78 research outputs found
Fog Radio Access Networks: Mobility management, interference mitigation and resource optimization
In order to make Internet connections ubiquitous and autonomous in our daily lives, maximizing the
utilization of radio resources and social information is one of the major research topics in future mobile
communication technologies. Fog radio access network (FRAN) is regarded as a promising paradigm
for the fifth generation (5G) of mobile networks. FRAN integrates fog computing with RAN and makes
full use of the edge of networks. FRAN would be different in networking, computing, storage and
control as compared with conventional radio access networks (RAN) and the emerging cloud RAN.
In this article, we provide a description of the FRAN architecture, and discuss how the distinctive
characteristics of FRAN make it possible to efficiently alleviate the burden on the fronthaul, backhaul
and backbone networks, as well as reduce content delivery latencies. We will focus on the mobility management, interference mitigation, and resource optimization in FRAN. Our simulation results show
that the proposed FRAN architecture and the associated mobility and resource management mechanisms
can reduce the signaling cost and increase the net utility for the RAN
Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory
Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization
Deep learning (DL) based joint resource allocation and RRH association in 5G-multi-tier networks
ABSTRACT: Fifth-Generation (5G) networks have adopted a multi-tier structural model which includes femtocells, picocells, and macrocells to ensure the user quality-of-service (QoS). To meet these QoS demands, the system requires optimization of different resources in different network dynamics carefully. However, if ignored, this will lead to long processing delays and high computational burdens. To avoid this, we proposed Deep Learning (DL) based resource allocation (RA) as a promising solution to meet the network requirements. DL is an effective mechanism where neural networks can learn to develop RA techniques. Thus, an optimized RA decision can be achieved using DL without exhaustive computations. Further, DL uses DL to achieve solutions for joint RA and remote-radio-head (RRH) association problems in multi-tier Cloud-Radio Access Networks (C-RAN). Initially, a summary of existing literature on DL-based RA techniques is provided, followed by a deep neural network (DNN) description, its architectures, and the data training method. Then, a supervised DL technique is presented to solve the joint RA and RRH-association problem. An efficient subchannel assignment, power allocation, and RRH-association (SAPARA) technique are used to generate the training data for the DNN model using the iterative approach where the seed data for the SAPARA technique is taken using a uniform power allocation and path-loss based association (UPA-PLBA) model. After training the DNN model, the accurateness of the presented model is tested. Simulation outcomes demonstrate that our proposed scheme is capable of providing an efficient solution in the considered scenario
Hybrid generalized non-orthogonal multiple access for the 5G wireless networks.
Master of Science in Computer Engineering. University of KwaZulu-Natal. Durban, 2018.The deployment of 5G networks will lead to an increase in capacity, spectral efficiency, low latency
and massive connectivity for wireless networks. They will still face the challenges of resource and
power optimization, increasing spectrum efficiency and energy optimization, among others.
Furthermore, the standardized technologies to mitigate against the challenges need to be developed
and are a challenge themselves. In the current predecessor LTE-A networks, orthogonal frequency
multiple access (OFDMA) scheme is used as the baseline multiple access scheme. It allows users to
be served orthogonally in either time or frequency to alleviate narrowband interference and impulse
noise. Further spectrum limitations of orthogonal multiple access (OMA) schemes have resulted in
the development of non-orthogonal multiple access (NOMA) schemes to enable 5G networks to
achieve high spectral efficiency and high data rates. NOMA schemes unorthogonally co-multiplex
different users on the same resource elements (RE) (i.e. time-frequency domain, OFDMA subcarrier,
or spreading code) via power domain (PD) or code domain (CD) at the transmitter and successfully
separating them at the receiver by applying multi-user detection (MUD) algorithms. The current
developed NOMA schemes, refered to as generalized-NOMA (G-NOMA) technologies includes;
Interleaver Division Multiple Access (IDMA, Sparse code multiple access (SCMA), Low-density
spreading multiple access (LDSMA), Multi-user shared access (MUSA) scheme and the Pattern
Division Multiple Access (PDMA). These protocols are currently still under refinement, their
performance and applicability has not been thoroughly investigated. The first part of this work
undertakes a thorough investigation and analysis of the performance of the existing G-NOMA
schemes and their applicability.
Generally, G-NOMA schemes perceives overloading by non-orthogonal spectrum resource
allocation, which enables massive connectivity of users and devices, and offers improved system
spectral efficiency. Like any other technologies, the G-NOMA schemes need to be improved to
further harvest their benefits on 5G networks leading to the requirement of Hybrid G-NOMA
(G-NOMA) schemes. The second part of this work develops a HG-NOMA scheme to alleviate the
5G challenges of resource allocation, inter and cross-tier interference management and energy
efficiency. This work develops and investigates the performance of an Energy Efficient HG-NOMA
resource allocation scheme for a two-tier heterogeneous network that alleviates the cross-tier
interference and improves the system throughput via spectrum resource optimization. By considering
the combinatorial problem of resource pattern assignment and power allocation, the HG-NOMA
scheme will enable a new transmission policy that allows more than two macro-user equipment’s
(MUEs) and femto-user equipment’s (FUEs) to be co-multiplexed on the same time-frequency RE
increasing the spectral efficiency. The performance of the developed model is shown to be superior to
the PD-NOMA and OFDMA schemes
Interference mitigation in cognitive femtocell networks
“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Femtocells have been introduced as a solution to poor indoor coverage in cellular communication which has hugely attracted network operators and stakeholders. However, femtocells are designed to co-exist alongside macrocells providing improved spatial frequency reuse and higher spectrum efficiency to name a few. Therefore, when deployed in the two-tier architecture with macrocells, it is necessary to mitigate the inherent co-tier and cross-tier
interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning and reasoning.
This research work focuses on the exploitation of cognitive radio in femtocells to mitigate the mutual interference caused in the two-tier architecture. The research work presents original contributions in mitigating interference in femtocells by introducing practical approaches which comprises a power control scheme where femtocells adaptively controls its transmit power levels to reduce the interference it causes in a network. This is especially useful since femtocells are user deployed as this seeks to mitigate interference based on their blind placement in an indoor environment. Hybrid interference mitigation schemes which combine power control and resource/scheduling are also implemented. In a joint threshold power based admittance and contention free resource allocation scheme, the mutual interference between a Femtocell Access Point (FAP) and close-by User Equipments (UE) is mitigated based on admittance. Also, a hybrid scheme where FAPs opportunistically use Resource Blocks (RB) of Macrocell User Equipments (MUE) based on its traffic load use is also employed. Simulation analysis present improvements when these schemes are applied with emphasis in Long Term
Evolution (LTE) networks especially in terms of Signal to Interference plus Noise Ratio (SINR)
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