Primary Interference Constraint Research Articles

Secrecy Performance of Cooperative Cognitive AF Relaying Networks With Direct Links Over Mixed Rayleigh and Double-Rayleigh Fading Channels

This paper investigates the secrecy performance of an underlay cooperative cognitive relaying network, wherein a secondary source vehicle communicates with a fixed secondary destination terminal via a direct link and with the assistance of a secondary amplify-and-forward relay vehicle in the presence of a passive secondary eavesdropper vehicle, taking into consideration of interference at the primary user. We assume that the eavesdropper vehicle takes the advantages of both the relay link and direct link. We consider that vehicle-to-vehicle links are modeled as double-Rayleigh fading, while vehicle-to-fixed infrastructure links are modeled as Rayleigh fading. Such a scenario finds it relevancy in vehicle-to-vehicle communication and/or vehicle-to-infrastructure communication under spectrum sharing heterogeneous cooperative vehicular networks. For such a realistic scenario, in particular, we derive a tight lower bound expression of the secrecy outage probability under mixed Rayleigh and double-Rayleigh fading channels. We also present an effective secrecy diversity order analysis and show that the considered system can achieve a secrecy diversity order of 2 for infinitely large average channel gain values of the main links. Finally, we demonstrate the accuracy of our analytical findings via numerical and simulation results and show the impact of channel conditions, primary interference constraints, and direct links on the secrecy performance of the considered system.

Harvesting-Throughput Tradeoff for CDMA-Based Underlay Cognitive Radio Networks With Wireless Energy Harvesting

This paper considers a green cognitive radio network wherein each secondary user (SU) is solely powered by an energy harvester which extracts energy from RF signals of a primary user transmitter. Each SU operates in a harvesting–transmitting fashion periodically. In each period, all SUs first harvest energy for a fixed duration and then transmit data using the harvested energy to one access point for the rest of the period in the code-division multiple access method. Considering the intrinsic harvesting-throughput tradeoff, we jointly optimize the harvesting time and transmit powers of SUs to maximize the sum throughput of the network, subject to the primary interference constraint and the energy causality constraint. Despite of the nonconvex nature of the formulated optimization problem, we find an equivalent but convex substitution to the original problem. Then, we propose a dual-decomposition-based solution method to solve the problem. Simulations finally demonstrate the efficiency of this paper.

Energy-Efficiency-Based Resource Allocation Framework for Cognitive Radio Networks With FBMC/OFDM

In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users' energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes.

Throughput-Optimal Multihop Broadcast on Directed Acyclic Wireless Networks

We study the problem of efficiently disseminating packets in multi-hop wireless networks. At each time slot, the network controller activates a set of non-interfering links and forward selected copies of packets on each activated link. The maximum rate of commonly received packets is referred to as the broadcast capacity of the network. Existing policies achieve the broadcast capacity by balancing traffic over a set of spanning trees, which are difficult to maintain in a large and time-varying wireless network. In this paper, we propose a new dynamic algorithm that achieves the broadcast capacity when the underlying network topology is a directed acyclic graph (DAG). This algorithm is decentralized, utilizes local information only, and does not require the use of spanning trees. The principal methodological challenge inherent in this problem is the absence of work-conservation principle due to the duplication of packets, which renders usual queuing modeling inapplicable. We overcome this difficulty by studying relative packet deficits and imposing in-order delivery constraints to every node in the network. We show that in-order delivery is throughput-optimal in DAGs and can be exploited to simplify the design and analysis of optimal algorithms. Our capacity characterization also leads to a polynomial time algorithm for computing the broadcast capacity of any wireless DAG under the primary interference constraints. In addition, we propose a multiclass extension of our algorithm, which can be effectively used for broadcasting in any network with arbitrary topology. Simulation results show that the our algorithm has a superior delay performance as compared with the traditional tree-based approaches.

Exploitation of spatial diversity in a novel cooperative spectrum sharing method based on PAM and modified PAM modulation

This article presents a novel cooperative spectrum sharing (CSS) scheme. The primary transmitter transmits a complex Quadrature amplitude modulation (QAM) signal in the first phase, and CSS occurs in the second phase. The secondary transmitter with the largest forwarding channel gain among the nodes that successfully decode the primary signal in the first phase is selected for CSS. This selected node employs a pulse-amplitude modulation (PAM) signal for primary information message (IM) instead of the QAM signal, and it employs a modified PAM signal for the secondary IM. The proposed modified PAM signal depends on the amplitude of the primary PAM signal. This method results in no mutual interference and negligible primary interference constraint and allows a higher degree of exploitation of spatial diversity, thus enabling increase in secondary power to improve primary transmission. The outage performance is enhanced in both the primary and secondary systems. The critical region, in which the primary outage performance is enhanced with the proposed CSS scheme, can be adjusted and widened by varying either the modulation cooperation sharing factor or the number of secondary transmitters.

Sequential Optimization for Subcarrier Pairing and Power Allocation in CP-SC Cognitive Relay Systems

A sequential optimization algorithm (SOA) for resource allocation in a cyclic-prefixed single-carrier cognitive relay system is proposed in this study. Both subcarrier pairing (SP) and power allocation are performed subject to a primary user interference constraint to minimize the mean squared error of frequency-domain equalization at the secondary destination receiver. Under uniform power allocation at the secondary source and optimal power allocation at the secondary relay, the ordered SP is proven to be asymptotically optimal in maximizing the matched filter bound on the signal-to-interference-plus-noise ratio. SOA implements the ordered SP before power allocation optimization by decoupling the ordered SP from the power allocation. Simulation results show that SOA can optimize resource allocation efficiently by significantly reducing complexity.

A new power allocation algorithm in cognitive radio networks

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio (SINR) loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

Decode and forward relaying for energy‐efficient multiuser cooperative cognitive radio network with outage constraints

We investigate the optimal allocation of power in the downlink cooperative cognitive radio network using decode and forward (DF) relaying technique. The power allocation in DF relaying for green cooperative cognitive radio with an objective of maximising energy-efficiency is a constraint non-linear non-convex fractional programming problem. The optimisation needs to satisfy the primary users interference constraints and secondary users outage constraints. The authors present the optimal power allocation in DF relaying by transforming the constraint non-linear non-convex fractional power allocation problem into a concave fractional programme by using Charnes–Cooper transformation. The authors also present an iterative algorithm that uses parametric transformation and guarantees e-optimal convergence. The convergence of the iterative algorithm is proved and numerical results obtained for cooperative cognitive radio network are presented with different network parameter settings.

Energy Efficient Transmissions in MIMO Cognitive Radio Networks

In this paper, we study energy-efficient transmissions for multiple-input multiple-output (MIMO) cognitive radio (CR) networks in which the secondary unlicensed users coexist with the primary licensed users. We want to optimize the time allocations and beamforming vectors for the secondary users (SUs), in order to minimize the energy consumption of the SUs while satisfying the SUs' rate requirements and the primary receivers' interference constraints. Compared with the tradition MIMO networks, the challenge here is that the SUs may not always be able to obtain the channel state information (CSI) to the primary receivers. We are interested in two different scenarios. The first is when the SUs have the luxury of knowing the CSI to the primary receivers, and the second is when the SUs do not have such an luxury. The corresponding optimization formulations involve joint time scheduling and beamforming, which are non-convex and are complicated to solve. Fortunately, we show that when the SUs are not able to obtain the CSI, the optimal time allocation and the optimal beamforming vectors can be found very efficiently in polynomial-time through a proper decomposition. When the SUs have perfect knowledge about the CSI, we show that the optimal solutions can still be obtained in polynomial time when the secondary system is under-utilized. If the traffic load to the secondary system is heavy, we propose a polynomial-time heuristic to generate a near-optimal solution. The simulation results show that our proposed energy-optimal-transmission algorithms can achieve an energy-saving of 30% to 91%, compared with the simplistic maximum-rate transmission policy, depending on the secondary system's traffic load.

Analyzing the Performance of Greedy Maximal Scheduling via Local Pooling and Graph Theory

Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling, or GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in which the Local Pooling conditions are satisfied, GMS achieves 100% throughput. Moreover, in networks in which the σ-Local Pooling conditions hold, GMS achieves σ% throughput. In this paper, we focus on identifying the specific network topologies that satisfy these conditions. In particular, we provide the first characterization of all the network graphs in which Local Pooling holds under primary interference constraints (in these networks, GMS achieves 100% throughput). This leads to a linear-time algorithm for identifying Local-Pooling-satisfying graphs. Moreover, by using similar graph-theoretical methods, we show that in all bipartite graphs (i.e., input-queued switches) of size up to 7 × <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , GMS is guaranteed to achieve 66% throughput, thereby improving upon the previously known 50% lower bound. Finally, we study the performance of GMS in interference graphs and show that in certain specific topologies, its performance could be very bad. Overall, the paper demonstrates that using graph-theoretical techniques can significantly contribute to our understanding of greedy scheduling algorithms.

Hybrid Opportunistic Scheduling in Cognitive Radio Networks

In a cognitive (secondary) multiple-access network which is subject to interference power constraints imposed by a primary system, it is desirable to mitigate the interference on the primary and to harvest multiuser diversity gains in the secondary. To simultaneously achieve these goals, a two-step (hybrid) scheduling method is proposed that pre-selects a set of secondary users based on their interference on the primary, and from among them selects the user(s) that yield the highest secondary throughput. The optimal number of active secondary transmitters is characterized as a function of the primary interference constraint, the secondary transmit power, and the number of secondary transmitters n. The secondary sum-rate (throughput) of the proposed algorithm grows optimally (proportional to log n). We investigate the tradeoff between scaling the secondary throughput and reducing interference on the primary, and characterize the optimum tradeoff in the regime of large n. Finally, we study user scheduling under fairness constraints, which is necessary when the channel statistics of secondary nodes are not identical. A modified hybrid scheduling rule is proposed to ensure user fairness, while still achieving the optimal growth rate for the secondary throughput.

Asynchronous CSMA Policies in Multihop Wireless Networks With Primary Interference Constraints

We analyze asynchronous carrier sense multiple access (CSMA) policies for scheduling packet transmissions in multihop wireless networks subject to collisions under primary interference constraints. While the (asymptotic) achievable rate region of CSMA policies for single-hop networks has been well-known, their analysis for general multihop networks has been an open problem due to the complexity of complex interactions among coupled interference constraints. Our work resolves this problem for networks with primary interference constraints by introducing a novel fixed-point formulation that approximates the link service rates of CSMA policies. This formulation allows us to derive an explicit characterization of the achievable rate region of CSMA policies for a limiting regime of large networks with a small sensing period. Our analysis also reveals the rate at which CSMA achievable rate region approaches the asymptotic capacity region of such networks. Moreover, our approach enables the computation of approximate CSMA link transmission attempt probabilities to support any given arrival vector within the achievable rate region. As part of our analysis, we show that both of these approximations become (asymptotically) accurate for large networks with a small sensing period. Our numerical case studies further suggest that these approximations are accurate even for moderately sized networks.

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria.

Distributed Throughput Maximization in Wireless Mesh Networks via Pre-Partitioning

This paper considers the interaction between channel assignment and distributed scheduling in multi-channel multi-radio Wireless Mesh Networks (WMNs). Recently, a number of distributed scheduling algorithms for wireless networks have emerged. Due to their distributed operation, these algorithms can achieve only a fraction of the maximum possible throughput. As an alternative to increasing the throughput fraction by designing new algorithms, we present a novel approach that takes advantage of the inherent multi-radio capability of WMNs. We show that this capability can enable partitioning of the network into subnetworks in which simple distributed scheduling algorithms can achieve 100% throughput. The partitioning is based on the notion of Local Pooling. Using this notion, we characterize topologies in which 100% throughput can be achieved distributedly. These topologies are used in order to develop a number of centralized channel assignment algorithms that are based on a matroid intersection algorithm. These algorithms pre-partition a network in a manner that not only expands the capacity regions of the subnetworks but also allows distributed algorithms to achieve these capacity regions. We evaluate the performance of the algorithms via simulation and show that they significantly increase the distributedly achievable capacity region. We note that while the identified topologies are of general interference graphs, the partitioning algorithms are designed for networks with primary interference constraints.

Queue Length Stability in Trees Under Slowly Convergent Traffic Using Sequential Maximal Scheduling

In this paper, we consider queue-length stability in wireless networks under a general class of arrival processes that only requires that the empirical average converges to the actual average polynomially fast. We present a scheduling policy, sequential maximal scheduling, and use novel proof techniques to show that it attains 2/3 of the maximum stability region in tree-graphs under primary interference constraints, for all such arrival processes. For degree bounded networks, the computation time of the policy varies as the the logarithm of the network size. Our results are a significant improvement over previous results that attain only 1/2 of the maximum throughput region even for graphs that have a simple path topology, in similar computation time under stronger (i.e., Markovian) assumptions on the arrival process.

Optimal delay scheduling in networks with arbitrary constraints

We consider the problem of designing an online scheduling scheme for a multi-hop wireless packet network with arbitrary topology and operating under arbitrary scheduling constraints. The objective is to design a scheme that achieves high throughput and low delay simultaneously. We propose a scheduling scheme that - for networks operating under primary interference constraints - guarantees a per-flow end-to-end packet delay bound of 5 d j /(1- ρ j ), at a factor 5 loss of throughput, where d j is the path length (number of hops) of flow j and ρ j is the effective loading along the route of flow j . Clearly, d j is a universal lower bound on end-to-end packet delay for flow j . Thus, our result is essentially optimal. To the best of our knowledge, our result is the first one to show that it is possible to achieve a per-flow end-to-end delay bound of O (# of hops) in a constrained network. Designing such a scheme comprises two related subproblems: Global Scheduling and Local Scheduling. Global Scheduling involves determining the set of links that will be simultaneously active, without violating the scheduling constraints. While local scheduling involves determining the packets that will be transferred across active edges. We design a local scheduling scheme by adapting the Preemptive Last-In-First-Out (PL) scheme, applied for quasi-reversible continuous time networks, to an unconstrained discrete-time network. A global scheduling scheme will be obtained by using stable marriage algorithms to emulate the unconstrained network with the constrained wireless network. Our scheme can be easily extended to a network operating under general scheduling constraints, such as secondary interference constraints, with the same delay bound and a loss of throughput that depends on scheduling constraints through an intriguing "sub-graph covering" property.