ABSTRACT The sphere decoding (SD) algorithm for polar codes can achieve maximum likelihood decoding performance, but suffers from high computational complexity. Based on the polar codes with symmetrical structure, the complexity of the SD algorithm can be reduced at the expense of a slight performance loss. To further improve the performance of the SD algorithm for symmetric polar codes, an improved flip SD algorithm is proposed in this paper. When the estimated codeword fails the cyclic redundancy check (CRC), the proposed algorithm flips an unreliable combined value and re‐performs the decoding process. Specifically, using the structure of symmetric polar codes, a flipping set is first constructed to incorporate unreliable combined values. The combined values in the flipping set are sorted according to their reliability. Then, a new flipping operation is developed for the flipped values to further improve performance. Simulation results show that, compared to the symmetric SD algorithms, the proposed algorithm can achieve a performance gain of up to 1.6 dB for the polar code of length 64 with different rates at the frame error rate (FER) of , with a slightly higher computational complexity; when compared to the original SD algorithm, the proposed algorithm can achieve a maximum of 1.58 dB performance gain at FER = but with a lower computational complexity.

Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.

In this article, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the offline optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On the one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the Line-of-Sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.

In this paper, we investigate the hybrid access control in two-tier small cell networks from the perspective of incentive mechanism design, considering macro base station's private information. We formulate this problem as a Stackelberg game. To be specific, the macro base station (MBS) and small cell base stations (SBSs) are modeled as a leader with specific payoff preference and followers, respectively. A subsidy mechanism is adopted by MBS. Only when the SBS can provide acceptable service level for macro user (MUE), then MBS handovers MUE to SBS and provides subsidy to SBS; otherwise, MBS serves MUE by itself. We consider the impacts of MBS's private information on the final equilibrium of the proposed game. Theoretical analysis and simulation results show it is better for MBS to broadcast the private information to get more payoff from the perspective of incentive mechanism design.

In this study, the authors investigate the spectrum sharing and interference management in hyper‐dense cloud radio access networks (C‐RANs). The authors formulate this problem as a local altruistic coalition formation game (LACF) with externalities. Different from previous studies considering maximising individual small cell's throughput, the authors’ purpose is to maximise the social welfare, i.e. the sum satisfaction of remote radio heads (RRHs) with diverse traffic types. The authors design a local altruistic utility function for RRH, i.e. each RRH cares about not only his own interest but also his neighbours’. They make autonomous decisions, finally shaping the entire RRHs into multiple disjoint coalitions. In addition, joint transmission coordinated multi‐point technology is considered within coalition to mitigate interference. Then, the authors propose a distributed coalitional formation algorithm based on modified recursive core to obtain the final stable coalition partition. Furthermore, the system stability, convergence and complexity of the proposed algorithm are analysed. Numerical results show that the proposed algorithm outperforms the classical and non‐cooperation schemes in the perspective of both social welfare and individual fairness. To be specific, the average rate per RRH of the authors’ proposed scheme is superior to the classical and non‐cooperation cases by 20 and 25% in hyper‐dense scenario, respectively.

Spectrum auction is one of the most effective solutions to allocate the spectrum resource following the market rules and has attracted much attention from both academia and industry. However, most of the existing studies assume that the spectrum buyers' demands are homogeneous and the interference relationship is fixed without any change with the variation of spectrum. Furthermore, the economical efficiency of auction outcome has not drawn enough attention. That motivates us to design an auction scheme to jointly consider the multi-demand of buyers, heterogeneous spectrum, and economical efficiency. In this paper, we propose a novel overlapping coalition formation-based double auction, called VERACITY, to address this problem. The auctioneer groups the conflict free buyers into the same coalition and allows a buyer to join multiple coalitions based on the heterogeneous demand. Dynamic overlapping coalition formation implemented by the auctioneer is to find the approximately optimal coalition structure corresponding to the economical efficiency outcome, i.e., maximizing the social welfare. Furthermore, we prove that VERACITY is individually rational, budget balanced, truthful, and economically efficient. Simulation results are presented to show the convergence and effectiveness of the proposed VERACITY.