,10], where selecting an arm leads to two different rewards associated with it
We consider an heterogeneous cellular network topology consisting of 5 macro and 5 micro base stations (BS) arbitrarily deployed in an area of 5 × 5 km2
Summary
The increasing popularity of portable smart devices has flared up rising traffic demand for radio access network and has been arousing massive energy consumption, which leads to the exhaustion of energy resources and causes a potential increase of CO2 emissions. Back-haul routers, and cellular access networks are the main source of energy consumption in the information and communication technology industry, which is equivalent of 2 to 10% of the global overall power consumption of human activity [2]. Due to the traffic load variation in time domain and dynamic distribution of cellular users among cells in space domain, there are opportunities for some BS to be put in sleep mode in order to achieve higher energy efficiency (EE). Instead of just turning off radio transceivers, the BS operators may prefer to turn off the underutilized BS and transfer the imposed traffic loads to neighbor active BS during low-traffic periods such as night time and/or weekend, which reduces the energy consumption [4]
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