Dynamic Resource Allocation in Cognitive Radio Networks: A Convex Optimization Perspective

This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.Comment: to appear in IEEE Signal Processing Magazine, special issue on convex optimization for signal processin

Cognitive Radio : A Solution for Issues in Network Convergence

While planning for strategic communication no nation or organisation can ignore the problems of dynamic radio resource allocation and interference. Among lot number of technologies as a solution Cognitive Radio (CR) is the best one, which helps to overcome the problem of interference, and also allow efficient and dynamic radio resource allocation. Applying Cognitive Radio Networks (CRN) to strategic communications as a solution, this roadmap has been proposed by nations and organizations. For fast network deployment it is mandatory to overcome traffic problem in spectrum, increase communication reshaping mechanism and also strategic radio should act as multi-functional Radio-Frequency(RF) Unit, these are the themes for which CRN is the choice. Wireless Sensor Networks(WSNs) present day have many challenges, if it get clubbed with CR, many problems can be solved. Theme of our research is to empower CRN, so that it will help to solve above problems and also help to manage traffic in network convergence services without ignoring or compromising security

Priority-based reserved spectrum allocation by multi-agent through reinforcement learning in cognitive radio network

Research in cognitive radio networks aims at maximized spectrum utilization by giving access to increased users with the help of dynamic spectrum allocation policy. The unknown and rapid dynamic nature of the radio environment makes the decision making and optimized resource allocation to be a challenging one. In order to support dynamic spectrum allocation, intelligence is needed to be incorporated in the cognitive system to study the environment parameters, internal state, and operating behaviour of the radio and based on which decisions need to be made for the allocation of under-utilized spectrum. A novel priority-based reserved allocation method with a multi-agent system is proposed for spectrum allocation. The multi-agent system performs the task of gathering environmental artefacts used for decision making to give the best of effort service in this adaptive communication

Adaptive resource allocation for cognitive wireless ad hoc networks

Widespread use of resource constrained wireless ad hoc networks requires careful management of the network resources in order to maximize the utilization. In cognitive wireless networks, resources such as spectrum, energy, communication links/paths, time, space, modulation scheme, have to be managed to maintain quality of service (QoS). Therefore in the first paper, a distributed dynamic channel allocation scheme is proposed for multi-channel wireless ad hoc networks with single-radio nodes. The proposed learning scheme adapts the probabilities of selecting each channel as a function of the error in the performance index at each step. Due to frequent changes in topology and flow traffic over time, wireless ad hoc networks require a dynamic routing protocol that adapts to the changes of the network while allocating network resources. In the second paper, approximate dynamic programming (ADP) techniques are utilized to find dynamic routes, while solving discrete-time Hamilton-Jacobi-Bellman (HJB) equation forward-in-time for route cost. The third paper extends the dynamic routing to multi-channel multi-interface networks which are affected by channel uncertainties and fading channels. By the addition of optimization techniques through load balancing over multiple paths and multiple wireless channels, utilization of wireless channels throughout the network is enhanced. Next in the fourth paper, a decentralized game theoretic approach for resource allocation of the primary and secondary users in a cognitive radio networks is proposed. The priorities of the networks are incorporated in the utility and potential functions which are in turn used for resource allocation. The proposed game can be extended to a game among multiple co-existing networks, each with different priority levels --Abstract, page iv

A Review paper based on spectrum sensing techniques in Cognitive Radio Networks

Natural frequency spectrum is scared resource; the efficient use of it can only accommodate the need of future computing world. But efficient use of it is not possible within the existing system, where the allocation of spectrum is done based on fixed spectrum access (FSA) policy. Many survey shows that it leads to under use of spectrum. For efficient utilization of spectrum innovative techniques is needed. using Dynamic spectrum access (DSA) policy we can exploiting the available spectrum, For given purpose Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing opportunistic usage of the frequency bands that are not heavily occupied by licensed users. This paper presents the study of different spectrum sensing techniques of cognitive radio networks. As we know Cognitive radio is a form of wireless communication where radio transceiver intelligently detects which spectrums are free which are not. After this it occupies the vacant one while avoiding busy one spectrum. Cognitive radios promote open spectrum allocation which is a clear departure from traditional command and control allocation schemes for radio spectrum usage. In short, they allow the formation of “infrastructure-less” collaborative network clusters—cognitive radio networks. However, how to detect free spectrum we have to use the spectrum sensing techniques, here we are describing all the spectrum sensing techniques and Finally concluded that cooperative sensing is better than Non-Cooperative sensing for primary user (PU) signal with  low SNR value. Keywords- CRN, FSA, PU, DSA, SU, SNR.

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

Joint Optimization of Detection Threshold and Resource Allocation in Infrastructure-based Multi-band Cognitive Radio Networks

[EN] Consider an infrastructure-based multi-band cognitive radio network (CRN) where secondary users (SUs) opportunistically access a set of sub-carriers when sensed as idle. The carrier sensing threshold which affects the access opportunities of SUs is conventionally regarded as static and treated independently from the resource allocation in the model. In this article, we study jointly the optimization of detection threshold and resource allocation with the goal of maximizing the total downlink capacity of SUs in such CRNs. The optimization problem is formulated considering three sets of variables, i.e., detection threshold, sub-carrier assignment and power allocation, with constraints on the PUs¿ rate loss and the power budget of the CR base station. Two schemes, referred to as offline and online algorithms respectively, are proposed to solve the optimization problem. While the offline algorithm finds the global optimal solution with high complexity, the online algorithm provides a close-to-optimal solution with much lower complexity and realtime capability. The performance of the proposed schemes is evaluated by extensive simulations and compared with the conventional static threshold selection algorithm specified in the IEEE 802.22 standard.This work is supported by the EU FP7 S2EuNet project (247083), the National Nature Science Foundation of China (NSF61121001), Program for New Century Excellent Talents in University (NCET) and the Spanish Ministry of Education and Science under project (TIN2008-06739-C04-02).Shi, C.; Wang, Y.; Wang, T.; Zhang, P.; Martínez Bauset, J.; Li, FY. (2012). Joint Optimization of Detection Threshold and Resource Allocation in Infrastructure-based Multi-band Cognitive Radio Networks. 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Delay Sensitive Communications over Cognitive Radio Networks

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to dynamic resource availability because of the licensed users' activities. In this paper, we study the optimal admission control and channel allocation decisions in cognitive overlay networks in order to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performance with low complexities.Comment: 11 pages, 8 figure