The high rotation speeds and mega-constellations of low earth orbit satellites (LEO SATs) cause the inter-satellite frequent handovers (HOs) problem which can lead to substantial performance degradation. This paper proposes a novel distributed multi-agent deep Q-network based SAT HO strategy for the LEO SAT networks to simultaneously minimize the number of HOs and maximize the throughputs and the visible times of UEs while satisfying the quality-of-service (QoS) constraints of all UEs. The proposed HO scheme allows UEs to independently and simultaneously perform the HO decision makings based on their own local information, which enables to immediately adapt to the dynamic changes of the LEO SAT network environments. The numerical results demonstrated that our proposed HO strategy achieves the lowest average HO rate and the highest achievable throughputs compared to other conventional HO strategies, while ensuring a higher QoS guarantee time ratio.

In this letter, dynamic content placement of a local cache server that can store a subset of content objects in its cache memory is studied. Contrary to the conventional model in which content placement is optimized based on the time-invariant popularity distribution of content objects, we consider a general time-varying popularity distribution and such a probabilistic distribution is unknown for content placement. A novel learning method for predicting the temporal distribution of future content requests is presented, which utilizes the request histories of content objects whose lifespans are expired. Then we introduce the so-called predictive caching strategy in which content placement is periodically updated based on the estimated future content requests for each update period. Numerical evaluation is performed using real-world datasets reflecting the inherent nature of temporal dynamics, demonstrating that the proposed predictive caching outperforms the conventional online caching strategies.

Quasi-orthogonal space time block codes with power scaling (QSTBC-PS) that linearly combines two Alamouti codes with unequal power scaling has been known to achieve full diversity in multi-input multi-output (MIMO) systems. In this correspondence, we evaluate the physical layer security performance of QSTBC-PS in MIMO wiretap channels under Rayleigh fading. We derive the tight closed-form approximate formula of secrecy outage probability for QSTBC-PS by using moment matching approximation technique and then derive its high signal-to-noise ratio (SNR) approximation and the probability of strictly positive secrecy capacity in explicit form. The numerical examples validate that our analytical results match well to the Monte-Carlo simulation results for general number of receive antennas in Bob and Eve.

Coded computation has attracted great interests as a promising technique to cope with straggling computing nodes in mobile edge computing (MEC) networks. Contrary to the existing coded computation schemes developed with a fixed network topology, this paper studies a MDS coded computation for random networks. Specifically, we put forth maximum distance separable (MDS) coded task offloading and investigate its MDS coded computing gain by deriving the average successful retrieval probability with stochastic geometry in random wireless edge computing networks, where it encodes the original task into multiple equal and small sized MDS coded sub-tasks and offloads their subset to edge computing nodes for computation. We also identify a tradeoff between the latency in processing a sub-task at an edge node and the minimal number of edge nodes required to retrieve the original task output, according to the size of MDS coded sub-tasks. To efficiently control the tradeoff, we determine the desirable size of MDS coded sub-tasks in a semi-closed form to maximize the average successful retrieval probability for regime 1 and regime 2 networks, which correspond to the cases that communication latency is negligible compared to computation latency and that computation latency is negligible compared to communication latency, respectively, and develop an efficient algorithm with low search complexity for a general environment. Our numerical results reveal that the proposed scheme outperforms the other conventional task offloading schemes such as partial task offloading and replication task offloading in terms of average successful retrieval probability.

To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.