Per User Throughput in Large Wireless Networks

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.

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.

In this paper, we propose a method to calculate the throughput of multiple uncooperative cognitive radio users. We use a bank of Markov models to derive the fraction of time that each user spends in successful channel access. The mutual interference among the users is modeled into the transitional probabilities of the Markov models, which makes it tractable to calculate multiple user throughput in large cognitive radio networks (CRN). In addition, to evaluate the level of greenness of the multiple user CRN we develop a method to optimize the ideal capacity of the fully cooperative CRN. This method relies on solving the sum-of-ratios linear fractional programming for optimization. Simulations are conducted to show the big gap between the uncooperative CRN performance and the ideal performance.

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.

It has been a challenge to develop efficient algorithms for relay selection and relay power optimization in arbitrarily large multi-hop wireless networks. In this paper, considering Gaussian networks with half-duplex decode-and-forward relays, we develop a practical network-wide signal processing procedure with which the relay's data rate is not degraded by mutual interference. Multi-hop relaying is thus immune to mutual interference. Then, we develop an algorithm to find approximately the optimal hop count and the optimal relays for source-destination transmission rate maximization. With a quadratic complexity O(N2), where N is the network size, this algorithm is efficient for arbitrarily large wireless networks. More interestingly, this algorithm is similar to the well-known Dijkstra's algorithm of wired networks.

This paper investigates the potential gain of cooperation in large wireless networks with multiple sources and relays, where the nodes form an homogeneous Poisson point process. The source nodes may choose their nearest neighbor from the set of inactive nodes as their relay. Although cooperation can potentially lead to significant improvements on the asymptotic error probability of a communication pair, relaying causes additional interference in the network, increasing the average noise. We address the basic question: how should source nodes optimally balance cooperation vs. interference to guarantee reliability in all communication pairs. Based on the decode-and-forward (DF) scheme at the relays, we derive closed-form approximations to the upper bounds on the error probability, averaging over all node positions. Surprisingly, in the small outage probability regime, there is an almost binary behavior that dictates — depending on network parameters — the activation or not of all relay nodes.

The evaluation and assessment of the design of large wireless networks is an important problem in numerous applications. Direct simulation is a traditional approach for studying such networks but is severely limited in its utility as the size of the network increases. This necessitates other means for studying large networks, one of which is the modeling of large networks with continuum models. In this paper, we introduce nonlinear partial differential equations whose solutions approximate the expected behavior of large networks governed by probabilistic communication rules. The relative speed at which solutions can be obtained from the continuum models allows for the investigation of routing protocols and communication limitations due to interference—a feat that is not feasible via simulation methods. Specifically, we investigate the effects of a directed diffusion routing protocol and explore communication limitations in an interference-sensitive network. Network design studies using the approximating continuum models are then presented.

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.

The throughput of large wireless networks with regular topologies is analyzed under two medium-access control schemes: synchronous array method (SAM) and slotted ALOHA. The regular topologies considered are square, hexagon, and triangle. Both nonfading channels and Rayleigh fading channels are examined. Furthermore, both omnidirectional antennas and directional antennas are considered. Our analysis shows that the SAM leads to a much higher network throughput than the slotted ALOHA. The network throughput in this paper is measured in either bits-hops per second per Hertz per node or bits-meters per second per Hertz per node. The exact connection between the two measures is shown for each topology. With these two fundamental units, the network throughput shown in this paper can serve as a reliable benchmark for future works on network throughput of large networks.

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 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 consider CSMA policies for scheduling packet transmissions in multihop wireless networks with one-hop traffic. The main contribution of the paper is to propose a novel CSMA policy, called Unlocking CSMA (U-CSMA), that enables to obtain both high throughput and low packet delays in large wireless networks. More precisely, we show that for torus interference graph topologies with one-hop traffic, U-CSMA is throughput optimal and achieves order-optimal delay. For one-hop traffic, the delay performance is defined to be order-optimal if the delay stays bounded as the network-size increases. Simulations that we conducted suggest that (a) U-CSMA is throughput-optimal and achieves order-optimal delay for general geometric interference graphs and (b) that U-CSMA can be combined with congestion control algorithms to maximize the network-wide utility and obtain order-optimal delay. To the best of our knowledge, this is the first time that a simple distributed scheduling policy has been proposed that is both throughput/utility optimal and achieves order-optimal delay.

Cognitive Radio (CR) networks have become an important component of the modern communication infrastructure due to their capability of improving spectrum usage efficiency by exploiting channels opportunistically. In CR networks, the network topology changes very frequently because of the temporarily available channels and dynamic transmitting parameters (e.g. transmission power and transmitting frequency), which may even result in network disconnectivity from time to time. Hence an interesting and open question is that: are there bounds on end-to-end delay between a source-destination pair with Euclidean distance d apart in such networks? These bounds are required for time-critical applications. This paper first investigates the nature of topology-incurred end-to-end delay in large multi-channel CR networks and then identifies the conditions under which the asymptotic topology-incurred delay scales linearly with the Euclidean distance (d); that is, the conditions under which the end-to-end delay is bounded. The results in this paper are validated through extensive simulations and can advance our understanding of CR network performance.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.