Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.

Utility Fair Resource Allocation Based on Game Theory in OFDM Systems

In this paper, we propose a distributed adaptive interference coordination algorithm for a practical orthogonal frequency division multiple access (OFDMA)-based mobile cellular systems. The designed algorithm can achieve an efficient frequency reuse for any user distribution and traffic load. Since no a priori frequency planning is required, the minimal coordination between base stations is also achieved. Moreover, the proposed algorithm can adapt to different network interference conditions and is power-saving in some degree. We also develop a way to decompose a multi-cell optimization problem into distributed single-cell optimization problems, which greatly reduces the computational complexity.

Distributed Adaptive Subchannel and Power Allocation for Downlink OFDMA with Inter-Cell Interference Coordination

This paper studies the fairness among the primary users PUs and the secondary users SUs for resource allocation in cognitive radio systems. We propose a novel co-opetition strategy based on the Kalai-Smorodinsky bargaining solution to balance the system efficiency and the fairness among users. The strategy formulates the spectrum sharing problem as a nonlinear and integral sum utility maximization subject to a set of constraints describing the co-opetition among the PUs and the SUs. Then, we solve the maximization problem by proposing a heuristical method that consists of four steps: multi-PU competition, PU's subcarrier contribution, multi-SU competition, and SU's subcarrier contribution. Extensive simulation results are presented by comparing the co-opetition strategy with several conventional ones, including the Kalai-Smorodinsky bargaining solution, sum rate maximization as well as the Max-Min. Results indicate that the co-opetition strategy can jointly balance the system efficiency and fairness in multiuser resource allocation, as it is able to support more satisfied users and in the meanwhile improve the utility of those unsatisfied. Moreover, the co-opetition can help enable the coexistence of the PUs and the SUs in cognitive radio systems. Copyright © 2013 John Wiley & Sons, Ltd.

Cooperative bargaining solution for efficient and fair spectrum management in cognitive wireless networks

This paper deals with radio resource allocation in fourth generation (4G) wireless mobile networks based on Orthogonal Frequency Division Multiple Access (OFDMA) as
an access method. In IEEE 802.16 m standard, a contiguous method for subchannel construction is adopted in order to reduce OFDMA system complexity. In this context, we propose a new subchannel gain computation method depending on frequency responses dispersion. This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. In heterogeneous data traffics, we study scheduling in order to provide delay guaranties to real-time services, maximize throughput of non-real-time services while ensuring fairness to users. We compare performances to recent existing algorithms in OFDMA systems showing that proposed schemes provide lower complexity, higher total system capacity, and fairness among users.

/pdf/a-gain-computation-enhancements-resource-allocation-for-27hi4nxcll.pdf

A Gain-Computation Enhancements Resource Allocation for Heterogeneous Service Flows in IEEE 802.16 m Mobile Networks

The present invention presents a mechanism to incorporate quality of service constraints imposed by higher layers into the allocation of resources performed preferably in a multi-user-multi-carrier MIMO system. Quality of service constraints are given as fractions of rates that each user should obtain out of the resulting sum rate. Allocation of spatial and frequency dimensions is carried out according to the cooperative zero-forcing with successive encoding and successive allocation methods (CZF-SESAM). The inventive approach basically constitutes a means of forming allocation in each iteration of the CZF-SESAM taking into account prescribed quality of service constraints. The procedure consists of three basic steps. After all potential beam-forming vectors have been computed on each subcarrier at a certain iteration, first, preferably for each user, only the beam-forming vector is on each subcarrier selected that yields the highest channel gain. In the second step, considering the subchannel selected in the first step, capacity values are computed for every user and based on these capacity values and the quality of service constraints, the spectral share of each user is computed. Finally, the particular assignation of space-frequency dimensions is carried out by departing from a sum rate optimum allocation and modifying the initial allocation according to a minimum energy loss principle, in order to comply the spectral shares computed in a second step.

Method and apparatus for subchannel allocation

In this paper, we propose a new and rather simple grouped-subcarrier allocation algorithms with proportional fairness among users in downlink OFDM transmission. The proposed algorithm tries to minimize the required transmit power while satisfying the rate requirement and BER constraint of each user. Subcarrier and power allocation are performed in two steps. We are mainly focusing on the subcarrier allocation step. First subcarriers are grouped to reduce the computational complexity of the allocation algorithm. Then we employ proportional constraints in terms of rate ratios to assure each user to achieve the target data rate in advance. Simulation results show that this low complexity resource allocation scheme achieves better performance than the fixed frequency division approach and shows comparable performance compared to previously derived suboptimal resource distribution schemes. It is also shown that with rate constraints the capacity is distributed more fairly and rationally among users.

A grouped and proportional-fair subcarrier allocation scheme for multiuser OFDM systems

Two important considerations in designing a HS-DPA system are algorithms for the selection of coding and modulation schemes based on the channel quality of the link and algorithms for the selection of the user to whom a particular slot is assigned. In this paper, our special focus is put on the mapping criteria between signal-to-interference-and-noise ratio (SINR) and modulation and coding scheme (MCS), taking into account frame error rate (FER) and HARQ information. We propose a simple criterion to optimize the mapping between SINR and MCS to maximize the throughput. Supposed that the max transmission times is pre-determined, based on the SINR estimated, the FER and the probability of each retransmission contributing to the throughput are calculated. Simulation results comparing performance of the proposed methods and traditional counterparts are presented, it is shown that our new algorithm outperforms conventional methods and is more easily to be implemented.

A novel mapping criterion scheme for MCS selection in CDMA based HARQ systems

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Dan Xu

Author Tools

Chat about Author

Papers

A grouped and proportional-fair subcarrier allocation scheme for multiuser OFDM systems

A novel mapping criterion scheme for MCS selection in CDMA based HARQ systems