A composite channel hopping algorithm for blind rendezvous in heterogeneous cognitive radio networks

The multichannel rendezvous problem in cognitive radio networks (CRNs) has been a hot research topic lately. One of the most challenging settings of the multichannel rendezvous problem is the oblivious rendezvous problem in heterogeneous CRNs, where: 1) there are no distinguishable roles of users; 2) users’ clocks are not synchronized; 3) users may have different available channel sets; and 4) there is no universal labelling of the channels. Most existing works in the literature focus on achieving deterministic bounds for the maximum conditional time-to-rendezvous (MCTTR) and perform poorly (in comparison with the random algorithm) for the expected time-to-rendezvous (ETTR) due to the “stay” modes in these works. In this paper, we tackle the oblivious rendezvous problem by taking both MCTTR and ETTR into consideration. In order to have guaranteed rendezvous, we only make two assumptions: (A1) there is at least one common available channel and (A2) there is a unique ID for each user. We first propose a new class of strong symmetrization mappings to encode user IDs for speeding up the rendezvous process. Two efficient and yet simple encoding schemes are proposed by utilizing the $\cal C$ -transform and the existing 4B5B encoding. Based on the new class of strong symmetrization mappings, we propose the two-prime modular clock algorithm for the two-user rendezvous problem. The ETTR of our algorithm is almost the same as that of the random algorithm and its MCTTR is also comparable to the best existing bound. We also extend the two-prime modular clock algorithm for multiuser rendezvous by proposing the stick together algorithm and the spread out algorithm. One interesting finding for the multiuser rendezvous problem is that the spread out algorithm is not always better than the stick together algorithm as commonly claimed in the literature.

In this paper, we consider heterogeneous cognitive radio networks (CRNs) comprising primary channels (PCs) with heterogeneous characteristics and secondary users (SUs) with various sensing and reporting qualities for different PCs. We first define the opportunity as the achievable total data rate and its cost as the energy consumption caused from sensing , reporting , and channel switching operations and formulate a joint spectrum discovery and energy efficiency objective to minimize the energy spent per unit of data rate. Then, a mixed integer non-linear programming problem is formulated to determine: 1) the optimal subset of PCs to be scheduled for sensing; 2) the SU assignment set for each scheduled PC; and 3) sensing durations and detection thresholds of each SU on PCs it is assigned to sense. Thereafter, an equivalent convex framework is developed for specific instances of the above combinatorial problem. For comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy, and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and is shown to perform very close to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs, and sensing qualities.

Heterogeneous cognitive wireless networks (HeCoNets)) are consisted of macrocells that are overlaid by small cells (e.g, femtocells, picocells). These small cells operate over the cognitive radio paradigm. In this paper, we consider a cooperative model in the uplink of HetCoNets, that includes picocell and famtocells networks that are using unlicensed channels from the macrocesll network. In our cooperative model, cognitive picocell users' equipments (CPUEs) and cognitive femtocell users (CFUEs) get incentives from cooperating with each other to improve the unlicensed channels usage and mitigate inter-tier and intra-tier interference while maximizing sum-rate of users in the HetCoNet. We apply a coalition game approach in which CPUEs and CFUEs are considered as players of the game. We have intensively simulated the proposed idea in matlab. Our simulation results show the effectiveness of our proposed compared with non-cooperative model.

End-to-end utility-based resource allocation for multi-RAT nodes in heterogeneous cognitive wireless networks

In cognitive radio (CR) networks, a pair of CR nodes have to ``rendezvous'' on a common channel for link establishment. Channel hopping (CH) protocols have been proposed for creating rendezvous over multiple channels to reduce the possibility of rendezvous failures caused by the detection of primary user signals. Rendezvous within a minimal bounded time over multiple channels is a challenging problem in heterogeneous CR networks where two CR nodes may have asynchronous clocks, different sensing capabilities, no common universal channel set, and heterogeneous channel index systems. In this paper, we present a systematic approach using group theory for designing CH protocols that guarantee the maximum number of rendezvous channels and the minimal time-to-rendezvous (TTR) in heterogeneous environments. We derive the minimum upper bound of TTR, and propose two types of rendezvous protocols that are independent of environmental heterogeneity. Analytical and simulation results show that these protocols are resistant to rendezvous failures under various network conditions.

As increasing number of cognitive radio network (CRN) standards are developed in TV White Spaces band with incompatible communication patterns, heterogeneous CRNs coexistence problem could not be avoided. However, most solutions on this problem have not considered CRNs’ actual data transmission demands and weighted fairness simultaneously. Therefore, we would like to introduce a novel On-demand ecological Species Competition based HEterogeneous networks coexistence MEchanism (O-SCHEME) in this paper. Inspired by ecology species competition model, O-SCHEME utilizes an ecology based spectrum allocation mechanism for guaranteeing heterogeneous CRNs’ spectrum shares weighted fairness. And by employing CRNs’ communication spectrum requirement constraints, actual data transmission needs could be satisfied without wasted communication resources. Through both theoretical and simulation analyses, we demonstrate that O-SCHEME can achieve stable and fair spectrum allocations among coexisting networks with high communication efficiency.

Recently, tremendous utilization of wireless networks has led to sever scarcity of radio spectrum resources. TV White Spaces (TVWSs) as novel bands enabling Cognitive Radio (CR) technology to improve spectrum resources utilization, have attracted significant standardisation efforts such as IEEE 802.11af, IEEE 802.16h and 802.22. As these heterogeneous networks may operate on the same channels of TVWSs, network coexistence problem cannot be avoided and is particularly challenging given the heterogeneous MAC/PHY layer protocols and operation parameters (e.g., tx power) employed in coexisting networks. In this paper, we develop a coexistence mechanism called ecological Species Competition based HEterogeneous networks coexistence MEchanism (SCHEME). Inspired by ecology based species competition model, SCHEME uses an ecological spectrum allocation method to assign available spectrums. Through both theoretical and simulation analysis, we demonstrate that SCHEME can achieve stable and fair spectrum allocation among coexisting networks.

In this paper, we study the problem of jointing power and rate optimization in multi-channel Heterogeneous Cognitive Radio Networks (H-CRNs), where the Leader-Follower relationship between Primary Users (PUs) in macrocells and Secondary Users (SUs) in femtocells is modeled as a Stackelberg game. We formulate the problem of power allocation in primary networks as a Non-Cooperative Game, while formulating the jointing power and rate optimization in cognitive networks as a Market Competition Equilibrium problem. Furthermore, we propose a Primary networks and Cognitive networks Joint Resource Allocation (PCJRA) algorithm to maximize the utilities of SUs subject to the maximum rate requirements for PUs. Theoretical analysis and simulation results demonstrate both the feasibility of the multi-channel H-CRNs model and the effectiveness of the proposed PCJRA algorithm.

This paper aims at maximizing the sum rate of secondary users (SUs) in Orthogonal Frequency Division Multiplexing (OFDM)-based Heterogeneous Cognitive Radio (CR) Networks by considering some practical limitations such as various traffic demands of SUs, interference constraint and imperfect spectrum sensing. The CR system operates in a slotted mode, in which the SUs have no power supplies and harvest energy from ambient radio signal. Assuming SUs operate in a time switching fashion, the time slot is partitioned into three non-overlapping parts devoted for energy harvesting, spectrum sensing and data transmission. Then, the general problem of sum rate maximization is formulated as a mixed integer programming task. Since this problem is intractable, we propose a sub-channel allocation scheme based on a factor called Energy Figure of Merit to approximately satisfy SUs' rate requirements. Then, the integer constraints of the optimization problem are removed and the problem can be reduced to a convex optimization task. Considering the trade-off between fractions of the time slot, we focus on finding the optimal structures of SUs that maximize the total throughput while guaranteeing the rate requirements of both real-time (RT) and non-real-time (NRT) SUs. The numerical results validate the effectiveness and efficiency of our sub-channel allocation method as well as performance gain of the optimal structure.

In this paper, a sequential channel sensing algorithm for multichannel heterogeneous Cognitive Radio Networks (CRNs) is studied. More specifically, a new transmission algorithm is introduced and its performance is evaluated via simulations. In the proposed algorithm, referred to as “Distributed algorithm”, we assume that the secondary users select their transmission channels without coordination by a centralized entity. In evaluating the performance of the proposed algorithm, we considered the likelihood of false alarm and missed detection. Our event-driven simulations results have shown that the new transmission algorithm we have introduced improve the average secondary Mbits transmitted in each time slot, which are considerably higher than the corresponding results of (i) the “γ-persistent strategy” introduced in [7] and (ii) the p-persistent random access sequential channel sensing algorithm (PPRA) for multichannel CRNs introduced in [11].

In this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield–Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.

Cognitive radio system consists of multiple wireless accesses that cover overlapping area, and cognitive terminals that use one or more of the wireless accesses simultaneously. In cognitive radio systems, secondary users can share the spectrum with the primary user as long as the quality of service (QoS) of the primary system is guaranteed. However, the system throughput of the cognitive system will be limited when the QoS requirement is stringent. We had proposed the architecture of the cognitive radio system, the inter-system handover protocols and packet format designed to achieve fast handover among the radio systems.

Cooperative Spectrum Sensing (CSS) is the critical component of the cognitive radio technology, which could relieve the shortage of spectrum recourses. The double threshold based CSS scheme was adopted to deal with the unreliability when the original energy value locates around the traditional single threshold. We utilize the dynamic double threshold based energy detection scheme, in which it can adjust the double threshold properly to respond the transformation of channel condition. This proposed dynamic double threshold based scheme could be verified by analyzing the formula and simulation, and it achieves the better performance compared with classical double threshold based CSS scheme.

Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks