Resource Allocation In Cognitive Radio Research Articles

Cognitive-Radio-Based Resource Management for Smart Transportation: A Sliding Mode Control Approach

Smart transportation is one of the key components of the smart city. A significant amount of data is produced by the numerous vehicles connected via wireless networks. This demands smart spectrum management for efficient data communication. The optimal bandwidth usage can be obtained by opportunistically sharing the spectrum among high-priority class primary and low-priority class secondary users’ vehicles. In this study, we develop a mathematical model to ensure effective resource allocation in Cognitive Radio based Smart Transportation Networks (CR-STN) considering the mobility, bandwidth constraint and interference models. Robust sliding mode control is synthesized using an exponential reaching law to avoid data packet congestion in CR-STN to ensure smart spectrum management. In addition, a dynamic event-triggered mechanism is incorporated with the sliding mode controller to reduce computational complexity and energy expenses. The effectiveness of the suggested controller has been validated by simulation results.

A branch-and-price approach to a variant of the cognitive radio resource allocation problem

Radio-frequency portion of the electromagnetic spectrum is a scarce resource. Cognitive radio (CR) technology has emerged as a promising solution to overcome the spectrum scarcity bottleneck. Through this technology, secondary users (SUs) sense the spectrum opportunities free from primary users (PUs), and opportunistically take advantage of these (temporarily) idle portions, known as spectrum holes. In this correspondence, we consider a variant of the cognitive radio resource allocation problem posed by Martinovic et al. in 2017. The distinguishing feature of this version of the problem is that each SU, due to its hardware limitations, imposes the requirement that the to-be-aggregated spectrum holes cannot be arbitrarily far from each other. We call this restriction as the Maximal Aggregation Range (MAR) constraint, and refer to this variant of the problem as the MAR-constrained hole assignment problem. The problem can be formalized as an NP-hard combinatorial optimization problem. We propose a novel binary integer linear programming (ILP) formulation to the problem. The number of constraints in this formulation is the number of spectrum holes plus the number of SUs. On the other hand, the number of binary decision variables in the formulation can be prohibitively large, as for each legitimate spectrum allocation to each SU, one variable is needed. Due to this difficulty, the resulting 0-1 ILP instances may not even be explicitly describable, let alone be solvable by an ILP solver. We propose a branch-and-price (B&P) framework to tackle this challenge. This framework is in fact a branch-and-bound procedure in which at each node of the search tree, we utilize the so-called (delayed) column generation technique for solving the LP relaxation of the corresponding subproblem. As evidenced by the numerical results, the LP relaxation bounds are very tight. This allows for a very effective pruning of the search space. Compared to the previously suggested formulations, the proposed technique can require much less computational effort.

A game theoretic power control and spectrum sharing approach using cost dominance in cognitive radio networks.

The wireless networks face challenges in efficient utilization of bandwidth due to paucity of resources and lack of central management, which may result in undesired congestion. The cognitive radio (CR) paradigm can bring efficiency, better utilization of bandwidth, and appropriate management of limited resources. While the CR paradigm is an attractive choice, the CRs selfishly compete to acquire and utilize available bandwidth that may ultimately result in inappropriate power levels, causing degradation in network’s Quality of Service (QoS). A cooperative game theoretic approach can ease the problem of spectrum sharing and power utilization in a hostile and selfish environment. We focus on the challenge of congestion control that results in inadequate and uncontrolled access of channels and utilization of resources. The Nash equilibrium (NE) of a cooperative congestion game is examined by considering the cost basis, which is embedded in the utility function. The proposed algorithm inhibits the utility, which leads to the decrease in aggregate cost and global function maximization. The cost dominance is a pivotal agent for cooperation in CRs that results in efficient power allocation. Simulation results show reduction in power utilization due to improved management in cognitive radio resource allocation.

Nash Bargaining in Resource Allocation for Cognitive Radio: A Review

This paper primarily concentrates on Nash bargaining (NB) which is extensively accepted in cognitive radio (CR) network for allocation of resources. The application domains and effectiveness of NB in wireless networks are discussed systematically. Afterward NB is formulated with step by step computation in sharing of resources for a unique scenario. The base station shares the resource of single primary user (PU) with a particular CR subject to constraint imposed by the PU. The utility function is designed that directly reflects the spectrum utilization of CR. The existence and uniqueness of NB solution is verified analytically. The theoretical and simulation analyses are done to find the competence of NB in sharing of resources to CR satisfying the prerequisites of PU. The variation of Jain’s fairness index with the data rate of CR signifies that the NB provides reasonably high-quality resource allocation to CR.

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

SummaryEffective use of unused licensed spectrum by secondary nodes without causing any harmful interference to the primary users is one of the challenging task, and in this paper, a detailed description of joint spectrum sensing and resource allocation in cognitive radio (CR) networks is discussed. The CR system utility is maximized by joint spectrum sensing and channel resource allocation scheme based on random walk grey wolf optimization (RW‐GWO) algorithm for frequency hopping cognitive radio‐based networks. In this paper, the nodes will sense the presence of primary users in the same channel and move to another channel in the guidance of a guide node (GN) if primary signal is detected in the channel. In order to achieve collision‐free communication, primary signals and secondary signals use the channels strategically and the mechanism is described in this paper. RW‐GWO is used to derive the optimum sensing and data transmission schedules. It selects the sensing nodes to sense the spectrum and other nodes take part in transmitting the data. GNs not only guide the nodes to hop to the next channel if the primary channel is detected, but also distribute the nodes in different available secondary hopping channels. Simulation results show that reliable spectrum sensing and efficient channel allocation can be achieved in our proposed algorithm.

Adaptive resources allocation algorithm based on modified PSO for cognitive radio system

Radio spectrum has become a rare resource due to the rapid development of wireless communication technique. Cognitive radio is one of important techniques to deal with this radio spectrum problem. But the resource allocation in cognitive radio also has its own issues, such as the flexibility of the allocation algorithm, the performance of resource allocation, and so on. In order to increase the flexibility of the allocation algorithm for cognitive radio, more and more researches are focusing on the evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO). Evolutionary algorithm can greatly improve the flexibility of the allocation algorithm for cognitive radio system in different communication scenarios, but the performances are relatively lower than the original mathematical methods. So in this paper, we proposed an adaptive resource allocation algorithm based on modified PSO for cognitive radio system to solve these problems. Modified particle swarm optimization (Modified PSO) has both genetic algorithm (GA) and particle swarm optimization (PSO)'s updating processes which makes this modified PSO overcame PSO's own disadvantages and keep advantages. Simulation results showed our proposed algorithm has enough flexibility to meet cognitive radio systems' requirements, and also has a better performance than original PSO.

Vertex Search based Energy-efficient Optimal Resource Allocation in Cognitive Radio ad hoc Networks

Cognitive Radio Ad Hoc Networks (CRAHN) is the infrastructure-less network model of Cognitive radios developed in an ad hoc manner. Regulating resource allocation in CRAHN is considered to be an energy constrained problem. Many researches have been performed for allocating spectrum in an efficient way using various protocols. In this paper, the Spectrum-Map-Empowered Opportunistic Routing (SMOR) model has been utilized as the fundamental model for routing and an energy efficient optimal spectrum allocation solution is provided. In the proposed model, the previously modified SMOR model is enhanced for the main objective of energy efficient and optimal resource allocation using Vertex search algorithm with a gradient-based approximation. Initially, the resource allocation problem is modelled into a non-convex optimization problem. The power allocation, data rate adaptation, channel allocation, and user scheduling policies are optimized for maximization of the energy efficiency during data transmission. The proposed Vortex search algorithm resolves this optimization problem by determining the training interval for the channel estimation and power consumption. The experimental results prove that the proposed Vertex search based modified SMOR (VS-M-SMOR) model provides efficient routing with energy efficient optimal resource allocation.

Cooperative Resource Allocation in Cognitive Radio Networks With Wireless Powered Primary Users

This letter focuses on cooperative resource allocation in multi-carrier cognitive radio (CR) networks where the primary user (PU) is wirelessly powered by the energy harvested from the signals transmitted by the secondary user (SU) for its own information transfer. We adopt the two-stage harvest-then-transmit protocol, where the wireless power transfer for the PU is executed first and then the information transfer for the PU takes place. Specifically, two schemes, namely subcarrier sharing (SS) and subcarrier exclusivity, are considered for coordinating the information transfer of the PU and the SU in the second stage. The resource allocation problems to maximize the SU rate subject to the PU minimum rate constraint are investigated. It is shown that the SS scheme achieves higher SU rate than the SE scheme at the cost of higher computation time when the PU minimum rate constraint is stringent and the number of subcarriers is large.

Dynamic resource allocation for cognitive radio based smart grid communication networks

Dynamic resource allocation for cognitive radio based smart grid communication networks

Interference-aware resource allocation algorithm in multicarrier-based cognitive radio networks

Interference between users is a significant issue for resource allocation in cognitive radio (CR) networks. The interference induced by the secondary users (SUs) to the primary users (PUs) derives from two aspects: out-of-band leakage (OOBL), spectrum sensing error (SSE). Filter bank multicarrier (FBMC) has small out-of-band leakage and high spectral efficiency in comparison with that of the orthogonal frequency division multiplexing (OFDM). In this paper, a resource allocation algorithm with considering SSEs in CR networks is proposed. The interference model is established first and then the proposed algorithm is divided into two steps, power is allocated to SUs under both interference constraints and total power budget. Simulation results based on FBMC and OFDM systems show that the proposed algorithm causes less interference to the PUs than that of the algorithm without considering OOBL, the performance of FBMC in interference and throughput is better than OFDM in CR networks.

Joint Sensing Adaptation and Resource Allocation for Cognitive Radio with Imperfect Sensing

In cognitive radio networks, imperfect spectrum sensing causes harmful interferences or low utilization of unused bands, depending on an access policy of the secondary system. Since these losses have a trade-off relationship, it is hard to decide proper sensing and access policies for the licensed bands. To deal with this problem, we propose a joint algorithm for sensing adaptation and opportunistic resource allocation. The joint optimization problem minimizes the total expected cost of the losses and utilities which are likely generated for the secondary system. To protect the primary system and ensure the secondary system's utility, we employ an average interference threshold constraint and a QoS constraint for the secondary system. Two types of average secondary utilities are considered for the QoS constraint; one is averaged over only transmitted frames and the other is averaged over total frames. Based on energy detection, we derive feasible ranges of sensing threshold to satisfy the constraints. Simulation results show that the proposed scheme dynamically adapts sensing threshold depending on the network environment and minimizes the total cost of the secondary system. In addition, we investigate the performance of the proposed scheme with the two types of the secondary utility.

Channel Gain Map Tracking via Distributed Kriging

A collaborative algorithm is developed to estimate the channel gains of wireless links in a geographical area. The spatiotemporal evolution of shadow fading is characterized by judiciously extending an experimentally verified spatial-loss field model. Kriged Kalman filtering (KKF), which is a tool with widely appreciated merits in spatial statistics and geosciences, is adopted and implemented in a distributed fashion to track the time-varying shadowing field using a network of radiometers. The novel distributed KKF requires only local message passing yet achieves a global view of the radio frequency environment through consensus iterations. Numerical tests demonstrate superior tracking accuracy of the collaborative algorithm compared with its noncollaborative counterpart. Furthermore, the efficacy of the global channel gain knowledge obtained is showcased in the context of cognitive radio resource allocation.

Cognitive radio resource allocation based on combined chaotic genetic algorithm

The combined chaotic genetic algorithm for cognitive radio resource allocation is proposed, and corresponding combined chaotic sequence generator is designed. Simulations are conducted by using the combined chaotic genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, and the simple genetic algorithm, thereby analyzing the multi-users, cognitive radio resource allocation. The results show that the combined chaotic genetic algorithm has advantages of fast convergence rate, vast search space and global convergence. The combined chaotic genetic algorithm has better performance than the other three algorithms in terms of cognitive radio resource allocation, there by reducing the bit error rate and the transmission power consumption of the system. Besides, it also has a faster convergence rate.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

Statistical resource allocation for multi-band Cognitive Radio systems

A recent development in wireless communications is Cognitive Radio (CR) technology, an innovative design approach which allows the realization of optimal allocation of the scarce radio resources such as the spectrum. Successful resource allocation in CR systems has to overcome the uncertainty of spectrum bands availability as well as the chaotic wireless propagation environment. Previously, most research in resource allocation in CRs has mainly concentrated on the spectrum opportunity discovery aspect while the robust QoS performance problem has remained largely unexplored. In this paper, we jointly consider the power control and spectrum band discovery problems under uncertain CR operative environments. This problem setting is a direct analogy of the portfolio optimization (PO) concept. In PO, once the variance of return is known, then an investor can use the information to find a wealth allocation strategy so as to minimize the investment risk. Similarly, in uncertain CR scenarios, once the variance of a QoS parameter (e.g., throughput) is known, then a power allocation strategy can be obtained leading to reliable communication. The resulting power strategy also marks out the subbands to be used — essentially achieving soft spectrum sensing. In this work, we shall use the concept of portfolio optimization to jointly achieve power control and subband allocation over uncertain CR environments. The limitations of the approaches are investigated through the sensitivity analysis of the solutions obtained. A raw data processing approach will also be given leading to an alternative algorithm for stable data processing. Numerical results are presented to demonstrate the potential of the proposed approaches.

Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.