Performance Analysis of Network-Assisted Two-Hop D2D Communications

Network-assisted single-hop device-to-device (D2D) communication can increase the spectral and energy efficiency of cellular networks by taking advantage of the proximity, reuse, and hop gains when radio resources are properly managed between the cellular and D2D layers. In this paper we argue that D2D technology can be used to further increase the spectral and energy efficiency if the key D2D radio resource management algorithms are suitably extended to support network assisted multi-hop D2D communications. Specifically, we propose a novel, distributed utility maximizing D2D power control (PC) scheme that is able to balance spectral and energy efficiency while taking into account mode selection and resource allocation constraints that are important in the integrated cellular-D2D environment. Our analysis and numerical results indicate that multi-hop D2D communications combined with the proposed PC scheme can be useful not only for harvesting the potential gains previously identified in the literature, but also for extending the coverage of cellular networks.Comment: 6 pages and 7 figure

A Performance Complexity Analysis of four Suboptimal SDMA Algorithms

Space division multiple access (SDMA) is a promising solution to improve the spectral efficiency of future mobile radio systems. However, finding the group of mobile stations (MSs) that maximizes the system capacity using SDMA is a complex combinatorial problem, which can only be assuredly solved through an exhaustive search (ES). Because an ES is usually too complex, several suboptimal SDMA algorithms have been proposed. SDMA algorithms mainly differ on the grouping metrics they employ to quantify the spatial compatibility among MSs and on the grouping algorithm used to build the SDMA groups while avoiding ESs. In this work, the performance-complexity trade-off of four SDMA algorithms is investigated in terms of the average system capacity they achieve and on the number of operations they require. Expressions for the computational complexity of the algorithms are presented and it is shown that the algorithms proposed by the authors by Maciel and Klein (2007) attain almost the same average system capacity with comparable or lower complexity than other algorithms considered for benchmarking

Scheduling for Massive MIMO with Hybrid Precoding using Contextual Multi-Armed Bandits

In this work we study different scheduling problems in the downlink of a Frequency Division Duplex multiuser wireless system that employs a hybrid precoding antenna architecture for massive Multiple Input Multiple Output. In this context, we propose a scheduling framework using Reinforcement Learning (RL) tools, namely Contextual Multi-Armed Bandits (CMAB), that can dynamically adapt themselves to solve three scheduling problems, which are: i) Maximum Throughput (MT); ii) Maximum Throughput with Fairness Guarantees (MTFG), and; iii) Maximum Throughput with QoS Guarantees (MTQG), which are well-known relevant problems. Before performing scheduling itself, we exploit statistical Channel State Information (CSI) to create clusters of spatially compatible User Equipmentss (UEss). This structure, combined with the usage of Zero-Forcing precoding, allows us to reduce the scheduler complexity by considering each cluster as an independent virtual RL scheduling agent. Next, we apply a new learning-based scheduler aiming to optimize the desired system performance metric. Moreover, only scheduled UEss need to feed back instantaneous equivalent CSI, which also reduces the signaling overhead of the proposal. The superiority of the proposed framework is demonstrated through numerical simulations in comparison with reference solutions

Paving the Way Toward Mobile IAB: Problems, Solutions and Challenges

Deploying access and backhaul as wireless links, a.k.a. integrated access and backhaul (IAB), is envisioned as a viable approach to enable flexible and dense networks. Even further, mobile IAB (mIAB) is a candidate solution to enhance the connectivity of multiple user equipment (UE) moving together. In this context, different of other works from the literature, the present work overviews the basis for the deployment of mIAB by presenting: 1) the current status of IAB standardization in the fifth generation (5G) new radio (NR); 2) a new taxonomy for state-of-the-art works regarding fixed IAB and mIAB; 3) an extensive performance analysis of mIAB based on simulation results; and 4) open challenges and potential future prospects of mIAB. Specifically, the proposed taxonomy classifies IAB works according to different perspectives and categorizes mIAB works according to the type of mobile node. For each type of mobile node, the main studied topics are presented. Regarding the performance evaluation, we consider an urban macro scenario where mIAB nodes are deployed in buses in order to improve the passengers’ connection. The results show that, compared to other network architectures, the deployment of mIAB nodes remarkably improves the passengers’ throughput and latency in both downlink and uplink

Suboptimal Resource Allocation for Multi-User MIMO-OFDMA Systems

iii This thesis was prepared in the period of time between April 2005 and September 2008, during which I have been with the Communications Engineering Lab at the Institute of Telecommunications of the Technische Universität Darmstadt. I would like to thank Prof. Dr.-Ing. Anja Klein for her support, incentive, guidance, and valuable suggestions and comments during the supervision of my studies, which have decisively contributed to the elaboration of this work. I also thank Prof. Dr.-Ing. Martin Haardt from the Technishe Universität Ilmenau for taking the time to be on my dissertation committee and evaluate my work. Moreover, I thank Prof. Dr.-Ing. Dr. h.c. Willmut Zschunke from the Technisch