Resource Optimization with Flexible Numerology and Frame Structure for Heterogeneous Services

We explore the potential of optimizing resource allocation with flexible numerology in frequency domain and variable frame structure in time domain, in presence of services with different types of requirements. We analyze the computational complexity and propose a scalable optimization algorithm based on searching in both the primal space and dual space that are complementary to each other. Numerical results show significant advantages of adopting flexibility in both time and frequency domains for capacity enhancement and meeting the requirements of mission critical services.Comment: 4 page

Online Knapsack Problem under Expected Capacity Constraint

Online knapsack problem is considered, where items arrive in a sequential fashion that have two attributes; value and weight. Each arriving item has to be accepted or rejected on its arrival irrevocably. The objective is to maximize the sum of the value of the accepted items such that the sum of their weights is below a budget/capacity. Conventionally a hard budget/capacity constraint is considered, for which variety of results are available. In modern applications, e.g., in wireless networks, data centres, cloud computing, etc., enforcing the capacity constraint in expectation is sufficient. With this motivation, we consider the knapsack problem with an expected capacity constraint. For the special case of knapsack problem, called the secretary problem, where the weight of each item is unity, we propose an algorithm whose probability of selecting any one of the optimal items is equal to $1-1/e$ and provide a matching lower bound. For the general knapsack problem, we propose an algorithm whose competitive ratio is shown to be $1/4e$ that is significantly better than the best known competitive ratio of $1/10e$ for the knapsack problem with the hard capacity constraint.Comment: To appear in IEEE INFOCOM 2018, April 2018, Honolulu H

PSO Algorithm Based Resource Allocation for OFDM Cognitive Radio

With the development of remote correspondences, the issue of data transmission lack has turned out to be more conspicuous. Then again, to sense the presence of authorized clients, range detecting procedures are utilized. Vitality recognition, Matched channel identification and Cyclo-stationary component location are the three ordinary techniques utilized for range detecting. However there are a few downsides of these strategies. The execution of vitality indicator is helpless to instability in noise power. Coordinated channel range detecting strategies require a devoted collector for each essential client. Cyclo-stationary element Detection requires parcel of calculation exertion and long perception time. This proposition talks about the routine vitality location strategy and proposed enhanced vitality identification technique utilizing cubing operation. Additionally, cyclic prefix based range detecting is talked about in this theory. Scientific Description of vitality location and cyclic prefix based range detecting strategies is likewise delineated for fading channels

Dynamic Resource Allocation Algorithms for Cognitive Radio Systems

Cognitive Radio (CR) is a novel concept for improving the utilization of the radio spectrum. This promises the efficient use of scarce radio resources. Orthogonal Frequency Division Multiplexing (OFDM) is a reliable transmission scheme for Cognitive Radio Systems which provides flexibility in allocating the radio resources in dynamic environment. It also assures no mutual interference among the CR radio channels which are just adjacent to each other. Allocation of radio resources dynamically is a major challenge in cognitive radio systems. In this project, various algorithms for resource allocation in OFDM based CR systems have been studied. The algorithms attempt to maximize the total throughput of the CR system (secondary users) subject to the total power constraint of the CR system and tolerable interference from and to the licensed band (primary users). We have implemented two algorithms Particle Swarm Algorithm(PSO) and Genetic Algorithm(GA) and compared their results