40 research outputs found

    Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems via Distributive Stochastic Learning

    Full text link
    In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in one cluster are adaptive to both intra-cluster channel state information (CCSI) and intra-cluster queue state information (CQSI), and computed at the cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost Constrained Partially Observed Markov Decision Process (CPOMDP). By exploiting the special problem structure, we shall derive an equivalent Bellman equation in terms of Pattern Selection Q-factor to solve the CPOMDP. To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the Pattern Selection Q-factor by the sum of Per-cluster Potential functions and propose a novel distributive online learning algorithm to estimate the Per-cluster Potential functions (at each CM) as well as the Lagrange multipliers (LM) (at each BS). We show that the proposed distributive online learning algorithm converges almost surely (with probability 1). By exploiting the birth-death structure of the queue dynamics, we further decompose the Per-cluster Potential function into sum of Per-cluster Per-user Potential functions and formulate the instantaneous power allocation as a Per-stage QSI-aware Interference Game played among all the CMs. We also propose a QSI-aware Simultaneous Iterative Water-filling Algorithm (QSIWFA) and show that it can achieve the Nash Equilibrium (NE)

    Machine Learning for Intelligent IoT Networks with Edge Computing

    Get PDF
    The intelligent Internet of Things (IoT) network is envisioned to be the internet of intelligent things. In this paradigm, billions of end devices with internet connectivity will provide interactive intelligence and revolutionise the current wireless communications. In the intelligent IoT networks, the unprecedented volume and variety of data is generated, making centralized cloud computing ine cient or even infeasible due to network congestion, resource-limited IoT devices, ultra-low latency applications and spectrum scarcity. Edge computing has been proposed to overcome these issues by pushing centralized communication and computation resource physically and logically closer to data providers and end users. However, compared with a cloud server, an edge server only provides nite computation and spectrum resource, making proper data processing and e cient resource allocation necessary. Machine learning techniques have been developed to solve the dynamic and complex problems and big data analysis in IoT networks. Speci - cally, Reinforcement Learning (RL) has been widely explored to address the dynamic decision making problems, which motivates the research on machine learning enabled computation o oading and resource management. In this thesis, several original contributions are presented to nd the solutions and address the challenges. First, e cient spectrum and power allocation are investigated for computation o oading in wireless powered IoT networks. The IoT users o oad all the collected data to the central server for better data processing experience. Then a matching theory-based e cient channel allocation algorithm and a RL-based power allocation mechanism are proposed. Second, the joint optimization problem of computation o oading and resource allocation is investigated for the IoT edge computing networks via machine learning techniques. The IoT users choose to o oad the intensive computation tasks to the edge server while keep simple task execution locally. In this case, a centralized user clustering algorithm is rst proposed as a pre-step to group the IoT users into di erent clusters according to user priorities for achieving spectrum allocation. Then the joint computation o oading, computation resource and power allocation for each IoT user is formulated as an RL framework and solved by proposing a deep Q-network based computation o oading algorithm. At last, to solve the simultaneous multiuser computation o oading problem, a stochastic game is exploited to formulate the joint problem of computation o oading mechanism of multiple sel sh users and resource (including spectrum, computation and radio access technologies resources) allocation into a non-cooperative multiuser computation o oading game. Therefore, a multi-agent RL framework is developed to solve the formulated game by proposing an independent learners based multi-agent Q-learning algorithm

    Throughput Maximization in Unmanned Aerial Vehicle Networks

    Get PDF
    The use of Unmanned Aerial Vehicles (UAVs) swarms in civilian applications such as surveillance, agriculture, search and rescue, and border patrol is becoming popular. UAVs have also found use as mobile or portable base stations. In these applications, communication requirements for UAVs are generally stricter as compared to conventional aircrafts. Hence, there needs to be an efficient Medium Access Control (MAC) protocol that ensures UAVs experience low channel access delays and high throughput. Some challenges when designing UAVs MAC protocols include interference and rapidly changing channel states, which require a UAV to adapt its data rate to ensure data transmission success. Other challenges include Quality of Service (QoS) requirements and multiple contending UAVs that result in collisions and channel access delays. To this end, this thesis aims to utilize Multi-Packet Reception (MPR) technology. In particular, it considers nodes that are equipped with a Successive Interference Cancellation (SIC) radio, and thereby, allowing them to receive multiple transmissions simultaneously. A key problem is to identify a suitable a Time Division Multiple Access (TDMA) transmission schedule that allows UAVs to transmit successfully and frequently. Moreover, in order for SIC to operate, there must be a sufficient difference in received power. However, in practice, due to the location and orientation of nodes, the received power of simultaneously transmitting nodes may cause SIC decoding to fail at a receiver. Consequently, a key problem concerns the placement and orientation of UAVs to ensure there is diversity in received signal strength at a receiving node. Lastly, interference between UAVs serving as base station is a critical issue. In particular, their respective location may have excessive interference or cause interference to other UAVs; all of which have an impact on the schedule used by these UAVs to serve their respective users

    Mobile Ad-Hoc Networks

    Get PDF
    Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

    DR9.3 Final report of the JRRM and ASM activities

    Get PDF
    Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version

    Design of large polyphase filters in the Quadratic Residue Number System

    Full text link
    corecore