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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 the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot's onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot's navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information. [ABSTRACT FROM AUTHOR]

In this paper, we investigate channel allocation for ultradense device-to-device (D2D) communications. Different from both binary graph and undirected hypergraph interference model, an improved directed hypergraph is applied to simultaneously represent cumulative and asymmetric interference aspects in the context of ultradense communications. We formulate the channel access problem in cloud D2D communication networks as a directed-hypergraph-based local altruistic game, which is proved to be an exact potential game. Then, a multiagent concurrent learning scheme in centralized-distributed fashion is proposed to search the optimal pure Nash equilibrium, which can also maximize the normalized network capacity. 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]

In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM) based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm. [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]

Metal‐halide perovskite has emerged as an effective photovoltaic material for its high power conversion efficiency (PCE), low cost and straightforward fabrication techniques. Unfortunately, its long‐term operational durability, mainly affected by halide ion migration and undercoordinated Pb2+ is still the bottleneck for its large‐scale commercialization. In this work, an ionic liquid (IL) is designed to effectively cap the grain surface for improved stability and reduced trap density. More specifically, the Br− in the IL passivates the undercoordinated Pb2+ by chemically bonding to it, resulting in a thin layer of ionic‐liquid‐perovskite formed on the surface, leading to improved photovoltaic performance and better stability. Specifically, the solar cell exhibits an open‐circuit voltage of 1.192 V and PCE of 24.33% under one‐sun illumination with negligible hysteresis, and a large area (10.75 cm2) integrated module achieves PCE of 20.33%. Moreover, the bare device maintains over 90% of its initial efficiency after 700 h of aging at 65 °C. It also shows outstanding stability with only about 10% degradation after being exposed to the ambient environment for 1000 h. The superior efficiency and stability demonstrate that the present IL passivating strategy is a promising approach for high‐performance large area perovskite solar cell applications. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the content delivery in edge caching networks where requesters can get the interested contents from surrounding helpers by device-to-device (D2D) communications. Taking the finite service capacity and cache space of helpers into account, the problem is to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. The hypergraph theory is first applied to analyze the cumulative content effect caused by multiple content sources, and the problem is formulated as a hypergraph based cooperation cache game with at least one pure Nash equilibrium (NE). The log-linear based cooperation caching algorithm (LBCC) is proposed to find the corresponding NE points. Simulation results verify the superiority of the proposed LBCC. [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]