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The Space-Ground Integrated Network (SGIN) concept constitutes a promising solution for providing seamless global coverage. However, the mobility of satellites and wireless terminals imposes unprecedented challenges on the location management in SGIN. We tackle this challenge by conceiving a split identifier (ID) and Network Address (NA) based design for providing natural mobility support, and characterize the ID-NA mapping allocation problem by exploiting the storage capacity of both Geostationary Earth Orbit Satellites (GEOSs) and Low Earth Orbiting Satellites (LEOSs) to form a spatially distributed binding resolution system and optimize the caching reward in each LEOS. By considering the large quantity of ID-NA mapping and the sparsity of popular mapping having positive mean caching rewards, we formulate the mapping allocation problem as a sparse Multi-Armed Bandit (MAB) learning procedure, where the mappings are treated as the arms and the LEOSs act as the players. A distributed learning algorithm, namely the Sparse Upper confidence bound based Learning aided Caching algorithm (SULC), is proposed for estimating the mean caching rewards of mappings and selecting the optimal mappings for caching. Moreover, we derive a sub-linear upper bound of the cumulative learning regret to prove the learning efficiency of the proposed SULC. Extensive simulations have been conducted to show that the proposed SULC can quickly identify the popular mappings and provide near-optimal content hit rates. In contrast with the existing solutions, SULC has higher caching rewards and can significantly reduce the cumulative regret after a short period of learning.

This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.

Unmanned aerial vehicle (UAV) base station has been proposed as a promising solution in emergency communication and supplementary communication for terrestrial networks due to its flexible layout and good mobility support. However, the dense deployment of UAV base station and ground base station brings great challenges in the configuration of neighbor cell list (NCL) during handover process. This paper presents a Cascading Bandits based Mobility Management (CBMM) algorithm for NCL configuration in the low altitude heterogeneous networks, where online learning is used to exploiting the historical handover information. In addition to the received signal strength, the cell load of each base station is also considered in the handover procedure. We aim at optimizing the configuration of NCL, so as to improve handover performance by increasing the probability of selecting the best target base station while at the same time reducing the selection delay. It is proved that the signaling overhead can be effectively reduced, since the proposed CBMM algorithm can significantly cut down the number of candidate base stations in NCL. Moreover, by ranking the candidate base stations according to their historical performance, the number of measured base stations in handover preparation phase can be effectively reduced to avoid extra delay. The simulation results of the proposed algorithm and other two existing solutions are presented to illustrate that the CBMM algorithm can achieve efficient handover management.

To address the problems in end-to-end networks, researchers have proposed an information-centric network(ICN). In-network caching is one of the important features of ICN, which can greatly improve the efficiency of content distribution. However, the Internet Service Providers(ISP) only deploy Innetwork caching if there is some financial incentives. The existing ICN pricing mechanisms only study paid content but ignore free content in the network. In addition, the existing ICN economic models do not take into account ISP cooperating with Content Provider(CP) to deploy in-network caches. Therefore, the pricing mechanisms already in place cannot be applied in practice. In this paper, different from the existing models which based on the paid content, a new ICN pricing model with free content based on non-cooperative game theory was proposed for the first time and advertisers were effectively added to the ICN pricing model. After analyzing the behavioral relationships between entities within the network, the authors establish the utility function for each entity. The authors analyze the impact of caching and pricing on the revenues of all entities and solve the equilibrium point to develop win-win pricing strategies. Considering the case where paid content coexists with free content in real networks, the authors proposes a more realistic and integrated model. In this paper, many experimental analyses have been conducted in addition to the theoretical analysis, and the experimental results show that the new model is closer to reality than the existing models and can provide better theoretical guidance for network pricing.

In this work we analyze the performance of a full-duplex (FD) multi-cell network where Poisson point process (PPP) distributed half-duplex (HD) users are served by FD base stations (BSs). The use of FD BSs instead of traditional half-duplex (HD) ones results in a surge in the amount of interference present in the network. In order to address this challenge we, (a) adopt a self-interference (SI) cancellation scheme at each BS, (b) apply linear interference alignment (IA) in each cell to cancel out intra-cell interference, and (c) model the distribution of the BSs as a Matern hard-core point process (HCPP) to mitigate inter-cell interference. The performance of both downlink (DL) and uplink (UL) users is analyzed by deriving general expressions, and easily computable good approximations for the ergodic capacity of each type of user.

In this paper, we analyze the performance of a cloud radio access network (CRAN), consisting of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS), each equipped with multiple antennas. To model the spatial distribution of RRHs and analyze its performance, we use stochastic geometry tools. In contrast to previous works on CRAN that consider Poisson Point Process (PPP) model for the spatial distribution of RRHs, we consider a more realistic Matern hard-core point process (MHCPP) model that imposes a certain minimal distance (referred to as hard-core distance) between the two RRHs so that the RRHs are not too close to each other. To compare system performance of CRAN when different transmission strategies are used, three RRH selection schemes are adopted including 1) the best RRH selection (BRS); 2) all RRHs participation (ARP); and 3) nearest RRH selection (NRS). Considering downlink transmission, the ergodic capacity, outage probability, and system throughput of CRAN are analytically characterized for different RRH selection schemes. The presented results demonstrate that compared to PPP model, the increase in hard-core distance will result in a higher outage probability and cause a negative impact on ergodic capacity. Furthermore, when the same total transmit power is consumed, BRS scheme provides the best outage performance while ARP scheme is the best RRH selection scheme when the same transmit SNR at each RRH is assumed. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance. We extend our work to multiuser case with zero-forcing (ZF) precoding where it is proven that the results in multiuser case reduce to the derived results in this work by substituting K=1 for single-user.

In this paper, we analyse the performance of a millimeter wave (mmWave) cloud radio access network (CRAN), where remote radio heads (RRHs) are modelled as a homogeneous Poisson point process (PPP) and blockages are randomly distributed. In contrast to the previous works on CRAN that operate below 6GHz, we consider CRAN operating in mmWave range (30-300 GHz). Since blockages have a significant impact on mmWave communications, we adopt a distance-dependent line-of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) RRHs as two independent non-homogeneous PPP. The outage performance and ergodic capacity of the LOS and NLOS RRHs are analysed and compared. When the RRH with the best channel is selected for transmission, the expressions of outage probability and throughput are provided. The presented results show that due to the severe path loss in NLOS links, in low transmitted power regime, the best RRH (BR) is always LOS. However, in high transmitted power regime, NLOS RRHs can be BR.

In this paper, the performance of a cloud radio access network (CRAN) is analysed, which consists of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS). Different from previous works on CRAN where Poisson Point Process (PPP) is used to model spatial distribution of RRHs, a more realistic Matern Hard-core point process (MHCPP) model is adopted in this work. To compare system performance of CRAN when different transmission strategies are used, two RRH selection schemes are adopted including 1) the best RRH selection (BRS) and 2) all RRHs participation (ARP). Considering downlink transmission, the outage probability and system throughput of CRAN are analytically characterized. The presented results demonstrate that compared to PPP model, the presence of hard-core distance will increase outage probability. Furthermore, the BRS scheme is more energy-efficient than the ARP scheme. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance.