Interference Temperature Constraints Research Articles

Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints

Power allocation is an important issue for Cognitive Radio Networks (CRNs), since it needs to consider the Quality of Service (QoS) for Secondary Users (SUs) while maintaining the interference power to Primary User (PU) below the Interference Temperature (IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange. Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm (IWFA).

Robust Relay Beamforming in Cognitive Two-Way Relay Networks

We propose a novel restriction and relaxation (RAR) method for robust relay beamforming design in cognitive two-way relay networks with imperfect channel state information (CSI). Due to the uncertainty in CSI, the self-interference cannot be completely canceled. Hence, both channel uncertainties and residual self-interference should be considered. The proposed RAR method aims to minimize the transmit power of relay nodes while guaranteeing the worst-case signal-to-interference plus noise ratio (SINR) of secondary users (SUs) as well as satisfying the interference temperature (IT) constraints. Specifically, in the restriction step of RAR, infinite number of complicated constraints are reformulated into a finite number of linear matrix inequalities (LMIs). In the relaxation step of RAR, the nonconvex robust design problem is transformed into a convex semidefinite program (SDP), which can be efficiently solved by interior-point methods. Simulation results verify the effectiveness and robustness of the proposed method.

Power control algorithm with faster convergence based on non-cooperative game for cognitive radio networks

Concerning the slow convergence of distributed power control algorithm in cognitive radio,a novel algorithm based on non-cooperative game was proposed for cognitive radio system.A Signal-to-Interference Ratio(SIR)-based tangent cost function of fewer iterations was designed to improve the convergence.The simulation results demonstrate that,compared with the Koskie-Gajic algorithm and the Cognitive Radios-Non-Cooperative Power Control Game(CR-NCPCG) algorithm,the proposed algorithm at the premise of satisfying the secondary users' demand for SIR and the primary user's interference temperature constraint,not only can fast converge,but also has higher average SIR with at least 0.3 dB when the number of users is less than twenty.It can control the power of secondary users effectively.

Performance Analysis of Distributed Beamforming in a Spectrum Sharing System

In this paper, we consider a distributed beamforming scheme (DBF) in a spectrum sharing system where multiple secondary users share the spectrum with some licensed primary users under an interference temperature constraint. We assume that the DBF is applied at the secondary users. We first consider optimal beamforming and compare it with the user selection scheme in terms of the outage probability and bit error rate performance metrics. Since perfect feedback is difficult to obtain, we then investigate a limited feedback DBF scheme and develop an analysis for a random vector quantization design algorithm. Specifically, the approximate statistics functions of the squared inner product between the optimal and quantized vectors are derived. With these statistics, we analyze the outage performance. Furthermore, the effects of channel estimation error and number of primary users on the system performance are investigated. Finally, optimal power adaptation and cochannel interference are considered and analyzed. Numerical and simulation results are provided to illustrate our mathematical formalism and verify our analysis.

Interference Temperature Constraint-based Radio Resource Allocation for the Network Coded Cognitive Cooperative Network (NCCCN)

Demand for higher data rate wireless applications has led to scarcity in radio frequency spectrum. Spectrum access achieves near-optimal spectrum utilization efficiency with the advent of cognitive radio technology. In cognitive radio network (CRN), each cognitive user, also called secondary user (SU), senses and uses radio spectrum opportunistically while regulating the interference constraint. This article focuses the radio resource allocation considering interference temperature constraints for the network coded cognitive cooperative network (NCCCN). Analog network coded (ANC) Orthogonal-Frequency-DivisionMultiplexing (OFDM) improves the capacity of the cognitive cooperative network (CCN). Moreover, CCN enhances the spectrum utilization efficiency. Power allocation optimization problems have been formed that maximize the data transmission rate of NCCCN under the total transmit and peak-interference powers or the total transmit and average-interference powers. The spectral efficiency of the proposed network is compared with the spectral efficiency of CCN without ANC. Simulation results show that the proposed NCCCN enhances spectral efficiency in compared to the CCN without ANC.

Spatially Selective Artificial-Noise Aided Transmit Optimization for MISO Multi-Eves Secrecy Rate Maximization

Consider an MISO channel overheard by multiple eavesdroppers. Our goal is to design an artificial noise (AN)-aided transmit strategy, such that the achievable secrecy rate is maximized subject to the sum power constraint. AN-aided secure transmission has recently been found to be a promising approach for blocking eavesdropping attempts. In many existing studies, the confidential information transmit covariance and the AN covariance are not simultaneously optimized. In particular, for design convenience, it is common to prefix the AN covariance as a specific kind of spatially isotropic covariance. This paper considers joint optimization of the transmit and AN covariances for secrecy rate maximization (SRM), with a design flexibility that the AN can take any spatial pattern. Hence, the proposed design has potential in jamming the eavesdroppers more effectively, based upon the channel state information (CSI). We derive an optimization approach to the SRM problem through both analysis and convex conic optimization machinery. We show that the SRM problem can be recast as a single-variable optimization problem, and that resultant problem can be efficiently handled by solving a sequence of semidefinite programs. Our framework deals with a general setup of multiple multi-antenna eavesdroppers, and can cater for additional constraints arising from specific application scenarios, such as interference temperature constraints in interference networks. We also generalize the framework to an imperfect CSI case where a worst-case robust SRM formulation is considered. A suboptimal but safe solution to the outage-constrained robust SRM design is also investigated. Simulation results show that the proposed AN-aided SRM design yields significant secrecy rate gains over an optimal no-AN design and the isotropic AN design, especially when there are more eavesdroppers.

Cognitive Radio Network Duality and Algorithms for Utility Maximization

We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary users in cognitive radio networks. All the users maximize the network utility by adapting their signal-to-interference-plus-noise ratio (SINR) assignment and transmit power subject to power budget constraints and additional interference temperature constraint for the secondary users. The utility maximization problem is challenging to solve optimally in a distributed manner due to the nonconvexity and the tight coupling between the power budget and interference temperature constraint sets. We first study a special case where egalitarian SINR fairness is the utility, and a tuning-free distributed algorithm with a geometric convergence rate is developed to solve it optimally. Then, we answer the general utility maximization question by developing a cognitive radio network duality to decouple the SINR assignment, the transmit power and the interference temperature allocation. This leads to a utility maximization algorithm that leverages the egalitarian fairness power control as a submodule to maintain a desirable separability in the SINR assignment between the secondary and primary users. This algorithm has the advantage that it can be distributively implemented, and the method converges relatively fast. Numerical results are presented to show that our proposed algorithms are theoretically sound and practically implementable.

Outage Analysis of Cognitive Two-Way Relaying Network with Physical-Layer Network Coding in Nakagami-m Fading Channels

In spectrum sharing systems, a secondary user (SU) is permitted to share frequency bands with primary user (PU) as long as its transmission does not interfere with the PU's communication. In this paper, the outage probability is investigated for the physical-layer network coding (PNC) based cognitive two-way relay network over Nakagami-m fading channel. By applying the interference temperature (IT) constraints at the transceiver nodes and relay nodes in secondary systems, we analyze the outage performance in underlay spectrum sharing with the best relay selection criterion. The cumulative distribution function (CDF) of the signal to noise ratio (SNR) for each link is derived to obtain the tight upper bound on the outage probability of the secondary relay system. Simulations results demonstrate the validity and accuracy of the theoretical analysis. Index Terms—Cognitive two-way relay network, physical layer network coding, outage probability, Nakagami-m fading channel.

Performance of User Selection in Cognitive Broadcast Channels

This letter presents an analytical performance investigation on the cognitive broadcast channels (BC) with user selection. Exact closed-form expressions for the outage probability and multiuser interference diversity gain of the cognitive BC are derived. In addition, closed-form upper and lower bounds for the ergodic capacity of the system are presented. These analytical results not only provide fast and efficient means to evaluate the performance of the system, they also enable us to gain valuable insights on the impact of key parameters such as peak transmit power, interference temperature constraint, primary transmit power and channel fading parameters on the system performance.

Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With Imperfect Monitoring

We develop a novel design framework for dynamic distributed spectrum sharing among secondary users (SUs), who adjust their power levels to compete for spectrum opportunities while satisfying the interference temperature (IT) constraints imposed by primary users. The considered interaction among the SUs is characterized by the following three unique features. First, the SUs are interacting with each other repeatedly and they can coexist in the system for a long time. Second, the SUs have limited and imperfect monitoring ability: they only observe whether the IT constraints are violated, and their observation is imperfect due to the erroneous measurements. Third, since the SUs are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the network rather than obeying the prescribed allocation of a centralized controller. To capture these unique features, we model the interaction of the SUs as a repeated game with imperfect monitoring. We first characterize the set of Pareto optimal operating points that can be achieved by deviation-proof spectrum sharing policies, which are policies that the selfish users find it in their interest to comply with. Next, for any given operating point in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows users to transmit in a time-division multiple-access (TDMA) fashion. In the presence of strong multi-user interference, our policy outperforms existing spectrum sharing policies that dictate users to transmit at constant power levels simultaneously. Moreover, our policy can achieve Pareto optimality even when the SUs have limited and imperfect monitoring ability, as opposed to existing solutions based on repeated game models, which require perfect monitoring abilities. Simulation results validate our analytical results and quantify the performance gains enabled by the proposed spectrum sharing policies.

Impact of Quality of Service Constraints on the Performance of Spectrum Sharing Cognitive Users

This article investigates the impact of choosing a specific quality of service constraint on the performance of the cognitive users in a spectrum sharing cognitive environment. To communicate over a wireless channel reserved to a primary user, the cognitive user has to satisfy the primary user's quality of service constraint. Cognitive systems under interference temperature and outage probability constraints are investigated in this work. The outage probability of the primary user in an interference temperature-constraint environment is analyzed, and the performance measures of the cognitive users are developed. A comparative study of the cognitive user's performance under equivalent outage probability and interference temperature constraints is conducted as well. Numerical results are presented to verify the theoretical analysis and compare the performance measures under these constraints. Results of this work illustrate the effects of the communication environment parameters on the cognitive users and detail the performance differences between the equivalent outage probability-constraint and interference temperature-constraint cognitive systems.

Robust Multicast Beamforming for Spectrum Sharing-Based Cognitive Radios

We consider a robust downlink beamforming optimization problem for secondary multicast transmission in a multiple-input multiple-output (MIMO) spectrum sharing cognitive radio (CR) network. The minimization of transmit power is formulated subject to both quality-of-service (QoS) constraints on the secondary receivers and interference temperature constraints on the primary users, under the assumption of imperfect channel state information (CSI). The problem is a nonconvex quadratically constrained quadratic program (QCQP), and in general it is hard to achieve the global optimality. As a compromise, we present two randomized approximation algorithms for the problem via convex optimization techniques. Apart from the general setting of the robust beamforming problem, we identify one interesting special case, the robust problem of which can be solved efficiently. Simulation results are presented to demonstrate the performance gains of the proposed algorithms over an existing robust design.

Protecting Primary Users in Cognitive Radio Networks with Effective Capacity Loss Constraint

Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.

Cognitive Code-Division Channelization with Admission Control

We consider the problem of joint resource allocation and admission control in a secondary code-division network coexisting with a narrowband primary system. Our objective is to find the maximum number of admitted secondary links and then find the optimal transmitting powers and code sequences of those secondary links such that the total energy consumption of the secondary network is minimized subject to the conditions that primary interference temperature constraints, secondary signal-to-interference-plus-noise ratio (SINR) constraints and secondary peak power constraints are all satisfied. This is an NP-hard optimization problem which motivates the development of suboptimal algorithms. We propose a novel iterative algorithm to solve this problem in a computationally efficient manner. Numerical results demonstrate that the proposed algorithm provides excellent solutions that result in high energy efficiency and large admitted percentage of secondary links.

Performance Analysis of Cognitive Radio Multiple-Access Channels Over Dynamic Fading Environments

In dynamically changing environments, the spectrum-sharing method is a promising method to address the spectrum underutilization problem for cognitive radio (CR) systems. This paper investigates the capacity of cognitive radio multiple-access channel (CR-MAC) over a dynamic fading environment. Multiple secondary users (SUs) transmit to the secondary base station under the transmit power (TP) and interference temperature (IT) at the primary base station constraints. In order to perform a general analysis, a theoretical dynamic fading model termed hyper-fading model, which is suitable to the dynamic nature of cognitive radio channel, is considered. The optimal power allocation method is employed to maximize the capacity of CR-MAC for hyper-fading channel with TP and IT constraints and full channel side information. Through the numerical simulations, the capacity of the hyper-fading channels are compared with that of other channel fading models such as Rayleigh, Nakagami-2, and with an additive white Gaussian noise channel. Additionally, the impacts of the number of SUs on capacity is investigated.

A stackelberg game approach to distributed spectrum management

We consider a cognitive radio system with one primary (licensed) user and multiple secondary (unlicensed) users. Given the interference temperature constraint, the secondary users compete for the available spectrum to fulfill their own communication need. Borrowing the concept of price from market theory, we develop a decentralized Stackelberg game formulation for power allocation. In this scheme, the primary user (leader) announces prices for the available tones such that a system utility is maximized. Using the announced prices, secondary users (followers) compete for the available bandwidth to maximize their own utilities. We show that this Stackelberg game is polynomial time solvable under certain channel conditions. When the individual power constraints of secondary users are inactive (due to strict interference temperature constraint), the proposed distributed power control method is decomposable across the tones and unlike normal water-filling it respects the interference temperature constraints of the primary user. When individual power constraints are active, we propose a distributed approach that solves the problem under an aggregate interference temperature constraint. Moreover, we propose a dual decomposition based power control method and show that it solves the Stackelberg game asymptotically when the number of tones becomes large.

Fair subcarrier and power allocation for multiuser orthogonal frequency-division multiple access cognitive radio networks using a Colonel Blotto game

The problem of subcarrier allocation (SA) and power allocation (PA) for both the downlink and uplink of cognitive radio networks (CRNs) is studied. Two joint SA and PA schemes based on Blotto games are presented for orthogonal frequency-division multiple access (OFDMA)-based CRNs. In this work, the authors consider a more practical scenario by taking into account the correlation between adjacent subcarriers. In the proposed games, secondary users (SUs) simultaneously compete for subcarriers using a limited budget. In order to win as many good subcarriers as possible, the SUs are required to wisely allocate their budget subject to the transmit power, budget and interference temperature constraints. Two PA and budget allocation strategies are derived to enable fair sharing of spectrum among the SUs. It is shown that by manipulating the total budget available for each SU, competitive fairness can be enforced. In addition, the conditions to ensure the existence and uniqueness of Nash equilibrium (NE) in the proposed methods are established and algorithms which ensure convergence to NE are proposed. Simulation results show that the proposed methods can converge rapidly and allocate resources fairly and efficiently in correlated fading OFDMA channels.

Outage Analysis of Decode-and-Forward Cognitive Dual-Hop Systems With the Interference Constraint in Nakagami-$m$ Fading Channels

This paper studies the outage probability performance of decode-and-forward (DF) cognitive dual-hop systems for Nakagami- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading channels, with consideration of an interference temperature limit. An exact outage probability expression is derived, and the impact of various key system parameters, such as interference temperature and fading severity, is investigated. Our results show that the interference temperature constraint causes the outage saturation phenomenon, but the outage performance gets better when the channel quality of the secondary transmission links improves. However, how the channel quality of the interfering links affects the outage performance depends on the channel quality of the secondary transmission links, as well as the interference temperature constraint and maximum transmit power.

Joint Spectrum Allocation and Power Control for Multihop Cognitive Radio Networks

We propose a new framework of joint spectrum allocation and power control to utilize open spectrum bands in cognitive radio networks (CRNs) by considering both interference temperature constraints and spectrum dynamics. We first address a simpler problem for the case of a single flow. A TDM-based power-control strategy is adopted to achieve maximum end-to-end throughput by choosing an appropriate multihop route and spectrum combination for each single flow. Then, the simpler solution is extended to the multiflow case in which interflow interference and cumulative interference temperature must be considered. Considering the overhead of switching route and spectrum, the optimal waiting time before making a switch is derived. Our in-depth simulation study has shown that the proposed algorithms utilize spectrum more efficiently than other existing algorithms.

Resource Allocation and Scheduling in Multi-Cell OFDMA Systems with Decode-and-Forward Relaying

In this paper, we formulate resource allocation and scheduling for multi-cell orthogonal frequency division multiple access (OFDMA) systems with half-duplex decode-and-forward (DF) relaying as a joint optimization problem taking into account multi-cell interference and heterogeneous user data rate requirements. For efficient multi-cell interference mitigation, we incorporate a time slot allocation strategy into the problem formulation. We transform the resulting non-convex and combinatorial optimization problem into a standard convex problem by imposing an interference temperature constraint, which yields a lower bound for the original problem. Subsequently, the transformed optimization problem is solved by dual decomposition and a semi-distributed iterative resource allocation algorithm with closed-form power and subcarrier allocation policies is derived to maximize the average weighted system throughput (bit/s/Hz/base station). Simulation results illustrate that our proposed semi-distributed algorithm achieves practically the same performance as the centralized optimal solution of the original non-convex problem and provides a substantial performance gain compared to single-cell resource allocation and scheduling schemes.