Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Multicast Scheduling and Resource Allocation Algorithms for OFDMA-Based Systems: A Survey

Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS)requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems

Radio resource management for OFDMA systems under practical considerations.

Orthogonal frequency division multiple access (OFDMA) is used on the downlink of broadband wireless access (BWA) networks such as Worldwide Interoperability for Microwave Access (WiMAX) and Long Term Evolution (LTE) as it is able to offer substantial advantages such as combating channel impairments and supporting higher data rates. Also, by dynamically allocating subcarriers to users, frequency domain diversity as well as multiuser diversity can be effectively exploited so that performance can be greatly improved. The main focus of this thesis is on the development of practical resource allocation schemes for the OFDMA downlink. Imperfect Channel State Information (CSI), the limited capacity of the dedicated link used for CSI feedback, and the presence of a Connection Admission Control (CAC) unit are issues that are considered in this thesis to develop practical schemes. The design of efficient resource allocation schemes heavily depends on the CSI reported from the users to the transmitter. When the CSI is imperfect, a performance degradation is realized. It is therefore necessary to account for the imperfectness of the CSI when assigning radio resources to users. The first part of this thesis considers resource allocation strategies for OFDMA systems, where the transmitter only knows the statistical knowledge of the CSI (SCSI). The approach used shows that resources can be optimally allocated to achieve a performance that is comparable to that achieved when instantaneous CSI (ICSI) is available. The results presented show that the performance difference between the SCSI and ICSI based resource allocation schemes depends on the number of active users present in the cell, the Quality of Service (QoS) constraint, and the signal-to- noise ratio (SNR) per subcarrier. In practical systems only SCSI or CSI that is correlated to a certain extent with the true channel state can be used to perform resource allocation. An approach to quantifying the performance degradation for both cases is presented for the case where only a discrete number of modulation and coding levels are available for adaptive modulation and coding (AMC). Using the CSI estimates and the channel statistics, the approach can be used to perform resource allocation for both cases. It is shown that when a CAC unit is considered, CSI that is correlated with its present state leads to significantly higher values of the system throughput even under high user mobility. Motivated by the comparison between the correlated and statistical based resource allocation schemes, a strategy is then proposed which leads to a good tradeoff between overhead consumption and fairness as well as throughput when the presence of a CAC unit is considered. In OFDMA networks, the design of efficient CAC schemes also relies on the user CSI. The presence of a CAC unit needs to be considered when designing practical resource allocation schemes for BWA networks that support multiple service classes as it can guarantee fairness amongst them. In this thesis, a novel mechanism for CAC is developed which is based on the user channel gains and the cost of each service. This scheme divides the available bandwidth in accordance with a complete partitioning structure which allocates each service class an amount of non-overlapping bandwidth resource. In summary, the research results presented in this thesis contribute to the development of practical radio resource management schemes for BWA networks

Policy-Based Radio Resource Management in Multicast OFDMA Systems

Η ασύρματηφασματική αποδοτικότητα είναι ένας, όλο και περισσότερο, σημαντικός παράγοντας εξαιτίας της ταχείας ανάπτυξης των ασύρματων υπηρεσιών ευρείας ζώνης. Η σχεδίαση ενός συστήματος με πολλά φέροντα, όπως είναι ένα σύστημα OFDMA,επιτρέπει στα συστήματα να έχουν υψηλή χωρητικότητα για να ικανοποιήσουν τις απαιτήσεις των υπηρεσιών ευρείας ζώνης.Αυτή η αυξημένη χωρητικότητα των συστημάτων μπορεί να βελτιστοποιηθεί περαιτέρω εκμεταλλευόμενοι καλύτερα τα χαρακτηριστικά των ασύρματων καναλιών. Ηθεμελιώδηςιδέα ενός σχήματος κατανομής πόρων είναι η αποτελεσματική κατανομή των διαθέσιμων ασύρματων πόρων, όπως είναι οι υποφορείς και η ισχύς εκπομπής, μεταξύ των χρηστών του συστήματος. Σχετικά με τα προβλήματα της κατανομής πόρων σε ασύρματα συστήματα τηλεπικοινωνιών βασισμένα στην τεχνική OFDMA, η περισσότερη έρευνα επικεντρώνεται στην αναζήτηση πολιτικών ανάθεσης υποφορέων και ισχύος. Οι διαθέσιμες τεχνικές της βιβλιογραφίας δεν μπορούν να εφαρμοστούν όπως είναι σε συστήματα πολυεκπομπής. Επιπλέον, οι υπάρχουσες τεχνικές δεν μπορούν να εφαρμοστούν αμετάβλητες σε πραγματικά συστήματα στα οποία υπάρχει μεγάλος αριθμός OFDMυποφορέων, καθώς η υπολογιστική πολυπλοκότητα είναι πολύ μεγάλη. Ο βασικός στόχος της παρούσας διπλωματικής εργασίας είναι η πρόταση ικανών μηχανισμών κατανομής των διαθέσιμων υποφορέων σε ασύρματα συστήματα πολυεκπομπής χρησιμοποιώντας την τεχνολογία OFDMA. Πιο συγκεκριμένα, σχετικά με τα συστήματα πολυεκπομπής, θεωρούμε ότι τόσο ο σταθμός βάσης όσο και κάθε χρήστης είναι εφοδιασμένοι με μοναδική κεραία και η μονάδα κατανομής δεν είναι ο υποφορέας, όπως στα συμβατικά συστήματα OFDMA, αλλά μία ομάδα γειτονικώνυποφορέων, η οποία ονομάζεται τεμάχιο, με σκοπό τη μείωση της μεγάλης υπολογιστικής πολυπλοκότητας. Ένας αποτελεσματικός αλγόριθμος προτείνεται του οποίου ο στόχος είναι η μεγιστοποίηση του συνολικού ρυθμού μετάδοσης δεδομένων με περιορισμούς στη συνολική διαθέσιμη ισχύ, στο BERανά τεμάχιο και στους αναλογικούς περιορισμούς μεταξύ των ρυθμών μετάδοσης δεδομένων των ομάδων χρηστών. Η προσομοίωση και η ανάλυση της πολυπλοκότητας που παρουσιάζονται, υποστηρίζουν τα πλεονεκτήματα της κατανομής πόρων σε συστήματα πολυεκπομπήςOFDMA τα οποία βασίζονται σε κατανομή τεμαχίων και έχουν ως στόχος την εξασφάλιση της αναλογικότητας μεταξύ των ρυθμών μετάδοσης δεδομένων των ομάδων χρηστών.Wireless spectral efficiency is increasingly important due to the rapid growth of demand for high data rate wideband wireless services. The design of a multi-carrier system, such as an OFDMA system, enables high system capacity suited for these wideband wireless services. This system capacity can be further optimized with a resource allocation scheme by exploiting the characteristics of the wireless fading channels. The fundamental idea of a resource allocation scheme is to efficiently distribute the available wireless resources, such as the subcarriers and transmission power, among all admitted users in the system. Regarding the problems of resource allocation in OFDMA-based wireless communicationsystems, much of the research effort mainly focuses on finding efficient power controland subcarrier assignment policies. With systems employing multicast transmission,the available schemes in literature are not always applicable. Moreover, the existing approachesare particularly inaccessible in practical systems in which there are a large numberof OFDM subcarriers being utilized, as the required computational burden is prohibitivelyhigh. The ultimate goal of this Thesis is therefore to propose affordable mechanisms toflexibly and effectively share out the available resources in multicast wireless systems deployingOFDMA technology. Specifically, according to multicast system, it is assumed thatboth the BS and each user are equipped witha single antenna and the allocation unit is not the subcarrier,as in conventional OFDMA systems, but a set of contiguoussubcarriers, which is called chunk, in order to alleviate the heavy computational burden. An efficient algorithmis proposed whose aim is to maximize the total throughput subject to constraints on totalavailable power,BER over a chunk, and proportional data rates constraints among multicast groups. Simulation and complexity analysis are provided to support thebenefits of chunk-based resource allocation to multicast OFDMA systems with targeting proportional data rates among multicast groups

link adaptation in satellite lte networks

This paper investigates the impact of the Round Trip Propagation Delay (RTPD) in the satellite LTE air interface with the adoption of MIMO technology. The Satellite LTE air interface will provide global coverage and hence complement its terrestrial counterpart in the provision of LTE services to mobile users. A land mobile dual-polarized GEO satellite system has been considered for this work. The link adaption is an important module for the scheduling scheme and the satellite LTE network as a whole in order to make optimal scheduling decisions and effectively utilize the network resources respectively. However, the long RTPD experienced when Channel Quality Indicator (CQI) is reported from the User Equipment (UE) to the eNodeB via GEO satellite causes misalignment between the reported CQI at the eNodeB and the present CQI of the mobile user. The aim of this paper is to investigate the effect of the misalignment as a result of long RTPD through simulations and also investigate the effect of varying CQI reporting interval on the system performance of Satellite LTE network. The possibility of using a fixed CQI rather than an adaptive CQI is also investigated

Cooperative control of relay based cellular networks

PhDThe increasing popularity of wireless communications and the higher data requirements of new types of service lead to higher demands on wireless networks. Relay based cellular networks have been seen as an effective way to meet users’ increased data rate requirements while still retaining the benefits of a cellular structure. However, maximizing the probability of providing service and spectrum efficiency are still major challenges for network operators and engineers because of the heterogeneous traffic demands, hard-to-predict user movements and complex traffic models. In a mobile network, load balancing is recognised as an efficient way to increase the utilization of limited frequency spectrum at reasonable costs. Cooperative control based on geographic load balancing is employed to provide flexibility for relay based cellular networks and to respond to changes in the environment. According to the potential capability of existing antenna systems, adaptive radio frequency domain control in the physical layer is explored to provide coverage at the right place at the right time. This thesis proposes several effective and efficient approaches to improve spectrum efficiency using network wide optimization to coordinate the coverage offered by different network components according to the antenna models and relay station capability. The approaches include tilting of antenna sectors, changing the power of omni-directional antennas, and changing the assignment of relay stations to different base stations. Experiments show that the proposed approaches offer significant improvements and robustness in heterogeneous traffic scenarios and when the propagation environment changes. The issue of predicting the consequence of cooperative decisions regarding antenna configurations when applied in a realistic environment is described, and a coverage prediction model is proposed. The consequences of applying changes to the antenna configuration on handovers are analysed in detail. The performance evaluations are based on a system level simulator in the context of Mobile WiMAX technology, but the concepts apply more generally

Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

It is anticipated that mobile data traffic and the demand for higher data rates will increase dramatically as a result of the explosion of wireless devices, such as the Internet of Things (IoT) and machine-to-machine communication. There are numerous location-based peer-to-peer services available today that allow mobile users to communicate directly with one another, which can help offload traffic from congested cellular networks. In cellular networks, Device-to-Device (D2D) communication has been introduced to exploit direct links between devices instead of transmitting through a the Base Station (BS). However, it is critical to note that D2D and IoT communications are hindered heavily by the high energy consumption of mobile devices and IoT devices. This is because their battery capacity is restricted. There may be a way for energy-constrained wireless devices to extend their lifespan by drawing upon reusable external sources of energy such as solar, wind, vibration, thermoelectric, and radio frequency (RF) energy in order to overcome the limited battery problem. Such approaches are commonly referred to as Energy Harvesting (EH) There is a promising approach to energy harvesting that is called Simultaneous Wireless Information and Power Transfer (SWIPT). Due to the fact that wireless users are on the rise, it is imperative that resource allocation techniques be implemented in modern wireless networks. This will facilitate cooperation among users for limited resources, such as time and frequency bands. As well as ensuring that there is an adequate supply of energy for reliable and efficient communication, resource allocation also provides a roadmap for each individual user to follow in order to consume the right amount of energy. In D2D networks with time, frequency, and power constraints, significant computing power is generally required to achieve a joint resource management design. Thus the purpose of this study is to develop a resource allocation scheme that is based on spectrum sharing and enables low-cost computations for EH-assisted D2D and IoT communication. Until now, there has been no study examining resource allocation design for EH-enabled IoT networks with SWIPT-enabled D2D schemes that utilize learning techniques and convex optimization. In most of the works, optimization and iterative approaches with a high level of computational complexity have been used which is not feasible in many IoT applications. In order to overcome these obstacles, a learning-based resource allocation mechanism based on the SWIPT scheme in IoT networks is proposed, where users are able to harvest energy from different sources. The system model consists of multiple IoT users, one BS, and multiple D2D pairs in EH-based IoT networks. As a means of developing an energy-efficient system, we consider the SWIPT scheme with D2D pairs employing the time switching method (TS) to capture energy from the environment, whereas IoT users employ the power splitting method (PS) to harvest energy from the BS. A mixed-integer nonlinear programming (MINLP) approach is presented for the solution of the Energy Efficiency (EE) problem by jointly optimizing subchannel allocation, power-splitting factor, power, and time together. As part of the optimization approach, the original EE optimization problem is decomposed into three subproblems, namely: (a) subchannel assignment and power splitting factor, (b) power allocation, and (c) time allocation. In order to solve the subproblem assignment problem, which involves discrete variables, the Q-learning approach is employed. Due to the large size of the overall problem and the continuous nature of certain variables, it is impractical to optimize all variables by using the learning technique. Instead dealing for the continuous variable problems, namely power and time allocation, the original non-convex problem is first transformed into a convex one, then the Majorization-Minimization (MM) approach is applied as well as the Dinkelbach. The performance of the proposed joint Q-learning and optimization algorithm has been evaluated in detail. In particular, the solution was compared with a linear EH model, as well as two heuristic algorithms, namely the constrained allocation algorithm and the random allocation algorithm, in order to determine its performance. The results indicate that the technique is superior to conventional approaches. For example, it can be seen that for the distance of $d = 10$ m, our proposed algorithm leads to EE improvement when compared to the method such as prematching algorithm, constrained allocation, and random allocation methods by about 5.26\%, 110.52\%, and 143.90\%, respectively. Considering the simulation results, the proposed algorithm is superior to other methods in the literature. Using spectrum sharing and harvesting energy from D2D and IoT devices achieves impressive EE gains. This superior performance can be seen both in terms of the average and sum EEs, as well as when compared to other baseline schemes