870 research outputs found
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
Resource Allocation and Service Management in Next Generation 5G Wireless Networks
The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other.
In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems.
First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework.
Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs).
Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined.
Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond
Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
In this paper, we study the resource allocation and user scheduling problem
for a downlink nonorthogonal multiple access network where the base station
allocates spectrum and power resources to a set of users. We aim to jointly
optimize the sub-channel assignment and power allocation to maximize the
weighted total sum-rate while taking into account user fairness. We formulate
the sub-channel allocation problem as equivalent to a many-to-many two-sided
user-subchannel matching game in which the set of users and sub-channels are
considered as two sets of players pursuing their own interests. We then propose
a matching algorithm which converges to a two-side exchange stable matching
after a limited number of iterations. A joint solution is thus provided to
solve the sub-channel assignment and power allocation problems iteratively.
Simulation results show that the proposed algorithm greatly outperforms the
orthogonal multiple access scheme and a previous non-orthogonal multiple access
scheme.Comment: Accepted as a regular paper by IEEE Transactions on Wireless
Communication
Radio resource allocation in relay based OFDMA cellular networks
PhDAdding relay stations (RS) between the base station (BS) and the mobile stations (MS) in a cellular system can extend network coverage, overcome multi-path fading and increase the capacity of the system.
This thesis considers the radio resource allocation scheme in relay based cellular networks to ensure high-speed and reliable communication. The goal of this research is to investigate user fairness, system throughput and power consumption in wireless relay networks through considering how best to manage the radio resource.
This thesis proposes a two-hop proportional fairness (THPF) scheduling scheme fair allocation, which is considered both in the first time subslot between direct link users and relay stations, and the second time subslot among relay link users.
A load based relay selection algorithm is also proposed for a fair resource allocation. The transmission mode (direct transmission mode or relay transmission mode) of each user will be adjusted based on the load of the transmission node.
Power allocation is very important for resource efficiency and system performance improvement and this thesis proposes a two-hop power allocation algorithm for energy efficiency, which adjusts the transmission power of the BS and RSs to make the data rate on the two hop links of one RS match each other.
The power allocation problem of multiple cells with inter-cell interference is studied. A new multi-cell power allocation scheme is proposed from non-cooperative game theory; this coordinates the inter-cell interference and operates in a distributed manner. The utility function can be designed for throughput improvement and user fairness respectively.
Finally, the proposed algorithms in this thesis are combined, and the system performance is evaluated. The joint radio resource allocation algorithm can achieve a very good tradeoff between throughput and user fairness, and also can significantly improve energy efficiency
Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality
In this paper, the problem of data correlation-aware resource management is
studied for a network of wireless virtual reality (VR) users communicating over
cloud-based small cell networks (SCNs). In the studied model, small base
stations (SBSs) with limited computational resources act as VR control centers
that collect the tracking information from VR users over the cellular uplink
and send them to the VR users over the downlink. In such a setting, VR users
may send or request correlated or similar data (panoramic images and tracking
data). This potential spatial data correlation can be factored into the
resource allocation problem to reduce the traffic load in both uplink and
downlink. This VR resource allocation problem is formulated as a noncooperative
game that allows jointly optimizing the computational and spectrum resources,
while being cognizant of the data correlation. To solve this game, a transfer
learning algorithm based on the machine learning framework of echo state
networks (ESNs) is proposed. Unlike conventional reinforcement learning
algorithms that must be executed each time the environment changes, the
proposed algorithm can intelligently transfer information on the learned
utility, across time, to rapidly adapt to environmental dynamics due to factors
such as changes in the users' content or data correlation. Simulation results
show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms
of delay compared to the Q-learning with data correlation and Q-learning
without data correlation. The results also show that the proposed algorithm has
a faster convergence time than Q-learning and can guarantee low delays.Comment: This paper has been accepted by Asiloma
Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks with Base Station Coordination
This paper addresses the problem of energy-efficient resource allocation in
the downlink of a cellular OFDMA system. Three definitions of the energy
efficiency are considered for system design, accounting for both the radiated
and the circuit power. User scheduling and power allocation are optimized
across a cluster of coordinated base stations with a constraint on the maximum
transmit power (either per subcarrier or per base station). The asymptotic
noise-limited regime is discussed as a special case. %The performance of both
an isolated and a non-isolated cluster of coordinated base stations is examined
in the numerical experiments. Results show that the maximization of the energy
efficiency is approximately equivalent to the maximization of the spectral
efficiency for small values of the maximum transmit power, while there is a
wide range of values of the maximum transmit power for which a moderate
reduction of the data rate provides a large saving in terms of dissipated
energy. Also, the performance gap among the considered resource allocation
strategies reduces as the out-of-cluster interference increases.Comment: to appear on IEEE Transactions on Wireless Communication
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