Primary User Interference Constraint Research Articles

Interference Suppression and Energy Efficiency Improvement With Massive MIMO and Relay Selection in Cognitive Two-Way Relay Networks

We analyze a relay assisted wireless communication link between two underlay massive multiple-input multiple-output (MIMO) terminals. Specifically, an amplify and forwarding (AF) two-way relay is optimally selected to maximize the sum rate and to keep the interference on the primary user (PU) below an interference threshold. We first obtain asymptotic signal-to-interference-plus-noise ratio (SINR) values for two scenarios: (1) the relays and the two end nodes use transmit power scaling and (2) only the end nodes use transmit power scaling. For these two cases, we derive optimal power allocations subject to the PU interference constraints. With these optimal power allocations, we analyze how relay selection impacts the outage, the sum rate, and the energy efficiency of the network. For the first scenario, the outage can be reduced to zero with appropriate power allocation and the relay selection can be done offline. For the second scenario, outage will depend on the instantaneous channel state between the relays and the PU. Furthermore, for a realistic power consumption model, we analyze the energy efficiency and show that relay selection will increase it.

Precoding Designs for Full-Duplex Multi-User MIMO Cognitive Networks with Imperfect CSI

This paper studies a cognitive radio (CR) network which consists of a full-duplex (FD) multi-user (MU) multipleinput multiple-output (MIMO) secondary user (SU) networks operating within the coverage of multiple primary users (PUs). It is assumed that the channel state information (CSI) matrices associated with SU systems are perfectly known whereas the CSI ones from SUs to PUs are imperfectly estimated. The problem of interest is to design robust precoding matrices at the SUs to maximize the CR sum rate subject to the SU transmit power constraints and harmful interference restrictions at PUs. Due to non-concavity of the objective function and intractability of robust PU interference constraints, the design problem is non-convex and challenging to directly solve. We exploit the difference of two concave functions to recast the sum rate objective function as a lower bounded concave one. In addition, a linear matrix inequality (LMI) transformation is used to handle the semi-infinite robust interference constraints. Then, the sequential convex programming method is carried out to iteratively solve a convex optimization problem in each iteration. The simulation results are provided to investigate the CR sum-rate (spectral efficiency) performance and the robustness against the CSI uncertainty.

Design, implementation and analysis of cognitive radio enabled intelligent WBAN gateway for cost-efficient remote health monitoring

In this paper, we design and implement an intelligent gateway for Wireless Body Area Network (WBAN) that seeks opportunistic access of licensed Primary User (PU) channels using Cognitive Radio (CR) to facilitate spectral-efficient, cost-efficient and reliable Non-Real Time (NRT) backhaul transmission of WBAN data for ubiquitous healthcare monitoring. The controller is termed herein as BodyCog-BNC. We develop and implement a cross-layer based modified protocol stack at the BodyCog-BNC, comprising of BodyCog-BNC Management Entity (BME) and BodyCog-BNC CR Medium Access Control (BCR-MAC) units, to exploit cross layer message passing to support the novel idea of spectrum agility. A spectral agile BodyCog-BNC is capable of intelligently switching to conventional licensed Wide-Area Access Network (WAN) technology in case of lack of proper PU channels or occurrence of successive handoffs in multiple PU channels. Furthermore, the protocol stack facilitates session management and energy-efficient backhaul CR transmission over a PU channel employing a proposed Inter-Sensing Time Optimization (ISTO) algorithm. ISTO leverages on convex optimization to select the optimum inter-sensing time for a PU channel to maximize the energy-efficiency within a PU interference constraint under sensing uncertainties. Closed form analytical expressions are also derived to estimate the average consumed energy, switching probability, switching time and the cost-efficiency of BodyCog-BNC. Exhaustive performance analysis shows that the BodyCog-BNC (WBAN gateway), provides a cost-efficient solution for different healthcare applications under varied operating conditions and CR cost regimes. Finally, through a prototype implementation and proof of concept we validate the ability of BodyCog-BNC in enabling NRT remote healthcare monitoring.

Linear approximation for bandwidth‐power product minimisation for downlink OFDM access in cognitive radio

In this Letter, the author proposes a low complex sub-optimal algorithm for resource allocation in spatial spectrum usage of a cognitive radio network by minimising the bandwidth power product while satisfying secondary user's quality of service and interference constraints of primary users. The author has proposed an approach to solve the problem by using linear programming techniques. The author compares the proposed method with the optimal solution to show its effectiveness.

Probabilistical Robust Power Control for Cognitive Radio Networks under Interference Uncertainty Conditions

<p class="0abstract"><a name="OLE_LINK11"></a><a name="OLE_LINK12"></a><span lang="EN-US">The focus of this paper is to find a robust power control strategy with uncertain noise plus interference (NI) in </span><span lang="EN-US">cognitive radio networks (</span><span lang="EN-US">CRNs</span><span lang="EN-US">)in an </span><span lang="EN-US">under orthogonal frequency-division multiplexing (OFDM) framework. The optimization problem is formulated to maximize </span><span lang="EN-US">the </span><span lang="EN-US">data rate of secondary users (SUs) under the constraints o</span><span lang="EN-US">f</span><span lang="EN-US"> transmission power of each SU, probabilistic the transmit rate of each SU at each subcarrier and robust interference constraint of primary user. In consideration of the feedback errors from the quantization </span><span lang="EN-US">due to</span><span lang="EN-US"> uniform distribution, the probabilistic constraint is transformed into closed forms. By using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, we solve this dual problem. Numerical simulation results show that our proposed algorithm is superior to the robust power control scheme based on interference gain worst case approach and non-robust algorithm without quantization error in perfect channels in the improvement of data rate of each SU, convergence speed and computational complexity.</span></p>

Energy efficiency in cognitive radio assisted D2D communication networks

The next generation networks intend to have features like device to device (D2D) connectivity, energy efficiency and spectral efficiency. This paper presents problem formulation for maximization of energy efficiency of cognitive radio assisted D2D networks subject to compliance of transmit powers of cellular users and interference constraints of primary users of broadcast network. Cognitive radio network (CRN) users are cellular users comprising of cellular and D2D users. Cellular users can opt any of the cellular or D2D mode. These CRN users opportunistically utilize spectrum of television (TV) white spaces. The problem thus formulated is NP-complete. Mesh adaptive direct search (MADS) algorithm has been used to find $$\varepsilon $$ -optimal solution. The results of MADS are compared with the global optimal solution obtained by exhaustive search algorithm (ESA). The simulation results reveal that MADS’ performance is equally good as that of ESA in most of the cases. MADS outperforms ESA in terms of energy efficiency comparison. Simulation results compare results of utility value, spectral efficiency and admission of CRN users with two types of utilities, i.e., utility with maximization of energy efficiency and utility without maximization of energy efficiency. Results testify that utility with maximization of energy efficiency outperforms its counterpart utility. MADS is also ideal algorithm as it has low computational complexity as compared to ESA. Computational complexity of ESA increases exponentially as numbers of users increase making MADS obvious choice for real life networks comprising of large number of cellular users.

Achievement of interference alignment in general underlay cognitive radio networks: Scenario classification and adaptive spectrum sharing

Interference alignment (IA) is suitable for cognitive radio networks (CRNs). However, in IA spectrum sharing (SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user (PU), which may lead to low signal-to-noise-ratio at cognitive radio receivers (CRRs). Consequently, sum rate of cognitive users (CUs) may fall short of the theoretical maximum through IA. To solve this problem, we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

AbstractEnergy efficiency (EE) has now become one of the essential parameters in the design of 5G and other next‐generation communication networks mainly due to economic and environmental concerns. In this paper, we develop an EE maximisation algorithm for the orthogonal frequency‐division multiplexing (OFDM)–based cognitive radio (CR) systems where the PU activity is highly dynamic. The problem is formulated as a joint optimisation problem in 3 variables: sensing duration, transmission duration, and transmit power on the subcarrier, with statistical constraint on the primary user (PU) interference. The statistical interference constraint frees the CR transmitter from the additional burden of obtaining the instantaneous channel quality feedback from the PU receivers. Furthermore, PU mobility, which is the main consideration of this work, closely portrays a real and practical CR network. We analyse how the mobility of the PUs affect their Quality of Service, particularly in terms of the interference impinged on PUs by the SUs. Moreover, the mobility of the PUs has been modelled, and PU interference constraints are reformulated by considering collisions between the PUs and SUs during their overlap period. Moreover, closed‐form expressions for secondary throughput and EE have been derived. The original problem, which is mathematically intractable due to its nonconvexity, is reformulated in terms of 3 alternately iterating subproblems. Numerical results show that with the proposed optimal algorithm, CR users can achieve significantly higher EE compared with existing algorithms. The suboptimal algorithm guarantees reduced complexity with a performance gap close to zero with the original optimal problem.

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) accessing the frequency band of a primary user (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU, and subsequently, eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the secondary users that aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the active learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate, and fast Bayesian learning method, the expectation propagation. The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Leakage-based beamforming via game theory for reverse spectrum auction in cellular offloading systems

The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic change of traffic demand in a day. In this paper, considering the dynamic characteristic of the cellular network traffic demand, we not only proposed an optimal, truthful reverse auction incentive framework, but also proposed a valuation function which is based on third-party access points’ capacity. We consider spectrum sharing in a third-party network where several secondary users (SUs) share spectrum with a primary user (PU). A leakage-based beamforming algorithm is proposed via game theory to maximize the sum utility of third-party access points subject to the signal-to-leakage-and-noise (SLNR) constraint of SUs and PU interference constraint. The sum throughput maximization problem is formulated as a non-cooperative game, where the SUs compete with each other over the resources. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can achieve a high sum throughput and converge to a locally optimal beamforming vector.

Stackelberg game-based precoding and power allocation for spectrum auction in fractional frequency reuse cognitive cellular systems

Spectrum auction has been considered as a promising approach to effectively reallocate spectrum resources in the secondary spectrum market. In our previous work, spectrum auction in a fractional frequency reuse (FFR) cognitive cellular system was studied. However, the bidding and valuation model of secondary users (SUs) are not close to practical applications as they introduced a random value of a fixed scope. In this paper, through an optimal interference price announced by the primary user (PU), a joint precoding and power allocation algorithm via Stackelberg game (OIPPS) is proposed to improve the spectrum auction problem subject to the interference constraint of PU, the transmission power constraint of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU in the FFR cognitive cellular system. Simulation results show the effectiveness of the proposed OIPPS algorithm in terms of the convergence of precoding and power allocation vectors and the maximized sum utility of SUs while taking full consideration of the PU’s revenue. Besides, a comparison between the bidding improved spectrum auction scheme and a traditional method is proposed to show the effectiveness of our proposed algorithm.

Performance analysis of transmit antenna selection for cognitive radio systems with imperfect channel estimation

In this article, we consider a spectrum sharing cognitive radio network with a transmit antenna selection scheme, subject to the peak interference constraint of primary user (PU) and the maximum transmit power limit of secondary user (SU). Due to time-varying properties or feedback latency, the channel estimation of the link from secondary transmitter to primary receiver may be imperfect, which will result in the violation of the interference constraint at PU. Under the condition of imperfect channel state information (CSI), we derive the cumulative distribution function (CDF) and probability density function (PDF) of the SU’s signal to noise ratio (SNR). Then, we obtain the closed-form expressions of average symbol error rate (SER) and capacity for the secondary system, respectively. Extensive simulations are performed with various system parameters to validate the correctness of our theoretical analysis.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

ABSTRACTIn cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

Power Allocation and Capacity Analysis of Secondary User in Heterogeneous Spectrum Sharing Systems

In this paper, we considered heterogeneous spectrum sharing system where the number of subcarriers of the primary user (PU) was twice as much as that of the secondary user (SU). In this case, due to non-orthogonality and inter-carrier interference (ICI) from SU to PU, it is difficult to satisfy the interference constraint of PU. In order to mitigate ICI and satisfy the interference constraint, we proposed a new transmission scheme of the SU with power allocation scheme. The proposed scheme will only generate subcarrierby - subcarrier interference. Therefore, it can easily satisfy the interference constraint of the PR and enhance the capacity of the SU. In addition, we derived the ergodic capacity of the SU. Based on numerical results, we confirmed that the proposed schemes could guarantee SU with a reliable capacity while satisfying the interference constraint of the PU. In addition, the derived capacity well matched the numerical results.

Finite Block-Length Analysis of Spectrum Sharing Networks: Interference-Constrained Scenario

This letter studies the interference-constrained performance of spectrum sharing networks using adaptive power allocation. The throughput of the secondary user is derived in different cases with constraints on the primary user average or instantaneous received interference power. The analysis is based on some recent results on the achievable rates of finite-length codes and shows the effect of the codeword length on the performance of spectrum sharing networks. The results indicate that, with codewords of finite length and different primary user's interference constraints, there is potential for spectrum sharing, if the transmission power is properly adapted.

Energy-Efficient Power Allocation for Cognitive Radio Networks with Joint Overlay and Underlay Spectrum Access Mechanism

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency-division multiple access–based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a “bit per Joule” metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy-efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS---PU couldn't be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.

An Auction Based Approach for Resource Allocation in Multi-Cell MIMO-OFDM Based Cognitive Radio Networks

The joint problems of channel and power allocation in multi-cell MIMO-OFDM based cognitive radio networks is addressed, in this work. More specifically, a repeated auction is proposed for the resource allocation problem, in which secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). With the inter-cell interference and mutual interference between PUs and SUs, the resource allocation problem is formulated as a non-convex optimization problem. Auction performs well in solving non-convex problems, therefore the interference auction is proposed. Moreover, with the theoretical analysis of equilibrium, an implementation algorithm for the auction is developed and the convergence is proved. Simulation results show that the interference auction obtains a good spectrum efficiency improvement and a rapid convergence rate.

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.