803 research outputs found
Adaptive stochastic radio access selection scheme for cellular-WLAN heterogeneous communication systems
This study proposes a novel adaptive stochastic radio access selection scheme for mobile users in heterogeneous cellular-wireless local area network (WLAN) systems. In this scheme, a mobile user located in dual coverage area randomly selects WLAN with probability of ω when there is a need for downloading a chunk of data. The value of ω is optimised according to the status of both networks in terms of network load and signal quality of both cellular and WLAN networks. An analytical model based on continuous time Markov chain is proposed to optimise the value of ω and compute the performance of proposed scheme in terms of energy efficiency, throughput, and call blocking probability. Both analytical and simulation results demonstrate the superiority of the proposed scheme compared with the mainstream network selection schemes: namely, WLAN-first and load balancing
A Distributed SON-Based User-Centric Backhaul Provisioning Scheme
5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes
Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing
Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’
Access Network Selection in Heterogeneous Networks
The future Heterogeneous Wireless Network (HWN) is composed of multiple Radio Access
Technologies (RATs), therefore new Radio Resource Management (RRM) schemes
and mechanisms are necessary to benefit from the individual characteristics of each RAT
and to exploit the gain resulting from jointly considering the whole set of the available
radio resources in each RAT. These new RRM schemes have to support mobile users
who can access more than one RAT alternatively or simultaneously using a multi-mode
terminal. An important RRM consideration for overall HWN stability, resource utilization,
user satisfaction, and Quality of Service (QoS) provisioning is the selection of the
most optimal and promising Access Network (AN) for a new service request. The RRM
mechanism that is responsible for selecting the most optimal and promising AN for a
new service request in the HWN is called the initial Access Network Selection (ANS).
This thesis explores the issue of ANS in the HWN. Several ANS solutions that attempt
to increase the user satisfaction, the operator benefits, and the QoS are designed, implemented,
and evaluated.
The thesis first presents a comprehensive foundation for the initial ANS in the H\VN.
Then, the thesis analyses and develops a generic framework for solving the ANS problem
and any other similar optimized selection problem. The advantages and strengths of the
developed framework are discussed. Combined Fuzzy Logic (FL), Multiple Criteria
Decision Making (MCDM) and Genetic Algorithms (GA) are used to give the developed
framework the required scalability, flexibility, and simplicity.
The developed framework is used to present and design several novel ANS algorithms
that consider the user, the operator, and the QoS view points. Different numbers of
RATs, MCDM tools, and FL inference system types are used in each algorithm. A
suitable simulation models over the HWN with a new set of performance evolution
metrics for the ANS solution are designed and implemented. The simulation results show
that the new algorithms have better and more robust performance over the random, the service type, and the terminal speed based selection algorithms that are used as reference
algorithms. Our novel algorithms outperform the reference algorithms in- terms of the
percentage of the satisfied users who are assigned to the network of their preferences and
the percentage of the users who are assigned to networks with stronger signal strength.
The new algorithms maximize the operator benefits by saving the high cost network
resources and utilizing the usage of the low cost network resources. Usually better
results are achieved by assigning the weights using the GA optional component in the
implemented algorithms
Technical advances in the design and deployment of future heterogeneous networks
The trend in wireless communications systems is the enhancement of the network infrastructure with the introduction of small cells, where a specific geographical area is served by low-range, low-power access points. The result is the creation of a heterogeneous topology where macrocells coexist with a variety of small-cell types. In this editorial article we briefly summarize the recent technical advances in the design and deployment of future heterogeneous networks addressed in the papers that compose this special issue. In particular the following aspects are considered: the design of interference and radio resource management algorithms, the analysis of the energy efficiency and power control issues in heterogeneous networks, the concept of coordination in small cell networks, key backhaul aspects of HetNets, deployment issues and overall management strategies.Peer ReviewedPostprint (published version
A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks
The diverse service requirements coming with the
advent of sophisticated applications as well as a large number
of connected devices demand for revolutionary changes in the
traditional distributed radio access network (RAN). To this end,
Cloud-RAN (CRAN) is considered as an important paradigm
to enhance the performance of the upcoming fifth generation
(5G) and beyond wireless networks in terms of capacity, latency,
and connectivity to a large number of devices. Out of several
potential enablers, efficient resource allocation can mitigate various
challenges related to user assignment, power allocation, and
spectrum management in a CRAN, and is the focus of this paper.
Herein, we provide a comprehensive review of resource allocation
schemes in a CRAN along with a detailed optimization taxonomy
on various aspects of resource allocation. More importantly,
we identity and discuss the key elements for efficient resource
allocation and management in CRAN, namely: user assignment,
remote radio heads (RRH) selection, throughput maximization,
spectrum management, network utility, and power allocation.
Furthermore, we present emerging use-cases including heterogeneous
CRAN, millimeter-wave CRAN, virtualized CRAN, Non-
Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex
enabled CRAN to illustrate how their performance can
be enhanced by adopting CRAN technology. We then classify
and discuss objectives and constraints involved in CRAN-based
5G and beyond networks. Moreover, a detailed taxonomy of
optimization methods and solution approaches with different
objectives is presented and discussed. Finally, we conclude the
paper with several open research issues and future directions
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