Yitao Xu

Load-Aware Dynamic Spectrum Access for Small-Cell Networks: A Graphical Game Approach

By employing the advances in wireless cloud technologies, this letter establishes a centralized-distributed optimization architecture for spectrum access in small cell networks. In the centralized aspect, the spectrum access problem is solved in the cloud. In the distributed aspect, the problem is solved by a game-theoretic solution, aiming to address the challenges of heavy computation complexity caused by the densely deployed small cells. The properties of the game, including the existence of pure Nash equilibrium and the achievable lower bound, are rigorously proved and analyzed. Furthermore, a cloud-assisted best response learning algorithm with low computational complexity is proposed, which converges to the global or local optima of the aggregate interference level of the small cell network. It is shown that the cloud-assisted algorithm converges rapidly and is scalable when increasing the number of small cells. Finally, simulation results are presented to validate the proposed cloud-based centralized-distributed solution.In this paper, we investigate the problem of dynamic spectrum access for small-cell networks using a graphical game approach. Compared with existing studies, we take the features of different cell loads and local interference relationship into account. It is proven that the formulated spectrum access game is an exact potential game (EPG) with the aggregate interference level as the potential function, and the Nash equilibrium (NE) of the game corresponds to the global or local optima of the original optimization problem. A lower bound of the achievable aggregate interference level is rigorously derived. Finally, we propose an autonomous best response (BR) learning algorithm to converge toward its NE. It is shown that the proposed game-theoretic solution converges rapidly, and its achievable performance is close to the optimum solution.

Database-Assisted Spectrum Access in Dynamic Networks: A Distributed Learning Solution

This paper investigates the problem of database-assisted spectrum access in dynamic TV white spectrum networks, in which the active user set is varying. Since there is no central controller and information exchange, it encounters dynamic and incomplete information constraints. To solve this challenge, we formulate a state-based spectrum access game and a robust spectrum access game. It is proved that the two games are ordinal potential games with the (expected) aggregate weighted interference serving as the potential functions. A distributed learning algorithm is proposed to achieve the pure strategy Nash equilibrium (NE) of the games. It is shown that the best NE is almost the same with the optimal solution and the achievable throughput of the proposed learning algorithm is very close to the optimal one, which validates the effectiveness of the proposed game-theoretic solution.

Energy‐efficient exploration and exploitation of multichannel diversity in spectrum sharing systems

This letter investigates the problem of energy-efficient exploration and exploitation of multichannel diversity in spectrum sharing cognitive radio systems where the secondary user sequentially explores the channel state information on the licenced channels with time and energy consumptions. As the number of the explored channels increases, the achieved multichannel diversity gain increases and so does the exploration consumption. Thus, there is a fundamental tradeoff between the multichannel diversity gain and channel exploration overhead. To maximise the expected normalised capacity of the secondary user, we formulate this tradeoff as an optimal stopping problem and propose a myopic one-stage look-ahead rule to solve it. It is shown that the one-stage look-ahead rule is optimal in the low power region; moreover, it also has good performance in general power region. Simulation results show that the achievable normalised throughput differs greatly for different exploration overhead, which can be regarded as a distinct feature of spectrum sharing systems. Copyright

Self-organising multiuser matching in cellular networks: a score-based mutually beneficial approach

In this study, the authors study the self-organising user assignment problem for the multi-user cooperation network. In the cellular network with user cooperation, idle users near the base station are seen as potential relay nodes, which can assist cell edge users to transmit data. Practically, the selfish nature of users is considered in the authors’ model. They design a score-based system, where the strategies of relay assignment are affected by the historical behaviours of users. That means not only the transmission performance, but also the accumulative contributions of users are considered. The proposed score-based system sufficiently encourages idle users to assist active users. Furthermore, the multi-user assignment problem is formulated as a one-to-one matching game, in which idle users and active users rank one another individually based on their own preference. To address the issue, they propose a self-organising mutually beneficial matching algorithm, which is proven to converge to a stable matching. Simulation results show that the proposed distributed algorithm yields well matching performance between source users and relay users, which is close to the optimal centralised results.

Distributed satisfaction-aware relay assignment: a novel matching-game approach

Based on matching game theory, this paper puts forward a novel-distributed relay-assignment approach to optimise the throughput satisfaction of source nodes, which is scarcely investigated in former works. The assignment problem is modelled as a matching market, where source nodes apply to relay nodes according to their throughput requirements, in the meantime, relay nodes obtain pay offs by assisting source nodes to transmit data. The proposed game appears in the category of matching games with both peer effects and dynamic quotas. The choice of each source node is related to not only the quality of channel gain but also the results of other source nodes choices. Dynamic quotas of matching game are considered because of the limited resource of relay nodes and dynamic requirements of source nodes, which adds difficulty of relay assignment. To address this issue, a novel self-organising matching approach is proposed. Then, the proposed algorithm is proved to converge to a two-sided stable matching between source nodes and relay nodes. Shown in the simulation results, the proposed distributed matching algorithm outperforms the existing schemes in terms of both global satisfaction and fairness within a reasonable convergence time. Copyright