This paper addressed the energy-efficient resource management problem of an amplify-and-forward relay-assisted bidirectional relay system under a quality-of-service (QoS) constraint. The objective is to develop a holistic resource management algorithm for joint implementation of relay selection, power adaptation and bit-rate management for optimal energy efficiency (EE). A three-stage approach is proposed to solve the energy-efficient resource management problem. At stage 1 (stage of power management), a per-subcarrier energy-efficient problem is investigated, leading to a power adaptation algorithm for maximizing the system EE and ensuring the required level of the system QoS. Within the framework of the power adaptation algorithm and by exploiting spatial diversity of multiple-relay channels, distributed relay selection is investigated at stage 2 (stage of relay management). Next, the bit-rate management problem is tackled at stage 3, namely assigning bit rate to different subcarriers to maximize the system EE further. Finally, summarizing the results achieved at the three stages, a novel EE technology combined with relay selection, bit-rate management and power adaptation is developed. Simulation results validated the correctness and the efficiency of the proposed algorithm. It is shown that the proposed algorithm can significantly reduce the total transmit power of the system while ensuring the required system QoS.

This paper solved a propulsion power minimization problem subject to a total data-bits constraint of an unmanned aerial vehicle (UAV) enabled full-duplex mobile relaying system, where a full-duplex UAV relay is dispatched as a mobile relay to assist data transfer from a source to a destination by using three trajectory flying modes, namely, the UAV first flies in a circle above the source, next flies to the destination in a straight line, and finally flies in a circle above the destination until all the data bits has been transferred. Since the propulsion power minimization problem is a non-convex mixed integer programming problem and its closed-form solution is hard to obtain, it is transformed to three sub-problems so as to simplify its solution. After solving the three sub-problems, an iterative algorithm is proposed to achieve a sub-optimal solution to the propulsion power minimization problem, leading to a new hybrid circular/straight trajectory (HCST) design. Computer simulations are conducted and the results validated the proposed HCST design. It is shown that compared to the straight or circular flight trajectory design, the HCST performs well in terms of energy saving for the long distance and big data communication cases.

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The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node problem (HNP) and exposed node problem (ENP) cannot be effectively handled when both LAA and WiFi nodes attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNP and ENP, based on LBT, we firstly analyze HNP and ENP by means of mathematical approach. The analytical results surprisingly reveal that the hidden node and exposed node probabilities are as high as 41% and 39.33%, respectively. Then, a Give And p-persistent Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure, namely LBR-GAT, is proposed to cope with LBT to reduce the collision caused by HNP as well as to retrieve the bandwidth sacrificed by the ENP. With LBR-GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi. Evaluation results show that the proposed LBR-GAT could conditionally obtain better network performance comparing to legacy LBT.