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Recently, the demand for small cell base stations (SBSs) has been exploding to accommodate the explosive increase in mobile data traffic. In ultra-dense small cell networks (UDSCNs), because the spatial and temporal traffic distributions are significantly disproportionate, the efficient management of the energy consumption of SBSs is crucial. Therefore, we herein propose a multi-agent distributed Q-learning algorithm that maximizes energy efficiency (EE) while minimizing the number of outage users. Through intensive simulations, we demonstrate that the proposed algorithm outperforms conventional algorithms in terms of EE and the number of outage users. Even though the proposed reinforcement learning algorithm has significantly lower computational complexity than the centralized approach, it is shown that it can converge to the optimal solution.

This paper presents a novel methodology for capturing the coupling between the different cells in both the uplink and downlink directions in a Wideband Code Division Multiple Access (WCDMA) scenario. It is based on the definition and computation of the gradient of the uplink cell load factor and the downlink transmitted power, which are the two main parameters that reflect the actual cell load in the two link directions. The paper shows that the gradient is able to capture the relevant information about the spatial distribution of traffic, which has an impact on cell performance. The proposed methodology is also used as the basis for defining and evaluating new Radio Resource Management (RRM) strategies that operate at a multi-cell level. [ABSTRACT FROM AUTHOR]

The mobility pattern of users is one of the distinct features of vehicular ad hoc networks (VANETs) compared with other types of mobile ad hoc networks (MANETs). This is due to the higher speed and the roadmap-restricted movement of vehicles. In this paper, we propose a new analytical mobility model for VANETs based on product-form queueing networks. In this model, we map the topology of the streets and the behavior of vehicles at both intersections and different parts of the streets onto different parameters of a BCMP¹ open queueing network comprising M/G/∞ nodes. This model represents a sparse situation for VANETs. To include the effect of dense situation on the mobility model, we modify the proposed queueing network as a new one comprising nodes with state-dependent service rates, i.e., M/G(n)/∞ nodes. With respect to the product-form solution property of the proposed queueing networks, we are able to find the spatial traffic distribution for vehicles at both sparse and dense situations. Furthermore, we are able to modify the proposed queueing network to find the lower and upper bounds for the probability of connectivity. In the last part of this paper, we show the flexibility of the proposed model by several numerical examples and confirm our modeling approach by simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general mobility model suitable for wireless multimedia networks. Our model is based on splitting a region into subregions. Furthermore, we make an analogy between subregions as well as their inter-connections with a multi-class Jackson queueing network comprising of infinite-server nodes. The main attribute of such a network is due to its product-form stationary distribution. Using this model, we are able to obtain a closed analytical form for the spatial traffic distribution corresponding to a specific number of network-connected users with different classes of service and mobility in a typical region. Also, we show the flexibility obtained by the proposed mobility model in representing some general distributions such as sum-of-hyper-exponentials (SOHYP), hyper-Erlang and Cox which were previously suggested to model mobility-related statistical parameters, e.g., cell dwell time and channel holding time. Finally, we apply the proposed model to a few mobility scenarios and obtain the resultant active user's location density. [ABSTRACT FROM AUTHOR]