Recent Advances in Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years. It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other. Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots. However, there are many challenges to realizing the full benefits of D2D. For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue. With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated. New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed. This edited book presents a collection of ten chapters, including one review and nine original research works on addressing many of the aforementioned challenges and beyond

Low-latency Data Uploading in D2D-enabled Cellular Networks

指導教員：姜　暁

Relay assisted device-to-device communication with channel uncertainty

The gains of direct communication between user equipment in a network may not be fully realised due to the separation between the user equipment and due to the fading that the channel between these user equipment experiences. In order to fully realise the gains that direct (device-to-device) communication promises, idle user equipment can be exploited to serve as relays to enforce device-to-device communication. The availability of potential relay user equipment creates a problem: a way to select the relay user equipment. Moreover, unlike infrastructure relays, user equipment are carried around by people and these users are self-interested. Thus the problem of relay selection goes beyond choosing which device to assist in relayed communication but catering for user self-interest. Another problem in wireless communication is the unavailability of perfect channel state information. This reality creates uncertainty in the channel and so in designing selection algorithms, channel uncertainty awareness needs to be a consideration. Therefore the work in this thesis considers the design of relay user equipment selection algorithms that are not only device centric but that are relay user equipment centric. Furthermore, the designed algorithms are channel uncertainty aware. Firstly, a stable matching based relay user equipment selection algorithm is put forward for underlay device-to-device communication. A channel uncertainty aware approach is proposed to cater to imperfect channel state information at the devices. The algorithm is combined with a rate based mode selection algorithm. Next, to cater to the queue state at the relay user equipment, a cross-layer selection algorithm is proposed for a twoway decode and forward relay set up. The algorithm proposed employs deterministic uncertainty constraint in the interference channel, solving the selection algorithm in a heuristic fashion. Then a cluster head selection algorithm is proposed for device-to-device group communication constrained by channel uncertainty in the interference channel. The formulated rate maximization problem is solved for deterministic and probabilistic constraint scenarios, and the problem extended to a multiple-input single-out scenario for which robust beamforming was designed. Finally, relay utility and social distance based selection algorithms are proposed for full duplex decode and forward device-to-device communication set up. A worst-case approach is proposed for a full channel uncertainty scenario. The results from computer simulations indicate that the proposed algorithms offer spectral efficiency, fairness and energy efficiency gains. The results also showed clearly the deterioration in the performance of networks when perfect channel state information is assumed

Deep reinforcement learning-based resource allocation strategy for energy harvesting-powered cognitive machine-to-machine networks

Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption to achieve Green Communication (GC) became an important research topic. In this paper, we investigate the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available energy in EH-devices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and effectively learns from the environment to make allocation decision without the complete and global network information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes in terms of average energy efficiency with an acceptable convergence speed

Interference cancellation and Resource Allocation approaches for Device-to-Device Communications

Network assisted Device-to-Device (D2D) communication as an underlay to cellular spectrum has attracted much attention in mobile network standards for local area connectivity as a means to improve the cellular spectrum utilization and to reduce the energy consumption of User Equipments (UEs). The D2D communication uses resources of the underlying mobile network which results in different interference scenarios. These include interference from cellular to D2D link, D2D to cellular link and interference among D2D links when multiple D2D links share common resources. In this thesis, an orthogonal precoding interference cancellation method is initially presented to reduce the cellular to D2D and D2D to cellular interferences when the cellular channel resources are being shared by a single D2D link. Three different scenarios have been considered when establishing a D2D communication along with a Base Station-to-UE communication. The proposed method is analytically evaluated in comparison with the conventional precoding matrix allocation method in terms of ergodic capacity. This method is then extended for a cluster based multi-link D2D scenario where interference between D2D pairs also exists in addition to the other two interference scenarios. In this work, cluster denotes a group of devices locally communicating through multi-link D2D communications sharing the same radio resources of the Cluster Head. Performance of the proposed method is evaluated and compared for different resource sharing modes. The analyses illustrate the importance of cluster head in each cluster to save the battery life of devices in that cluster. The outage probability is considered as a performance evaluation matrix for guaranteeing QoS constrain of communication links. Hence, the mathematical expressions for outage probability of the proposed method for single-link and multi-link D2D communications are presented and compared with an existing interference cancellation technique. To execute the cluster based interference cancellation approach, a three-step resource allocation scheme is then proposed. It first performs a mode selection procedure to choose the transmission mode of each UEs. Then a clustering scheme is developed to group the links that can share a common resource to improve the spectral efficiency. For the selection of suitable cellular UEs for each cluster whose resource can be shared, a cluster head selection algorithm is also developed. Maximal residual energy and minimal transmit power have been considered as parameters for the cluster head selection scheme. Finally, the expression for maximum number of links that the radio resource of shared UE can support is analytically derived. The performance of the proposed scheme is evaluated using a WINNER II A1 indoor office model. The performance of D2D communication practically gets limited due to large distance and/or poor channel conditions between the D2D transmitter and receiver. To overcome these issues, a relay-assisted D2D communication is introduced in this thesis where a device relaying is an additional transmission mode along with the existing cellular and D2D transmission modes. A transmission mode assignment algorithm based on the Hungarian algorithm is then proposed to improve the overall system throughput. The proposed algorithm tries to solve two problems: a suitable transmission mode selection for each scheduled transmissions and a device selection for relaying communication between user equipments in the relay transmission mode. Simulation results showed that our proposed algorithm improves the system performance in terms of the overall system throughput and D2D data rate in comparison with traditional D2D communication schemes