In underlaid device-to-device (D2D) cellular networks, severe radio interference can be typical for cellular and D2D users, which causes each D2D user's quality-of-experience (QoE) to degrade significantly. Thus, in this paper, a dynamic Stackelberg game is formulated with a single-leader (base station) and multiple-followers (D2D pairs). The leader reduces interference within the network by charging a price to followers, whereas followers react to this price and compete to find optimal transmit power and resource block allocation. To enhance D2D user QoE, D2D users are categorized into one of three application classes with each class mapped to a different utility function. To address this, we propose a crucial innovation where the several different utility functions, available to followers, are solved using a non-scalarized approach, as a scalarized approach to this multi-criteria optimization problem is generally infeasible in real-time D2D cellular communications. To solve the game, a distributed algorithm is proposed, and it is shown to converge to a sub-game perfect Stackelberg equilibrium across all users. Simulation results show that the proposed approach reduces transmit power effectively and increases throughput, giving a Pareto-efficient outcome, while guaranteeing best social welfare and satisfaction across all D2D users.

In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and gather correlated data, and, thus, clustering MTDs based on data correlation leads to a decrease in the number of redundant bits transmitted to the base station. The clustering problem is formulated as an evolutionary game, which models the interactions among a massive number of MTDs, in order to decrease MTD transmission power. A novel utility function that captures the tradeoff between minimizing the average MTD transmission power per cluster and maximizing cluster size (or minimizing signaling overhead) is proposed. To solve this game, a distributed algorithm is proposed to allow a massive number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS) that is robust to a small portion of MTDs deviating, e.g., due to some stochastic changes in the M2M environment from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation, is derived. Simulation results show the efficiency of the proposed algorithm in clustering MTDs with highly correlated data: on average, the proposed approach yields reductions of up to 44.1% and 15.25% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively.

In this paper, the problem of correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In such dense networks, MTDs sense an environment and transmit their data to the base station (BS) via a cellular uplink. However, since MTDs are typically closely located to each other they will gather correlated data, and, thus, large amounts of redundant bits can be transmitted to the BS. To address this problem, an evolutionary coalitional (EC) game is proposed to cluster MTDs into coalitions in a fully distributed and autonomous manner, based on the correlation of their data. The proposed EC game allows a reduction in the number of redundant bits being sent to the BS, while also reducing the energy used for transmission by each MTD. To solve the EC game, a distributed coalition formation algorithm is proposed and shown to reach an evolutionary stable coalition structure, which is robust to a small portion of MTDs changing their strategy at the stable outcome. For this game, the maximum portion of MTDs that can deviate from the stable coalitional structure is derived. Simulation results show that the proposed approach can effectively cluster MTDs with highly correlated data which, in turn, enables those MTDs to eliminate a large number of redundant bits. Moreover, the results show that, for a given maximum correlation factor and network density, the transmission energy per MTD can be decreased by 19%, compared to a baseline merge-and-split algorithm. In addition, when a maximum correlation factor is considered, the number of redundant bits that can be eliminated per coalition is increased by 50%, compared to the merge-and-split algorithm.

Device-to-Device (D2D) communications is a key aspect of future 5G cellular networks, as it can help improve spectral efficiency, system capacity, resource utilization, and quality-of-service (QoS), for the overall network. In this paper, a dynamic cross-layer leave-and-join based coalition formation game with non-transferable utility (NTU) is proposed. Our proposed game jointly optimizes mode selection (at the network layer), and resource allocation from the cell and cellular user/s, including power control (at the physical layer), in a distributed wireless network with D2D communications. Moreover, we aim to maximize channel rate and minimize transmit power for both cellular users and D2D pairs, while selecting optimal transmission mode for all D2D pairs. The coalition partition of the proposed game converges to a Nash stable outcome, which is socially efficient. The simulation results illustrate that once a Nash stable coalition partition is achieved, then optimal transmit power and channel rate are achieved for all D2D pairs and cellular users.

Device-to-Device (D2D) communications underlaying cellular communications can increase resource utilization, system capacity, and interference, while increasing quality of service (QoS) for cellular and D2D users. In this paper, we design a non-cooperative cross-layer repeated game, which jointly optimizes mode selection (i.e. either operating in cellular mode or reuse mode) at the network layer and power control at the physical (PHY) layer for a distributed wireless network. The proposed game aims to minimize transmit power, and select optimal transmission mode, while achieving acceptable packet delivery ratio (PDR). The effects on PDR with respect to communication reliability and optimum mode switching point over time are determined. The game is constituted of a non-cooperative power control game and a two-armed bandit game, and by combining these two games we obtain rapid convergence to a Pareto-optimal outcome for both mode selection and transmit power.