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In this paper, we investigate the resource allocation for dense cloud non-orthogonal multiple access smallcell networks (NOMA SCN), aimed at maximizing the users' quality of service (QoE). First, we construct a directed hypergraph to model the complex inter-interference relationship for cloud NOMA SCN. Then, we formulate the QoE-oriented channel allocation and user pairing problem in NOMA SCN as a local cooperation game. The game is proved to be an exact potential game. Moreover, the optimal pure strategy Nash equilibrium (PNE) in the proposed game can maximize the network QoE level. To achieve the optimal PNE in the proposed game, we redesign a directed-hypergraph-based multi-agent learning algorithm, which allows multiple non-coupled agents in directed hypergraph to simultaneously update their actions. Finally, simulation results are presented to validate the proposed learning scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of traffic offloading in a two-tier downlink smallcell network with hybrid access. We formulate a Stackelberg game with pre-offloading decision, which includes two steps, to study the problem. The utility of the MBS is designed as a piecewise function, corresponding to the two possible decisions of the MBS. We design a bonus-based incentive mechanism to motivate smallcell base stations (SBSs) serve the MUEs. We prove the existence and uniqueness of the Stackelberg equilibrium. We also consider the incomplete information game, where the SBSs have private information. A Bayesian Stackelberg game with pre-offloading decision is formulated and the existence of Bayesian Stackelberg equilibrium is proved. Our proposed approach achieves much higher utilities than the existing approach does under some circumstances in both cases, which proves the necessity and superiority of our proposed game structure. [ABSTRACT FROM AUTHOR]

This paper investigates the channel selection problem for anti-jamming defense in an adversarial environment. In our work, we simultaneously consider malicious jamming and co-channel interference among users, and formulate this anti-jamming defense problem as a Stackelberg game with one leader and multiple followers. Specifically, the users and jammer independently and selfishly select their respective optimal strategies and obtain the optimal channels based on their own utilities. To derive the Stackelberg Equilibrium, a hierarchical learning framework is formulated, and a hierarchical learning algorithm (HLA) is proposed. In addition, the convergence performance of the proposed HLA algorithm is analyzed. Finally, we present simulation results to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The sphere decoding (SD) algorithm can provide (sub)optimal solutions with reduced computational complexity of maximum likelihood (ML) detection for multi-input multi-output (MIMO) communication systems. In this paper, we propose a novel low complexity symmetric-coded based SD algorithm for short polar codes with low rate. At the encoding stage, the first N/2 sub-channels transmit the frozen bits, while the information bits are selected from the latter N/2 sub-channels. Two symmetric codes are generated due to the mathematical structure of the generator matrix, which is well conditioned to the SD search. At the decoding stage, the presented SD algorithm computes the Euclidean distance value by the combined signals to estimate the latter N/2 input bits. Furthermore, the backtrack operation starts from the earlier (N / 2 + 1) -th bit, which can significantly reduce the average visited nodes (AVN). Simulation results show that, compared to the original SD algorithm, the presented variant of the SD algorithm can reduce the AVN to 0.9 % for the polar code P(64, 14) at SNR = 1 dB with a performance loss within 0.2 dB. The presented SD algorithm may find applications in MIMO systems where the complexity of the standar ML detection increases exponentially with the transmitting antennas. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the channel access problem considering switching cost in the block fading channels with unknown information of channel occupation and quality. We formulate this problem as a multi-armed bandit (MAB) problem with the goal of minimizing the outage rate and avoiding frequent channel switching. To achieve this goal, a block based multi-armed bandit (BMAB) learning algorithm is proposed. Furthermore, the BMAB algorithm is extended to cope with the short-term deep channel fading, by exploiting the repetition time diversity (RTD). The regrets of the proposed two algorithms are proved to be logarithmic in time. Performance analysis and simulation results show that the proposed algorithms outperform standard SMAB algorithm in average system outage rate, switching cost and throughput. In addition, the repetition time diversity multi-armed bandit (RTDMAB) algorithm is better than BMAB algorithm in the presence of deep channel fading at the cost of receiving complexity. [ABSTRACT FROM AUTHOR]