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The through-wall system, which applies radio technologies to detect objects behind the wall, can find many appealing applications such as public security, life detection, and medical health monitoring. While being studied for years, the recent advances in high-performance handheld computing devices and artificial intelligence have made the through-wall system more practical. In this article, we present a tutorial-like study on the fundamental radio technologies used in the through-wall system, as well as its recent advances. Different from the traditional through-wall radars, this paper mainly focuses on the handy through-wall techniques with low power, narrow bandwidth, lightweight, no contact, and civil use. Advanced through-wall systems and open research issues are also presented.

To further boost the performance of LTE to meet the ever-increasing mobile traffic demand in a cost-effective way, applying LTE in unlicensed spectrum, known as LTE-U technology, is considered as a promising complementary solution for achieving the ultra-capacity foreseen in 5G and beyond. In the unlicensed spectrum, LTE-U will share the channel with other unlicensed networks, e.g., Wi-Fi. However, the centralized control architecture of LTE networks is inherently different from the distributed channel access of Wi-Fi network, which poses great challenges to achieve fair coexistence of the two networks. To this end, in this paper, we propose a cross-layer proportional fairness (PF)-based framework to jointly optimize the protocol parameters of the medium access control layer and physical layer of an LTE-U network. Specifically, to achieve throughput-oriented PF between the two heterogeneous networks, the cross-layer optimization framework can be decoupled into a device number weighted time occupation ratio-oriented PF optimization problem and a channel-power allocation-based instantaneous transmission rate-oriented PF optimization problem. Given that LTE-U base stations adopt a listen-before-talk-based channel access scheme, the interactions between the LTE-U and the Wi-Fi networks are modeled by two interactive Markov chains. The effectiveness and the superior performance of the proposed cross-layer PF-based optimization framework are demonstrated and verified by simulations.

The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.

Without the aid of licensed channel, deploying long-term evolution (LTE) networks over unlicensed spectrum (named standalone LTE-U networks) faces the difficulty of establishing and maintaining synchronisation between user equipments and base stations. In this work, considering the two modes of listen-before-talk-based channel access scheme, frame-based equipment (FBE) and load-based equipment (LBE), the authors propose analytical frameworks to study the successful probability of synchronisation and the energy consumption of synchronisation in a standalone LTE-U network. Specifically, for the LBE mode, the authors also propose a Lattice-Poisson algorithm-based approach to derive the distribution of the channel non-occupancy period of a standalone LTE-U network. Furthermore, the authors explore the impact of diverse protocol parameters of both FBE and LBE modes on the two studied performance metrics. Simulation results demonstrate the accuracy of the analysis, and shed some light on the selection of FBE and LBE for standalone LTE-U networks, in terms of synchronisation, energy consumption, and throughput of standalone LTE-U and Wi-Fi networks.

Mobile cloud computing (MCC) enables mobile devices to outsource their computing, storage and other tasks onto the cloud to achieve more capacities and higher performance. One of the most critical research issues is how the cloud can efficiently handle the possible overwhelming requests from mobile users when the cloud resource is limited. In this paper, a novel MCC adaptive resource allocation model is proposed to achieve the optimal resource allocation in terms of the maximal overall system reward by considering both cloud and mobile devices. To achieve this goal, we model the adaptive resource allocation as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests. Extensive simulations are conducted to demonstrate that our proposed model can achieve higher system reward and lower service blocking probability compared to traditional approaches based on greedy resource allocation algorithm. Performance comparisons with various MCC resource allocation schemes are also provided.