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International audience—In this work, we use a stochastic geometric approach in order to study the impact on energy consumption when base stations are switched off independently of each other. We present here both the uplink and downlink analysis based on the assumption that base stations are distributed according to an independent stationary Poisson point process. This type of modeling allows us to make use of the property that the spatial distribution of the base stations after thinning (switching-off) is still a Poisson process. This implies that the probability distribution of the SINR can be kept unchanged when switching-off base stations provided that we scale up the transmission power of the remaining base stations. We then solve the problem of optimally selecting the switch-off probabilities so as to minimize the energy consumptions while keeping unchanged the SINR probability distribution. We then study the trade-off in the uplink performance involved in switching-off base stations. These include energy consumption, the coverage and capacity, and the impact on amount of radiation absorbed by the transmitting user

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We consider a common object (data) that each mobile in the medium is interested to receive, and which can be obtained from any base station transmitting the data. For example, the broadcast object could be streaming transmission of a sport or cultural event, or it could be some signaling such as a beacon for time synchronization or for power control purposes. This problem can be conceived as a coalition game played by mobiles which we call as association game of mobiles. This game has an incentive to form grand coalition where all players join to the game. We prove that using Bondareva-Shapley theorem, this coalition game has a non-empty core which means that grand coalition is stable. Then, we examine the cost allocation policy for different methods such as egalitarian allocation, proportional repartition of total cost, the Shapley value and the nucleolus. We also conclude that if the nucleolus is used as the cost allocation algorithm, the players maintain the grand coalition satisfying the minimization of total cost for broadcast transmission.

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RAN energy consumption is a major OPEX source for mobile telecom operators, and 5G is expected to increase these costs by several folds. Moreover, paradigm-shifting aspects of the 5G RAN architecture like RAN disaggregation, virtualization and cloudification introduce new traffic-dependent resource management decisions that make the problem of energy-efficient 5G RAN orchestration harder. To address such a challenge, we present a first comprehensive virtualized RAN (vRAN) system model aligned with 5G RAN specifications, which embeds realistic and dynamic models for computational load and energy consumption costs. We then formulate the vRAN energy consumption optimization as an integer quadratic programming problem, whose NP-hard nature leads us to develop GreenRAN, a novel, computationally efficient and distributed solution that leverages Lagrangian decomposition and simulated annealing. Evaluations with real-world mobile traffic data for a large metropolitan area are another novel aspect of this work, and show that our approach yields energy efficiency gains up to 25% and 42%, over state-of-the-art and baseline traditional RAN approaches, respectively.

Emergence of shared spectrum such as CBRS 3.5 GHz band promises to broaden the mobile operator ecosystem and lead to proliferation of small cell deployments. We consider the inter-operator interference problem that arises when multiple small cell networks access the shared spectrum. Towards this end, we take a novel communication-free approach that seeks implicit coordination between operators without explicit communication. The key idea is for each operator to sense the spectrum through its mobiles to be able to model the channel vacancy distribution and extrapolate it for the next epoch. We use reproducing kernel Hilbert space kernel embedding of channel vacancy and predict it by vector-valued regression. This predicted value is then relied on by each operator to perform independent but optimal channel assignment to its base stations taking traffic load into account. Via numerical results, we show that our approach, aided by the above channel vacancy forecasting, adapts the spectrum allocation over time as per the traffic demands and more crucially, yields as good as or better performance than a coordination based approach, even without accounting the overhead of the latter.