Cross-Layer Energy Efficient Resource Allocation in PD-NOMA based H-CRANs: Implementation via GPU

In this paper, we propose a cross layer energy efficient resource allocation and remote radio head (RRH) selection algorithm for heterogeneous traffic in power domain - non-orthogonal multiple access (PD-NOMA) based heterogeneous cloud radio access networks (H-CRANs). The main aim is to maximize the EE of the elastic users subject to the average delay constraint of the streaming users and the constraints, RRH selection, subcarrier, transmit power and successive interference cancellation. The considered optimization problem is non-convex, NP-hard and intractable. To solve this problem, we transform the fractional objective function into a subtractive form. Then, we utilize successive convex approximation approach. Moreover, in order to increase the processing speed, we introduce a framework for accelerating the successive convex approximation for low complexity with the Lagrangian method on graphics processing unit. Furthermore, in order to show the optimality gap of the proposed successive convex approximation approach, we solve the proposed optimization problem by applying an optimal method based on the monotonic optimization. Studying different scenarios show that by using both PD-NOMA technique and H-CRAN, the system energy efficiency is improved

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and 5G beyond, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on the user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational complexities. For each phase, we show how the problem can be solved efficiently. We also propose a low-complexity RAF in which the complexity of the delivery algorithm is significantly reduced. A Delivery-aware cache refreshment strategy (DACRS) in the delivery phase is also proposed to tackle the dynamically changes of network stochastic information. Extensive numerical assessments demonstrate a performance improvement of up to 30% for our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure

Interference Management in NOMA-based Fog-Radio Access Networks via Joint Scheduling and Power Adaptation

Non-Orthogonal Multiple Access (NOMA) and Fog Radio Access Networks (FRAN) are promising candidates within the 5G and beyond systems. This work examines the benefit of adopting NOMA in an FRAN architecture with constrained capacity fronthaul. The paper proposes methods for optimizing joint scheduling and power adaptation in the downlink of a NOMA-based FRAN with multiple resource blocks (RB). We consider a mixed-integer optimization problem which maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints, so as to determine i) the user-to-RB assignment, ii) the allocated power to each RB, and iii) the power split levels of the NOMA users in each RB. The paper proposes a feasible decoupled solution for such non-convex optimization problem using a three-step hybrid centralized/distributed approach. The proposed solution complies with FRAN operation that aims to partially shift the network control to the FAPs, so as to overcome delays due to fronthaul rate constraints. The paper proposes and compares two distinct methods for solving the assignment problem, namely the Hungarian method, and the Multiple Choice Knapsack method. The power allocation and the NOMA power split optimization, on the other hand, are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments

Robust Radio Resource Allocation in MISO-SCMA Assisted C-RAN in 5G Networks

In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this regard, by supposing multiple input and single output (MISO) transmission technology, a novel robust radio resource allocation is proposed where considering uncertain channel state information (CSI), the worst case approach is applied. The main goal of the proposed radio resource allocation is to, maximize the system sum rate with maximum available power at radio remote head (RRH), minimum rate requirement of each slice, maximum frounthaul capacity of each RRH, user association, and SCMA constraints. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where in each iteration, beamforming and joint codebook allocation and user association subproblem are solved separately. By introducing some auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm (MMA) is applied. Via numerical results, the performance of the proposed system model versus different system parameters and for different channel models are investigated.Comment: 11 pages, 8 figure

C-RAN CoMP Methods for MPR Receivers

The growth in mobile network traffic due to the increase in MTC (Machine Type Communication) applications, brings along a series of new challenges in traffic routing and management. The goals are to have effective resolution times (less delay), low energy consuption (given that wide sensor networks which are included in the MTC category, are built to last years with respect to their battery consuption) and extremely reliable communication (low Packet Error Rates), following the fifth generation (5G) mobile network demands. In order to deal with this type of dense traffic, several uplink strategies can be devised, where diversity variables like space (several Base Stations deployed), time (number of retransmissions of a given packet per user) and power spreading (power value diversity at the receiver, introducing the concept of SIC and Power-NOMA) have to be handled carefully to fulfill the requirements demanded in Ultra-Reliable Low-Latency Communication (URLLC). This thesis, besides being restricted in terms of transmission power and processing of a User Equipment (UE), works on top of an Iterative Block Decision Feedback Equalization Reciever that allows Multi Packet Reception to deal with the diversity types mentioned earlier. The results of this thesis explore the possibility of fragmenting the processing capabilities in an integrated cloud network (C-RAN) environment through an SINR estimation at the receiver to better understand how and where we can break and distribute our processing needs in order to handle near Base Station users and cell-edge users, the latters being the hardest to deal with in dense networks like the ones deployed in a MTC environment

Resource Management and Quality of Service Provisioning in 5G Cellular Networks

With the commercial launch of 5G technologies and fast pace of expansion of cellular network infrastructure, it is expected that cellular and mobile networks traffic will exponentially increase. In addition, new services are expected to spread widely, such as the Internet of Things connected to mobile networks. This will add additional burden in terms of traffic load. As a result, some studies suggest that mobile traffic may increase more than 1000 times compared to the amount of traffic that is generated nowadays. This means that network resources for mobile services must be managed and controlled in a smart way, because resources are always limited, but the demand for services and the need for keeping user equipment always connected to mobile networks can be considered unlimited, leaving gap between huge service demands and available resources. In order to narrow this gap, major consideration should be given to the management of network resources to avoid network congestion and performance degradation during peak hour/s and traffic spikes, and allow access to network services to more customers when demand is high. On the other hand, guaranteeing quality of service requirements for the wide range of new services is another challenge that must be met in 5G networks. In this paper we will review 5G networks characteristics and specifications, then carry out a survey on resource management and QoS provisioning to improve and manage resource utilization in 5G networks.Comment: 21 pages, 8 figures, 3 table

An Overview of Low latency for Wireless Communications: an Evolutionary Perspective

Ultra-low latency supported by the fifth generation (5G) give impetus to the prosperity of many wireless network applications, such as autonomous driving, robotics, telepresence, virtual reality and so on. Ultra-low latency is not achieved in a moment, but requires long-term evolution of network structure and key enabling communication technologies. In this paper, we provide an evolutionary overview of low latency in mobile communication systems, including two different evolutionary perspectives: 1) network architecture; 2) physical layer air interface technologies. We firstly describe in detail the evolution of communication network architecture from the second generation (2G) to 5G, highlighting the key points reducing latency. Moreover, we review the evolution of key enabling technologies in the physical layer from 2G to 5G, which is also aimed at reducing latency. We also discussed the challenges and future research directions for low latency in network architecture and physical layer

Interference Mitigation via Rate-Splitting and Common Message Decoding in Cloud Radio Access Networks

Cloud-radio access networks (C-RAN) help overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs), and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, where the cloud is connected to the BSs via limited-capacity backhaul links. The paper proposes splitting the message of each user into two parts, a private part decodable at the intended user only, and a common part which can be decoded at a subset of users, as a means to enable large-scale interference management in CRAN. To this end, the paper optimizes a transmission scheme that combines rate splitting (RS), common message decoding (CMD), clustering and coordinated beamforming. The paper focuses on maximizing the weighted sum-rate subject to per-BS backhaul capacity and transmit power constraints, so as to jointly determine the RS-CMD mode of transmission, the cluster of BSs serving private and common messages of each user, and the associated beamforming vectors of each user private and common messages. The paper proposes solving such a complicated non-convex optimization problem using $l_0$-norm relaxation techniques, followed by inner-convex approximations (ICA), so as to achieve stationary solutions to the relaxed non-convex problem. Numerical results show that the proposed method provides significant performance gain as compared to conventional interference mitigation techniques in CRAN which treat interference as noise (TIN)

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic