Heterogeneous Cognitive Networks Research Articles

A Two-Stage Matching Game and Repeated Auctions for Users Admission and Channels Allocation in 5G HetNets

The fifth-generation (5G) wireless cellular networks aim to increase the users’ capacities and quality of experience as well as to integrate different wireless bands and modes of access. The introduction of cognitive radio technology and heterogeneous networks (HetNets) as part of the architecture of the 5G aims to efficiently reuse the available spectrum. This paper presents a two-stage framework based on matching theory and auction games with the objective of efficiently admitting secondary users to the wireless scene. In the first stage, a fast convergence matching game for the users admission problem in 5G HetNets is considered, where secondary users are associated to appropriate secondary base stations. These base stations access the available primary spectrum on behalf of its associated users in the next stage, namely a repeated modified English auction. Results show the existence of a stable matching point for the users admission game and a Walrasian equilibrium point for the repeated auctions. In addition, extensive simulations are performed to compare the proposed repeated auction against the single auction and the matching theory. It is proven that the repeated auction game outperforms the single auction game and matching game in terms of the occupancy of primary channels and the satisfaction of the secondary users.

A vertical handoff decision scheme using subjective-objective weighting and grey relational analysis in cognitive heterogeneous networks

With the limitation and low utilization rate of spectrum resources, how to maintain the best continuous network connection for user while reducing the total number of handoff has become an urgent problem to be solved in the handoff decision of cognitive heterogeneous networks . Therefore, a cognitive heterogeneous network vertical handoff decision scheme combining subjective-objective weighting and grey relational analysis (GRA) is proposed. Firstly, an agent-based decision-making framework is proposed based on the review of relevant literature; secondly, the attributes weighting algorithm combining subjective-objective weighting is proposed; thirdly, the steps of analytic hierarchy process (AHP), entropy weighting method, and GRA method are analyzed in detail; then an example is given to analyze the proposed algorithm in single decision point, and the performance of the proposed algorithm is compared with that of three similar multi-attribute decision making (MADM) algorithms; lastly, some problems and challenges of the existing vertical handoff MADM decision-making algorithm are discussed, and the future research directions are proposed. Simulation results show that the proposed method is effective in reducing the total handoff number and ranking anomaly rate.

Cognitive Heterogeneous Wireless Network and Artificial Intelligence-Based Supply Chain Efficiency Optimization Application.

Cognitive radio can specifically perceive the surrounding environment, can understand and learn the changes in the surrounding environment, and can adjust its own adaptation method, channel calculation, and other transmission parameters at any time. This cognitive radio method is more flexible and is an intelligent wireless communication system, a relatively advanced system. Cognitive communication devices can detect changes in the surrounding wireless environment and relatively change their own parameters to cope with the changes in the environment and ensure the stability of communication quality. As a different part of the supply chain, enterprises use the sharing economy and cooperation to improve the overall competitiveness of the enterprises in the supply chain, ultimately achieve a win-win effect for all enterprises in the supply chain, and complete the improvement of the company's competitiveness. The supply chain model can help companies to be familiar with the structure, operation process, and orientation of the supply chain in which they are located, so that they can clearly understand the work process through simulation scenarios and better understand the problems of the supply chain work process. Supply chain supply with cognitive heterogeneous wireless networks and artificial intelligence is a necessary way to deeply improve the operation and working process of supply chain control. In short, the cognitive radio system proposed in this study can receive network-related messages by observing the surrounding wireless network environment, and can receive internal communication tasks and emergency communication tasks based on historical data and prediction of future relationships. Through the access control process, we make an appropriate strategy and finally complete the communication function of the system.

Joint Video Packet Assignment, Power Control and User Scheduling Over Cognitive Multi-Homing Heterogeneous NOMA Networks

Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

Max-Min Energy-Efficient Optimization for Cognitive Heterogeneous Networks With Spectrum Sensing Errors and Channel Uncertainties

In this letter, we design a robust resource allocation algorithm for a non-orthogonal multiple access (NOMA)-based cognitive heterogeneous network under spectrum sensing errors and channel uncertainties. Our objective is to maximize the minimum energy efficiency of femtocells subject to the maximum power constraint, the cross-tier interference constraint and the minimum rate constraint. The infinite-dimensional robust constraints with channel uncertainties are converted into convex ones by using a worst-case transformation approach. Then, the deterministic non-convex problem is transformed into a convex one by using convex optimization theory. Numerical results demonstrate that the proposed algorithm can achieve fast convergence and strong robustness.

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

An Opportunity Downlink Interference Alignment Algorithm Based on Punch Scheduling in Cognitive Heterogeneous Networks

For the hierarchical structure and frequency reuse of cognitive heterogeneous networks, it is inevitable to effectively deal with interference while gaining capacity. In this article, we consider the downlink cognitive heterogeneous network, in which a macro base station can serve a certain number of macro users, and each macro cell is composed of a certain number of micro cells. An opportunistic downlink interference alignment (ODIA) algorithm for cognitive heterogeneous networks based on puncture scheduling is proposed. First, we designed a pair of precoding and decoding matrices to eliminate co-layer interference. Second, in order to reduce the overhead required to transmit system state information(CSI), we proposed a user scheduling algorithm that not only can obtain a larger interference-free size, but also improvements in fairness, system capacity and overflow probability. The simulation results show that the proposed ODIA scheme is superior to the existing interference management algorithms in terms of interference, system capacity and fairness of system resource allocation.

Distributed Joint Optimization of Beamforming and PowerAllocation for Maximizing the Energy Efficiency of CognitiveHeterogeneous Networks.

This paper investigated an energy-efficient beamforming and power allocation strategy for cognitive heterogeneous networks with multiple-input-single-output interference channels. To maximize the sum energy efficiency of secondary users (SUs) while keeping the interference to primary networks under a predetermined threshold, I propose a distributed resource allocation algorithm using dual methods, in which each SU updates its beamforming vector and transmit power iteratively without any information sharing until convergence. The simulation results verify that the performance of the proposed scheme is comparable to that of the optimal scheme but with a much shorter computation time.

A Novel Clustering Scheme For Heterogeneous Cognitive Radio Wireless Sensor Networks

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019.
 Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads.
 Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime.
 Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption.
 Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption.
 Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition.
 Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors.
 Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes.

Computing Energy Efficiency for a Cognitive Heterogeneous Satellite Network Based on Interference Management

Computing Energy Efficiency for a Cognitive Heterogeneous Satellite Network Based on Interference Management

Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study.

BackgroundAlthough some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests.ObjectiveThis study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors.MethodsOur study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation.ResultsA total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests.ConclusionsWe developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

Power, Subcarrier Allocation and Relay Selection with Interference Managements for OFDM-based Cognitive Relay Heterogeneous Networks

Power, Subcarrier Allocation and Relay Selection with Interference Managements for OFDM-based Cognitive Relay Heterogeneous Networks

Double Threshold Weighted Energy Detection for Asynchronous PU Activities in the Presence of Noise Uncertainty

Cognitive Heterogeneous Network (CHN) is a network that provides heterogeneous services on a cognitive radio basis. The existence of such heterogeneous services introduces a syndrome of concurrent problems. Handling each problem separately may result in more consequences. This paper aims to mitigate the combined effects of noise uncertainty and asynchronous primary user occurrence within the sensing interval of the secondary user in cognitive heterogeneous network. The paper proposes a double threshold energy detection method based on unequal scale sampling criteria. An analytic formula is deduced for both thresholds employing the power scaling criteria. Simulation results show a complete agreement with the performance expectations in terms of increased probability of detection while keeping the false alarm probability within the required limits, which is guaranteed by plotting the ROC curves. Moreover, an optimal threshold is formulated to minimize the overall detection errors, that led to further enhancements in performance as illustrated also via simulations.

Joint energy-efficient resource allocation and transmission duration for cognitive HetNets under imperfect CSI

Cognitive heterogeneous network (CR-HetNet) has been deemed a promising technique to improve spectral efficiency and system capacity. Designing effective resource allocation (RA) and transmission duration algorithms can further improve energy efficiency (EE) of communication systems. Different from the existing EE RA algorithms, in this paper, we focus on robust EE-based maximization RA problem by jointly optimizing power allocation, subcarrier allocation and transmission duration under the consideration of spectrum sensing errors and channel uncertainties simultaneously. The formulated optimization problem is an NP-hard and non-convex problem that is challenging to solve, especially in the case of a large number of users. A suboptimal algorithm is proposed to achieve subcarrier allocation for each femtocell user, and a Taylor expansion approximation is used to determine the optimal transmission duration. Furthermore, a Bernstain approximation approach is introduced to convert the optimization problem with uncertain constraints into a convex one which is solved by an iteration-based algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing with the existing algorithms in terms of robustness and EE.

Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

Aiming at the research of resource allocation and interference management in multi-layer wireless networks in heterogeneous cognitive networks, this paper mainly discusses the resource allocation and interference management in heterogeneous cognitive networks and proposes our proposed cluster-based cooperative interference management scheme. This scheme minimizes the cross-layer interference of multiple base station cells to primary users and avoids the same-layer interference between base station cells through joint channel allocation and power allocation. Through cognitive perception or joint scheduling, the available channel can be obtained by the home base station cell, and then the channel, power and other resources can be allocated according to the related algorithms proposed by us, so as to ensure the harmonious coexistence of the home base station cell and the authorization system such as macrocell. Finally, we validate the proposed algorithm. The simulation results show that the algorithms are effective in reducing interference, but the degree is different.

Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

Reducing the interference to the licensed mobile users and obtaining the energy efficiency are key issues in cognitive heterogeneous networks. A corresponding rate loss constraint is proposed to be used for the sensing-based spectrum sharing (SBSS) model in cognitive heterogeneous networks in this paper. Resource allocation optimization strategy is designed for the maximum energy efficiency under the proposed interference constraint together with average transmission power constraint. An efficiency algorithm is studied to maximize energy efficiency due to the nonconvex optimal problem. Furthermore, the relationship between the proposed protection criterion and the conventional interference constraint strategy under imperfect sensing condition for the SBSS model is also investigated, and we found that the conventional interference threshold can be regarded as the upper bound of the maximum rate loss that the primary user could tolerate. Simulation results have shown the effectiveness of the proposed protection criterion overcome the conventional interference power constraint.

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by MCS switchings, we further introduce a switching cost factor in the proposed algorithm. Simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90 percent to 100 percent of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, and is 30 percent to 100 percent higher than those of the benchmark algorithms. Meanwhile, the proposed algorithm with the switching cost factor can achieve a better balance between the primary transmission rate and system overheads than both the optimal algorithm and benchmark algorithms.

Multi-objective optimization method for spectrum allocation in cognitive heterogeneous wireless networks

In a multi-heterogeneous network dense deployment and convergence environment, the cognitive radio technology dynamically utilizes the idle spectrum resources of each primary network, which can effectively improve the data transmission rate and system capacity, and is also a key technology of the next generation wireless communication network. However, how to efficiently and reasonably select the network and allocate idle spectrum resources to meet the low cost and large traffic demand of users is a difficult problem in the case of dynamic coupling with multiple heterogeneous primary networks overlapping coverage and diversified user requirements. In this paper, with the goal of maximizing the total bandwidth and minimizing the total cost, a multi-objective optimization mathematical model for network selection and idle spectrum allocation is established in the context of comprehensive consideration of the difference of spectrum resource attributes of different primary networks in the network domain and the diversification of secondary user requirements in the user domain, and the complexity of the problem is analyzed. Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this paper. The first is the simplification method. By analyzing the relationship between optimization objectives and constraints, simplification method simplifies the objective function and constraints of the mathematical model and transforms the complex allocation problem into a standard form of the 01 programming problem. The second is the intelligent optimization algorithm. By improving the traditional multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), an improved NSGA-II(INSGA-II) method is proposed, which combines the interference constraints of the primary network and the service quality requirements of the secondary users into the objective value evaluation of NSGA-II algorithm, while making the decision selection on the optimal solution set to select a compromise solution. Finally, the performance of this two methods is compared and analyzed through experiments. The experimental results show that the simplified method has higher efficiency advantages, and the INSGA-II algorithm can often meet the user’s service requirements at a lower cost, especially the cost priority strategy.

Resource Allocation in Heterogeneous Cognitive Radio Network With Non-Orthogonal Multiple Access

In this paper, we study resource allocation problems for a two-tier cognitive heterogeneous network in interweave spectrum sharing mode. Secondary users (SUs) in small cells (SCs) opportunistically access the licensed spectrum resources. Non-orthogonal multiple access (NOMA) is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes. Practically, there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth, which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs. Aiming to maximize the sum throughput of second-tier SCs network, we formulate a mixed integer non-linear programming problem with the constraints of the available idle bandwidth, the successive interference cancellation complexity, the transmission power budget, and the minimum data requirements. To efficiently solve this problem, we decompose the original optimization problem into bandwidth resource allocation subproblem, SUs clustering subproblem, and power allocation subproblem. Based on the scale of SCs network and the activities of licensed spectrum, we introduce an optimal bandwidth configuration to maximize the average sum throughput of SCs. By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU, we develop a novel SUs clustering algorithm which can improve the throughput of a cluster by grouping SUs with more distinctive channel conditions. With the results of SUs clustering, we propose power allocation within a NOMA cluster by using Karush-Kuhn-Tucker optimality conditions. Furthermore, we perform power allocation across NOMA clusters by using the difference of convex programming. The simulation results validate the performance of the proposed resource allocation algorithms.

Cognitive Heterogeneous Networks With Multiple Primary Users and Unreliable Backhaul Connections

Wireless backhaul has emerged as a suitable and flexible alternative to wired backhaul; however, it is not as reliable as its wired counterpart. This paper presents, for the first time, a comprehensive model including a heterogeneous underlay cognitive network with small cells also acting as multiple secondary users, multiple primary users, and unreliable wireless backhaul. In this system, a macro-base station connects to multiple secondary transmitters via wireless backhaul links. In addition, multiple secondary transmitters send information to a secondary receiver by sharing the same spectrum with multiple primary users. A Bernoulli process is adopted to model the backhaul reliability. A selection combining protocol is used at the secondary receiver side to maximize the received signal-to-noise ratio. We investigate the impact of the number of secondary transmitters, the number of primary users, as well as the backhaul reliability on the system performance in Rayleigh fading channels. Two key constraints are considered on the system performance: 1) maximum transmit power at the secondary transmitters and 2) peak interference power at the primary users caused by secondary transmitters. Closed-form expressions for outage probability, ergodic capacity, and symbol error rate and the asymptotic expressions for outage probability and symbol error rate are derived. Moreover, closed-form expressions are also applicable to non-cooperative scenarios.