This article investigates the mobility management of an ultra dense cellular network (UDN) from an energy-efficiency (EE) point of view. Many dormant base stations (BSs) in a UDN do not transmit signals, and thus a received power based handover (HO) approach as in traditional cellular networks is hardly applicable. In addition, the limited front/backhaul capacity compared to a huge number of BSs makes it difficult to implement a centralized HO and power control. For these reasons, a novel user-centric association rule is proposed, which jointly optimizes HO and power control for maximizing EE. The proposed mobility management is able to cope not only with the spatial randomness of user movement but also with temporally correlated wireless channels. The proposed approach is implemented over a HO time window and tractable power control policy by exploiting mean-field game (MFG) and stochastic geometry (SG). Compared to a baseline with a fixed HO interval and transmit power, the proposed approach achieves the 1.2 times higher long-term average EE at a typical active BS.

This paper proposes a dual-connectivity (DC) profile allocation algorithm, in which a central macro base station (MBS) is underlaid with randomly scattered small base stations (SBSs), operating on different carrier frequencies. We introduce two dual-connectivity profiles and the differences among them. We utilize the characteristics of dual-connectivity profiles and their applying scenarios to reduce feasible combination set to consider. Algorithm analysis and numerical results verify that our proposed algorithm achieve the optimal algorithm's performance within 5% gap with quite low complexity up to 10 −6 times.

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One of the challenges facing the next-generation wireless networks is to cope with the expected demand for data. This calls for an efficient spectrum regulation that can enable mobile subscribers to support high quality of service (QoS) and mobile network operators (MNOs) to leverage their profit streams. In this paper, we present a new spectrum allocation policy in a monopoly situation. The problem is formulated as a Stackelberg game. We show that the conventional spectrum leasing contract may lead to the unprecedented scenario in which costs outweigh their revenues. On the other hand, our proposed spectrum leasing contract can not only maximize user welfare but also leverage MNO's profit streams. We show that our spectrum leasing contract can increase user welfare and MNO's profit up to 75% and 20%, respectively, relative to the conventional spectrum leasing contract. Thus, regulators must rewrite their spectrum allocation policy in order to maximize user welfare and leverage MNO's profit streams.

We study bidding and pricing competition between two spiteful mobile network operators (MNOs) with considering their existing spectrum holdings. Given asymmetric-valued spectrum blocks are auctioned off to them via a first-price sealedbid auction, we investigate the interactions between two spiteful MNOs and users as a three-stage dynamic game and characterize the dynamic game's equilibria. We show an asymmetric pricing structure and different market share between two spiteful MNOs. Perhaps counter-intuitively, our results show that the MNO who acquires the less-valued spectrum block always lowers his service price despite providing double-speed LTE service to users. We also show that the MNO who acquires the high-valued spectrum block, despite charing a higher price, still achieves more market share than the other MNO. We further show that the competition between two MNOs leads to some loss of their revenues. By investigating a cross-over point at which the MNOs' profits are switched, it serves as the benchmark of practical auction designs.

Reverse pricing has been recognized as an effective tool to handle demand variability and uncertainty in the travel industry (e.g., airlines and hotels). To investigate its viability in mobile communication services, as a benchmark case, we first consider that a single mobile network operator (MNO) adopts (MNO-driven) forward pricing only, taking into account heterogeneous and stochastic user demands. To effectively deal with the drawbacks of forward pricing only, we propose (user-driven) two-dimensional reverse pricing on top of forward pricing and design a ξ-approximate polynomial-time algorithm that can maximize the revenue of the MNO. Through analytical and numerical results, we show that the proposed scheme can achieve "triple-win" solutions: Higher average network capacity utilization, the increase in the average revenue of the MNO, and the increment in the total average payoff of the users. To verify its feasibility in practice, we further implement its real prototype and perform experimental studies. We show that the proposed scheme still creates triple-win solutions in practice. Our findings provide a new outlook on resource allocation, and design guidelines for adopting two-dimensional reverse pricing on top of forward pricing.

In this letter, we focus on the performance of a worst-case mobile user (MU) in the downlink cellular network. We derive the coverage probability and the spectral efficiency of the worst-case MU using stochastic geometry. Through analytical and numerical results, we draw out interesting insights that the coverage probability and the spectral efficiency of the worst-case MU decrease down to 23% and 19% of those of a typical MU, respectively. By applying a coordinated scheduling (CS) scheme, we also investigate how much the performance of the worst-case MU is improved.