Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed

Spatial Domain Management and Massive MIMO Coordination in 5G SDN

In 5G mobile communication systems, massive multiple-input multiple-output (MIMO) and heterogeneous networks (HetNets) play crucial roles to achieve expected coverage and capacity across venues. This paper correspondingly addresses software-defined network (SDN) as the central controller of radio resource management in massive MIMO HetNets. In particular, we identify the huge spatial domain information management and complicated MIMO coordination as the grand challenges in 5G systems. Our work accordingly distinguishes itself by considering more network MIMO aspects, including flexibility and complexity of spatial coordination. In our proposed scheme, SDN controller first collects the user channel state information in an effective way, and then calculates the null-space of victim users and applies linear precoding to that null-space. Simulation results show that our design is highly beneficial and easy to be deployed, due to its high quality of service performance but low computation complexity

Performance Evaluation and Enhancement in 5G Networks : A Stochastic Geometry Approach

PhDThe deployment of heterogeneous networks (HetNets), in which low power nodes (LPNs) and high power nodes (HPNs) coexist, has become a promising solution for extending coverage and increasing capacity in wireless networks. Meanwhile, several advanced technologies such as massive multi-input multi-output (MIMO), cloud radio access networks (C-RAN) and device-to-device (D2D) communications have been proposed as competent candidates for supporting the next generation (5G) network. Since single technology cannot solely achieve the envisioned 5G requirements, the e ect of integrating multiple technologies in one system is worth to be investigated. In this thesis, a thoroughly theoretical analysis is conducted to evaluate the network performance in di erent scenarios, where two or more 5G techniques are employed. First, the downlink performance of massive MIMO enabled HetNets is fully evaluated. The exact and asymptotic expressions for the probability of a user being associated with a macro cell or a small cell are presented. The analytical expressions for the spectrum e ciency (SE) and energy e ciency (EE) in the K-tier network are also derived. The analysis reveals that the implementation of massive MIMO in the macro cell can considerably improve the network performance and decrease the demands for small cells in HetNets, which simpli es the network deployment. Then, the downlink performance of a massive MIMO enabled heterogeneous C-RAN is investigated. The exact expressions for the SE and EE of the remote radio heads (RRHs) tier and a tractable approximation approach for evaluating the SE and EE of the macrocell tier are obtained. Numerical results collaborate the analysis and prove that massive MIMO with dense deployment of RRHs can signi cantly enhance the performance of heterogeneous C-RAN theoretically. Next, the uplink performance of massive MIMO enabled HetNets is exploited with interference management via derived SE and EE expressions. The numerical results show that the uplink performance in the massive MIMO macrocells can be signi cantly improved through uplink power control in the small cells, while more uplink transmissions in the macrocells have mild adverse e ect on the uplink performance of the small cells. In addition, the SE and EE of the massive MIMO macrocells with heavier load can be improved by expanding the small cell range. Lastly, the uplink performance of the D2D underlaid massive MIMO network is investigated and a novel D2D power control scheme is proposed. The average uplink achievable SE and EE expressions for the cellular and D2D are derived and results demonstrate that the proposed power control can e ciently mitigate the interference from the D2D. Moreover, the D2D scale properties are obtained, which provide the su cient conditions for achieving the anticipated SE. The results demonstrate that there exists the optimal D2D density for maximizing the area SE of D2D tier. In addition, the achievable EE of a cellular user can be comparable to that of a D2D user. Stochastic geometry is applied to model all of the systems mentioned above. Monte Carlo simulations are also developed and conducted to validate the derived expressions and the theoretical analysis

one6G white paper, 6G technology overview:Second Edition, November 2022

6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond. These are characterized by a variety of diverse, often conflicting requirements, from technical ones such as extremely high data rates, unprecedented scale of communicating devices, high coverage, low communicating latency, flexibility of extension, etc., to non-technical ones such as enabling sustainable growth of the society as a whole, e.g., through energy efficiency of deployed networks. On the one hand, 6G is expected to fulfil all these individual requirements, extending thus the limits set by the previous generations of mobile networks (e.g., ten times lower latencies, or hundred times higher data rates than in 5G). On the other hand, 6G should also enable use cases characterized by combinations of these requirements never seen before, e.g., both extremely high data rates and extremely low communication latency). In this white paper, we give an overview of the key enabling technologies that constitute the pillars for the evolution towards 6G. They include: terahertz frequencies (Section 1), 6G radio access (Section 2), next generation MIMO (Section 3), integrated sensing and communication (Section 4), distributed and federated artificial intelligence (Section 5), intelligent user plane (Section 6) and flexible programmable infrastructures (Section 7). For each enabling technology, we first give the background on how and why the technology is relevant to 6G, backed up by a number of relevant use cases. After that, we describe the technology in detail, outline the key problems and difficulties, and give a comprehensive overview of the state of the art in that technology. 6G is, however, not limited to these seven technologies. They merely present our current understanding of the technological environment in which 6G is being born. Future versions of this white paper may include other relevant technologies too, as well as discuss how these technologies can be glued together in a coherent system