Distribution Of Radio Resources Research Articles

Comparative Study of Radio Resource Distribution Algorithms

The equitable distribution of radio resources among different users in wireless networks is a difficult problem and has attracted the interest of many studies. This study presents the Proportional Fair Q-Learning Algorithm (PFLA) to enable the equitable distribution of radio resources among diverse users through the integration of Q-learning and proportional fairness principles. The PFLA, Round Robin (RR), and Max Throughput (MaxTP) algorithms were compared to evaluate their effectiveness in resource allocation. Performance was measured in terms of sum-rate throughputs and fairness index. The comparison results showed an improvement in the fairness index metrics for PFLA compared to the other algorithms. PFLA showed gains of 11.62 and 43% in the fairness index compared to RR and MaxTP, respectively. These results show that PFLA is more efficient in utilizing available resources, leading to higher overall system throughput and demonstrating its ability to balance performance metrics between users, especially when the number of users increases.

A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)

A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.

Cross-Layer Model of Dynamic Distribution of Radio Resources and Data Flow Service in LTE Networks

In this article, the results of the development of a mathematical model for the time-frequency resource allocation of the uplink channel and flow service in LTE (Long Term Evolution) networks are given. The proposed model is aimed at ensuring the maximum performance of the radio channel and the guaranteed quality of service for data flows of wireless network users. A comparative analysis of the proposed model with the existing methods of the time-frequency resource allocation of the LTE technology is carried out in terms of ensuring the overall performance of the uplink and allocating the required transmission rate to user stations while maintaining the quality of service. It is shown that the proposed model of dynamic distribution of radio resources and servicing of data streams increases the overall performance of the uplink compared to the Round Robin and Proportional Fair methods, by 1.42 and 1.23 times, respectively.

Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks

For accommodating the heterogeneous services that are anticipated for the fifth-generation (5G) mobile networks, the concept of network slicing serves as a key technology. Spanning both the core network (CN) and radio access network (RAN), slices are end-to-end virtual networks that share the resources of a physical network. Slicing the RAN can be more challenging than slicing the CN since RAN slicing deals with the distribution of radio resources, which have fluctuating capacity and are harder to extend. Improving multiplexing gains, while assuring the slice isolation is the main challenging task for RAN slicing. This paper provides a flexible and configurable framework for RAN slicing, where diverse requirements of slices are simultaneously taken into account, and slice management algorithms adjust the control parameters of different radio resource management (RRM) mechanisms to satisfy the slices’ service level agreements (SLAs). One of the proposed algorithms is based merely on heuristics and the other one utilizes an artificial neural network (ANN) to predict the behavior of the cellular network and make better decisions in the adjustment of the RRM mechanisms. Furthermore, a protection mechanism is devised to prevent the slices from negatively influencing each other’s performances. A simulation-based analysis demonstrates that in presence of local or global overload of one of the slices, the ANN-based method increases the number of key performance indicators (KPIs) that fulfill their defined SLA targets. Finally, we show that the proposed protection mechanism can force the negative effects of an overloading slice to be contained to that slice and the other slices are not affected as severely.

Contribution to radio resource distribution approach in wireless cellular software defined networking

We witness actually huge wireless traffic demand on a limited bandwidth. This leads to develop complex and power-hungry network technologies that are often harder to manage. Thus, some core network features as radio resource management (RRM) introduce important issues as scalability and energy efficiency. This paper debates on next generation wireless cellular network radio resource distribution (RRD) algorithms. We leverage software defined network (SDN) benefits by proposing algorithms on demand (AoD), which aggregates several schedulers at the network controller. Based on Markov prediction, a real time context data analysis adapts the most suited RRD scheme at the evolved Node B. This choice depends on cell status (load, interference, etc.), thanks to the device programmability feature of SDN. Moreover, AoD reduces power consumption by optimising always the transmission rate. Simulations show that one can approach fifth generation (5G) radio policies by AoD theory with quality of experience and low carbon footprint as benefits.

Contribution to radio resource distribution approach in wireless cellular software defined networking

We witness actually huge wireless traffic demand on a limited bandwidth. This leads to develop complex and power-hungry network technologies that are often harder to manage. Thus, some core network features as radio resource management (RRM) introduce important issues as scalability and energy efficiency. This paper debates on next generation wireless cellular network radio resource distribution (RRD) algorithms. We leverage software defined network (SDN) benefits by proposing algorithms on demand (AoD), which aggregates several schedulers at the network controller. Based on Markov prediction, a real time context data analysis adapts the most suited RRD scheme at the evolved Node B. This choice depends on cell status (load, interference, etc.), thanks to the device programmability feature of SDN. Moreover, AoD reduces power consumption by optimising always the transmission rate. Simulations show that one can approach fifth generation (5G) radio policies by AoD theory with quality of experience and low carbon footprint as benefits.

An Efficient Scheduling Scheme for OFDMA Resource Blocks with Joint User Scheduling based on Earliest Deadline First with Carrier Aggregation (CA) in LTE-A System

This paper focuses on the resource planning within the downlink of Long Term Evolution Advanced (LTE-A) in addition to Carrier Aggregation (CA) technology. Every time CA is being put to use, it is very vital to complement the LTE-A system with a robust and suitable resource scheduling plan. The two basic resource scheduling schemes are Separate Random User Scheduling and Joint User Scheduling (JUS). The former scheme is simpler, but less efficient, while the latter performs optimally at the expense of higher computation complexity. As a matter of fact, LTE-Advanced networks are designed to render minimal latency and additional broadcast rates, coupled with increased spectral operational performance beyond the benchmark of 3G networks. Nevertheless, concerning the insufficient radio resources, the efficient application and management of these scarce resources are most desirable. Hence, the best approach for profitable resource allocation must be of central focus to meet the Quality of Service (QoS) standard. For the purpose of enhancing the system equity and refraining from high complexity of the system, it is recommended that the JUS is based on Earliest Deadline First (EDF), to offer QoS guarantees and equitable distribution of radio resources. From the simulation results, the following improvements were made: The proposed JUS based on EDF, improved the throughput of the users by adjusting the deadline for the allocated resource blocks of the users. The JUS-EDF has improved the users throughput by 10 % as compared to simple JUS. It also enhanced the system fairness by adjusting the priority of each user according to Earliest Deadline compared to JUS, by 8.88 %. Also JUS-EDF reduced packet delays by 1 s, as compared to the conventional JUS. This paper has introduced a sub-optimized packet scheduling algorithm to fully utilize the new feature in LTE-A downlink system.

Long Term Evolution (LTE) Scheduling Algorithms in Wireless Sensor Networks (WSN)

Many applications of wireless sensor network such as smart metering, environment monitoring and health care [1] use a large number of sensor nodes, which generate a huge amount of traffic with the specification of the different QoS requirements. Broadband transmission can be achieved through the mobile systems. Long Term Evolution (LTE) is the main efficient broadband technology in current mobile communications. Introducing Wireless sensor networks into the Long Term Evolution (LTE) networks, can be achieved successfully but with some challenges, such as the traffic overload due to a large number of sensor nodes. Traffic scheduling plays an important role in LTE technology; this can be achieved by assigning the shared resources among users in an efficient and optimized manner. This paper discusses the effect of many types of scheduling algorithms on the downlink performance, this can be done in terms of throughput, block error rate (BLER) and fairness measurements using a MATLAB-based system level Simulator is provide which from Vienna University under License Agreement for ”Academic Usage” [8]. An effective radio scheduler to optimize the distribution of radio resources among the sensor nodes is also studied. The guaranteed QoS user demands can be considered as a great challenge in the current field of research. General Terms Computer Network, Wireless Networks, Modeling, Scheduling, Algorithms.

Survey on Scheduling and Radio Resources Allocation in LTE

This paper focuses on an essential task of the enhanced NodeB eNodeB element in LTE architecture, the Radio Resource Manager RRM, which aims to accept or reject requests for connection to the network based on some constraints and ensuring optimal distribution of radio resources between Users Equipments UEs. Its main functionalities include Admission Control AC and Packet Scheduling PS. This paper will center mainly on the PS part of the RRM task, which performs the radio resource allocation in both uplink and downlink directions. Several approaches and algorithms have been proposed in the literature to address this need ie : allocate resources efficiently, the diversity and multitude of algorithms is related to the factors considered for the optimal management of radio resource, specifically, the traffic type and the QoS Quality of Service requested by the UE. In this article, an art's state of the radio resource allocation strategies and a detailed study of several scheduling algorithms proposed for LTE (uplink and downlink) are made. Therefore, we offer our evaluation and criticism.