Inter-operator infrastructure sharing:trade-offs and market

Abstract We model the problem of infrastructure sharing among mobile network operators (MNOs) as a multiple-seller single-buyer market where the MNOs are able to share their own base stations (BSs) with each other. First, we use techniques from stochastic geometry to find the coverage probability of the infrastructure sharing system and analyze the trade-off between increasing the transmit power of a BS and the BS intensity of a buyer MNO required to achieve a given quality-of-service (QoS) in terms of the coverage probability. We show that when the transmit power of the BSs and/or the BS intensity of a network increases, the system becomes interference limited and the coverage probability tends to saturate at a certain value. As such, when the required QoS is set above this bound, an MNO can improve its coverage by buying infrastructure from other MNOs. Subsequently, we analyze the strategy of a buyer MNO on choosing how many MNOs and which MNOs to buy the infrastructure from. The optimal strategy of the buyer is given by greedy fractional knapsack algorithm. On the sellers’ side, the pricing problem and the problem of determining the fraction of infrastructure to be sold are formulated using a Cournot oligopoly game

Edge caching for cache intensity under probabilistic delay constraint

Abstract In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of “cache intensity” under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically

Multi-operator spectrum sharing for small cell network:a matching game perspective

Abstract One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other’s spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system

Infrastructure sharing for mobile network operators:analysis of trade-offs and market

Abstract The conflicting problems of growing mobile service demand and underutilization of dedicated spectrum has given rise to a paradigm where mobile network operators (MNOs) share their infrastructure among themselves in order to lower their operational costs, while at the same time increase the usage of their existing network resources. We model and analyze such an infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. Assuming that the locations of the BSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability for a user served by the buyer MNO in an infrastructure sharing environment. We analyze the trade-off between increasing the transmit power of a base station (BS) and the intensity of BSs owned by the buyer MNO required to achieve a given quality-of-service (QoS) in terms of the SINR coverage probability. Also, for a seller MNO, we analyze the power consumption of the network per unit area (i.e., areal power consumption) which is shown to be a piecewise continuous function of BS intensity, composed of a linear and a convex function. Accordingly, the BS intensity of the seller MNO can be optimized to minimize the areal power consumption while achieving a minimum QoS for the buyer MNO. We then use these results to formulate a single-buyer multiple-seller BS infrastructure market. The buyer MNO is concerned with finding which seller MNO to purchase from and what fraction of BSs to purchase. On the sellers’ side, the problem of pricing and determining the fraction of infrastructure to be sold is formulated as a Cournot oligopoly market. We prove that the iterative update of each seller’s best response always converges to the Nash Equilibrium

On spectrum sharing among micro-operators in 5G

Abstract The growing demand in indoor small cell networks has given rise to the concept of micro-operators (MOs) for local service delivery. We model and analyze a spectrum sharing system involving such MOs where a buyer MO buys multiple licensed subbands provided by the regulator. All small cell base stations (SBSs) owned by a buyer MO can utilize multiple licensed subbands. Once the buyer MO obtain subbands, it allows other MOs to share these subbands. A deterministic model in which the location of the SBSs are known can lead to unwieldy problem formulation, when the number of SBSs is large. As such, we adopt a stochastic geometric model of the SBS deployment instead of a deterministic model. Assuming that the locations of the SBSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability and average data rate for a typical user (UE) served by the buyer MO in a spectrum sharing environment. In order to satisfy the QoS constraint, we provide a greedy algorithm to find how many licensed subbands and which subband the buyer MO should purchase from the regulator. We also derive the coverage probability of the buyer MO for interference limited system