Cognitive Base Station Research Articles

Energy-Efficient Frequency and Power Allocation for Cognitive Radios in Television Systems

Energy-efficient resource allocation for cognitive radios operating in television systems (TV white spaces) presents a unique challenge compared with other cognitive radios because the interference constraint is for the whole frequency band rather than per carrier. This paper presents a subchannel and power allocation protocol that maximizes the energy efficiency (EE) of transmissions from a cognitive base station operating in the TV white spaces. The system model conforms to the IEEE 802.22 standard, and the proposed two-step solution to the EE maximization problem satisfies users' minimum rate requirements and keeps the interference to the primary users in the neighboring areas below a specified threshold. The first step of the protocol is a near-optimal but low-complexity subchannel assignment. This is followed by an optimal power allocation procedure that is obtained by analyzing the Karush–Kuhn–Tucker conditions. The computational complexity of the resulting resource allocation protocol is the same as that of the least complex resource allocation protocol for orthogonal frequency division multiple access (OFDMA) downlinks in the literature. Simulation results show that our protocol achieves higher EE compared with a modified and improved version of the OFDMA protocol from the literature.

MIMO Cognitive Radio User Selection With and Without Primary Channel State Information

In this paper, we study user selection (US) strategies for a multiple-input-multiple-output (MIMO) cognitive radio (CR) downlink network, where the r-antenna underlay CR secondary users (SUs) coexist with a primary user (PU), and all terminals are equipped with multiple antennas. Two main scenarios are considered: 1) The t-antenna cognitive base station (CBS) has perfect or partial channel state information at the transmitter (CSIT) from the CBS to the PU receiver (RX), and 2) the CBS has absolutely no PU CSIT. For these scenarios, we propose and evaluate multiple SU selection schemes that are applicable to both best-effort PU interference mitigation and hard interference temperature (IT) constraints. The computational complexity of the proposed schemes can be significantly smaller than that of an exhaustive search with negligible performance degradation. For the selection of C SUs out of K candidates, our proposed sliding window scheme, for example, is of complexity O(Krt <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), whereas an exhaustive search is on the order of O(( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> )C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). When t and r are C of the same order, the computational complexity of the proposed scheme can be ( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> )C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> /K times smaller. Mathematical complexity analysis and C numerical simulations are provided to show the advantage of our schemes.

Intelligent Cognitive Radio Models for Enhancing Future Radio Astronomy Observations

Radio astronomy organisations desire to optimise the terrestrial radio astronomy observations by mitigating against interference and enhancing angular resolution. Ground telescopes (GTs) experience interference from intersatellite links (ISLs). Astronomy source radio signals received by GTs are analysed at the high performance computing (HPC) infrastructure. Furthermore, observation limitation conditions prevent GTs from conducting radio astronomy observations all the time, thereby causing low HPC utilisation. This paper proposes mechanisms that protect GTs from ISL interference without permanent prevention of ISL data transmission and enhance angular resolution. The ISL transmits data by taking advantage of similarities in the sequence of observed astronomy sources to increase ISL connection duration. In addition, the paper proposes a mechanism that enhances angular resolution by using reconfigurable earth stations. Furthermore, the paper presents the opportunistic computing scheme (OCS) to enhance HPC utilisation. OCS enables the underutilised HPC to be used to train learning algorithms of a cognitive base station. The performances of the three mechanisms are evaluated. Simulations show that the proposed mechanisms protect GTs from ISL interference, enhance angular resolution, and improve HPC utilisation.

Implementation of OpenFlow based cognitive radio network architecture: SDN&amp;R

The static conventional network architecture is ill-suited to the growing management complexity and highly dynamic wireless network topologies. Software Defined Radio systems and their extension to cognitive and smart radio are characterized by distinct control loops for management which constantly increase network complexity and management inefficiencies, due to clear-cut between radio and core network management. Adding numerous devices and networks together will constantly increase the management cost, thus hinders scalability. Therefore, a holistic solution to synchronize radio and networks status has an elevated demand. To interconnect these systems and devices together, there is a need for a common management interface. OpenFlow is the first standard interface that enables Software Defined Networking (SDN). It can be rolled out in a variety of networking devices to enable improved automation and management by using common Application Program Interfaces to abstract the underlying networking details. The Software Defined Networking & Radio (SDN&R) framework proposed here has a potential combination between SDN and Radio networks to discover the underlying dynamism in cognitive access networks with integrated radio management. By isolating the control plane from the data plane, SDN&R enables a flexible management framework empowered by end-to-end goals through OpenFlow. In this article, we propose, validate, and evaluate the SDN&R architecture. In doing so, first we implement the OpenFlow enabled cognitive basestations (BSs) on Wireless Open-Access Research Platform. Furthermore, we develop software agents on BSs to provide radio status information to the cognitive control application implemented on the SDN controller. The results verify that the proposed framework in-lines with layer-2 or layer-3 forwarding performance. We claim that this work represents the first successful implementation results which synergizes SDN with Cognitive networks that motivates researchers towards SDN based radio resource management.

Optimal Beamforming and Power Allocation for Cognitive Femto Base Stations Based on Soft Decision

In this paper, an optimal beamforming and power allocation for the sensing-based spectrum sharing (SBSS) that employs a soft decision on the state of the primary user is proposed. Different from the existing soft-decision-based SBSS that employs a single antenna and adjusts only transmit power, in the proposed scheme, the secondary transmitter employs multiple transmit antennas and controls the beamforming direction and the transmit power. We design the optimal beamforming and power allocation under the constraints of total transmit power and interference power, where both average and instantaneous power constraints are considered. Simulation results demonstrate that the proposed technique can significantly improve the average throughput of the secondary user compared with the existing methods. The proposed beamforming for the soft-decision-based SBSS is a promising scheme, particularly for cognitive femto base stations (BSs) to boost up the spectral efficiency while managing the interference to the macro BSs.

Beamforming schemes for hybrid satellite-terrestrial cooperative networks

This paper studies the relay beamforming (BF) schemes for a hybrid satellite-terrestrial cooperative network (HSTCN), where a cognitive base station (CBS) with multi-antenna is used not only as a relay to assist the signal transmission between a satellite and a terrestrial primary user (PU), but also as a base station to transmit signals to multiple secondary users (SUs). Our goal is to minimize the total transmit power, while guaranteeing the quality-of-service (QoS) of all users. Specifically, by supposing that the channel state information (CSI) of both the uplink and downlink are perfectly known at the relay, we first present a non-robust BF method. Then, note that downlink CSI often contains errors, we propose two approaches based on worst-case criterion, namely, lowest bound robust BF scheme (LB-RBF) and tight bound robust BF scheme (TB-RBF), each of which relaxes the constraints and converts the complex problem to a convex optimization problem so that it can be solved by using standard numerical optimization packages. Finally, computer simulations are provided to demonstrate the performance of the three BF algorithms.

Resource Allocation Algorithm of Joint Channel Quality Evaluation Based on Non-cooperative Game in Cognitive Radio Network

For the centralized cognitive radio network, a resource allocation algorithm of joint channel quality evaluation based on non-cooperative game is proposed. The utility function for the game module considers both the receiving SINR threshold and transmitted power and tries to reduce interference as much as possible on the basis of guaranteeing the QoS demands for various services. The available channels can be evaluated through primary users occupying channel time and channel idle/occupied switching times. CR-BS (Cognitive Radio Base Station) calculates queue priority in a scheduling cycle based on users’ difierent services and dynamically adjusts service opportunities for realtime services and non-realtime services, which can provide difierential services and guarantee the equality for difierent services. The simulation shows that the proposed algorithm converges fast, can still ensures each user’s SINR value higher than its QoS demand threshold in low SNR environment, reduce the interference to primary users, and enhance network performance.

On the Study of Cognitive Bidirectional Relaying with Asymmetric Traffic Demands

AbstractIn this paper, we consider a cognitive radio network scenario, where two primary users want to exchange information with each other and meanwhile, one secondary node wishes to send messages to a cognitive base station. To meet the target quality of service (QoS) of the primary users and raise the communication opportunity of the secondary nodes, a cognitive bidirectional relaying (BDR) scheme is examined. First, system outage probabilities of conventional direct transmission and BDR schemes are presented. Next, a new system parameter called operating region is defined and calculated, which indicates in which position a secondary node can be a cognitive relay to assist the primary users. Then, a cognitive BDR scheme is proposed, giving a transmission protocol along with a time-slot splitting algorithm between the primary and secondary transmissions. Information-theoretic metric of ergodic capacity is studied for the cognitive BDR scheme to evaluate its performance. Simulation results show that with the proposed scheme, the target QoS of the primary users can be guaranteed, while increasing the communication opportunity for the secondary nodes.

Energy Efficiency Behavior in Heterogeneous Networks under Various Operating Situations of Cognitive Small Cells

Recently, several approaches were followed for the enhancement and better resource utilization in mobile networks; this is to achieve energy efficient consumption for production and delivery of an information bit. Using Cognitive Femto cells (as a member of the small base stations’ family) proves that, it is an efficient solution for achieving this goal[1]. The use of Energy Efficiency term η has become one of the major indices for measuring the performance of these systems. η is the measure of the overall system Capacity (C) in bps/Hz versus the Consumed Energy (E) in Joules [2]. In consistence with many researches, analytic models and empirical measurements, η will be investigated throughout the course of this work. Cognitive Base Stations (CBS) (as an element of the system model) which performs the traffic offloading operations is proved to enhance η performance. In this work, a combination of both analytic and simulation models are used to construct a practical system model. The obtained model is then used to illustrate the effect of different operational parameters that are involved in the η problem. On the other hand, the current paper tries to focus on the selection criteria that may be used to design the cooperative cognitive networks in order to achieve the best η indices. Both of CBSs radii as well as the inter-separation distances (between CBSs and MBS location) are examined to obtain best η index for different operation scenarios; in addition, both of capacity and energy consumption are taken into consideration based on practical operating measures. This work proposed several nonlinear equations with fixed parameters to be used by field engineers to achieve the results with minimum reduced computation complexity. So, the current work may be of importance for the regulator bodies as well as the cognitive mobile operators.

Resource Allocation in MIMO-OFDM-Based Cooperative Cognitive Radio Networks

We consider a MIMO-OFDM-based cooperative cognitive radio network (CCRN) in which there exists a number of regenerative relays to cooperate with the cognitive base station (CBS) in serving an SU. Our objective is to optimize the transmit covariance matrices at the CBS and the relays and obtain the optimal subcarrier pairing, relay assignment, where the dual-decomposition technique is recruited as a tool. The optimal resource allocation is performed subject to the individual power constraints in the CBS and relays and the interference power constraint on the primary system. Moreover, due to the high computational complexity of the optimal approach, a suboptimal algorithm is further proposed. The joint allocation of the resources in the proposed suboptimal algorithm is performed taking into account the decode-and-forward cooperation strategy, the interference imposed on the primary network, and the power budgets at the CBS and the relays. The performance of different approaches and the effect of deploying multiple antennas at users are explored through simulations.

Downlink Beamforming in Underlay Cognitive Cellular Networks

We propose a novel scheme for downlink beamforming design in an underlay cognitive cellular system. The beamforming design is formulated as an optimization problem with the objective of keeping the cognitive base station transmit power as well as the induced interference on the primary users, below predefined system thresholds. This is subject to providing a certain level of signal-to-interference-plus-noise ratio (SINR) to the secondary users. We then derive the corresponding semidefinite programming form for the formulated optimization problem and propose an iterative algorithm to obtain the beamforming vectors as the optimal solutions. We further analytically show the convergence of the proposed iterative algorithm. Extensive simulations verify that the proposed algorithm quickly converges to the optimal solution. We then compare the proposed scheme with a benchmarking system defined based on the previous methods proposed in the related literature. Comparisons show that the proposed algorithm outperforms the benchmarking system and induces lower interference at the primary service receivers. It is also observed that the proposed algorithm offers a higher sum rate in comparison to the benchmarking system. Simulation results further reveal that the proposed approach effectively works at a relatively high SINR level required by secondary users and strict interference threshold set by the primary system while the benchmarking system fails to do so.

Secrecy Outage and Diversity Analysis of Cognitive Radio Systems

In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), {while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively}. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of {the coordinated and uncoordinated eavesdroppers}. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, {no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst.} Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.

Opportunistic Spectrum Sharing in Relay-Assisted Cognitive Systems With Imperfect CSI

In this paper, we consider spectrum sharing between cognitive radio (CR) users and a licensed primary user (PU) to enhance the spectrum efficiency. In the considered scenario, the PU is not able to successfully transmit data to the intended receiver. In this case, the cognitive base station (CBS) in the vicinity of the primary network offers its help to transmit the primary data. In return, the CR system shares the spectrum with the primary system. In particular, we formulate an opportunistic spectrum sharing approach that determines the optimal beamforming weights at the CBS to maximize the overall worst throughput of the CRs while guaranteeing a certain quality of service (QoS) for the PU. Various formulations of beamforming are proposed, which consider different relaying scenarios, including robust designs that are applicable with imperfect channel state information (CSI) at the PU. The original optimization problems are nonconvex and do not have any closed-form solutions. However, using a convex optimization approach, we transform them into convex forms and find approximate solutions using semidefinite programming (SDP) along with randomization techniques. We consider different examples that demonstrate the efficiency of the proposed approach.

Adaptive Mode Selection in Multiuser MISO Cognitive Networks With Limited Cooperation and Feedback

In this paper, we consider a multiuser (MU) multiple-input-single-output (MISO) downlink cognitive network coexisting with a primary network. With the purpose of exploiting the spatial degree of freedom to counteract the internetwork interference and intranetwork (interuser) interference simultaneously, we propose performing zero-forcing beamforming (ZFBF) at the multiantenna cognitive base station (BS) based on the instantaneous channel state information (CSI). The challenge of designing ZFBF in cognitive networks lies in how to obtain the interference CSI. To solve it, we introduce a limited internetwork cooperation protocol, namely, the quantized CSI conveyance from the primary receiver to the cognitive BS via purchase. Clearly, the more the feedback amount, the better the performance, but the higher the feedback cost. To achieve a balance between the performance and feedback cost, we take the maximization of the feedback utility function (FUF), which is defined as the difference of the average sum rate and feedback cost while satisfying the interference constraint, as the optimization objective, and derive the transmission mode and feedback amount joint optimization scheme. Moreover, we quantitatively investigate the impact of CSI feedback delay and obtain the corresponding optimization scheme. Furthermore, through asymptotic analysis, we present some simple schemes. Finally, numerical results confirm the effectiveness of our theoretical claims.

Collaborative Spectrum Sensing of Cognitive Radio Networks with Simple and Effective Fusion Scheme

Cognitive radio (CR) provides a mechanism for effective spectrum usage. Spectrum sensing is an essential component of CR which comprises intelligent signal processing algorithms to identify the spectrum holes. In this paper, we propose a novel soft combining scheme, called mean cumulative sum (MCS), at the cognitive base station or Fusion center (FC), and we have investigated our proposed method through closed form expressions of probability of detection and probability of false alarm, respectively. We have also provided a method of obtaining an optimum threshold to minimize the sensing error which occurs in FC. Our simulation results show that proposed MCS combining scheme outperforms single user spectrum sensing and performs equally well as that of equal gain soft combining scheme. In addition, our results show that sensing error is reduced when optimum threshold is chosen at the FC.

Optimal resource allocation for opportunistic spectrum access in heterogeneous MIMO cognitive radio networks

AbstractThe optimal resource allocation in multi‐antenna multi‐band cognitive radio networks with heterogeneous secondary users (SUs) and underlay strategy is presented in this work. The heterogeneity is due to different types of secondary networks, where some of the SUs constitute an ad hoc network and access the resources (channel and power) in a distributed and competitive fashion, referred to as distributed SUs (DSUs), and others are served by a cognitive base station (CBS) and are referred to as centralised SUs (CSUs). The objectives of this work are twofold. At first, we study the joint problems of transmission time and power allocation for CSUs in uplink with the constraint of fairness. Afterwards, for the heterogeneous case with both the CSUs and DSUs, the resource allocation problem is formulated, and an iterative power water‐filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. The simulation results illustrate the performance of the proposed scheme and its superiority over other resource allocation schemes. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Modelling and Simulation of Interference Cancellation Receiver for MIMO Multicarrier CDMA based Cognitive Radio

In this paper, modeling and simulation of a receiver with interference cancellation technique for the downlink transmission of multiple input multiple output (MIMO) multicarrier code-division multiple access (MC-CDMA) based cognitive radio is investigated. Synchronous transmission of multiple users’ signals over frequency selective Rayleigh channel is considered. Linear MMSE frequency domain equalization is utilized to overcome channel effect. Orthogonal Variable Spreading Factor (OVSF) codes are used to support users of different data bit rates. An adaptive subcarrier deactivation method is proposed for deactivating a set of subcarriers used for downlink transmission by the cognitive radio base station that overlap with primary user’s transmission in order to avoid causing interference to the primary user. Orthogonality between the spreading codes is lost due to subcarrier deactivation which causes interference among secondary cognitive radio users. In this paper a modified Group-wise Successive Interference Cancellation (m-GSIC) technique is proposed to suppress the interference caused due to subcarrier deactivation. The simulation results show that the proposed interference cancellation technique results in better BER performance over conventional interference cancellation techniques

Joint Optimization of Clustering and Cooperative Beamforming in Green Cognitive Wireless Networks

We investigate the trade-off between performance and energy efficiency in cooperative cognitive underlay systems. By cooperating, cognitive base stations mitigate the interference and serve their users more effectively at the cost of spending more power. With a flexible cooperation scheme, we thus jointly optimize the clustering and the beamforming to minimize the overall power consumption while satisfying the secondary users' quality of service and respecting the primary users' interference limits. We formulate this problem as a mixed-integer nonlinear program and decompose it into a master problem and a beamforming subproblem. Then, we derive an iterative algorithm based on the generalized Benders decomposition method to find an optimal solution. Moreover, we propose simple techniques to speed up its convergence. When the clusters are fixed and non-overlapping, we also propose a decentralized algorithm with limited signaling schemes using the alternating direction method of multipliers. In contrast to previous works, our distributed algorithm handles the total interference power constraints coupling the cognitive base stations. Through simulations, we analyze the effectiveness and convergence of the proposed algorithms and show the benefits of the cooperation in cognitive wireless networks.

Dynamic-Dual-Threshold Cooperative Spectrum Sensing Algorithm Based on D-S Evidence Theory

To further improve the performance of traditional dual-threshold cooperative spectrum sensing, the article proposed a dynamic-dual-threshold cooperative spectrum sensing algorithm based on D-S evidence theory (DDDS). The article is applied the D-S evidence theory in dual-threshold sensing algorithm and set threshold value depending on the channel gain. If the received signal is outside of dual-threshold, the cognitive user will send the judgment result to the base station; otherwise the cognitive user will calculate trust information based on D-S evidence theory, and then transmit them to the cognitive base station. Cognitive base station makes the final decision.

Dynamic-Dual-Threshold Cooperative Spectrum Sensing Algorithm Based on D-S Evidence Theory

To further improve the performance of traditional dual-threshold cooperative spectrum sensing, the article proposed a dynamic-dual-threshold cooperative spectrum sensing algorithm based on D-S evidence theory (DDDS). The article is applied the D-S evidence theory in dual-threshold sensing algorithm and set threshold value depending on the channel gain. If the received signal is outside of dual-threshold, the cognitive user will send the judgment result to the base station; otherwise the cognitive user will calculate trust information based on D-S evidence theory, and then transmit them to the cognitive base station. Cognitive base station makes the final decision.