Wireless network optimization via stochastic sub-gradient descent: Rate analysis

The layering principle has been long identified as a way to increase the interoperability and to improve the design of telecommunication protocols, where each layer offers services to adjacent upper layers and requires functionalities from adjacent lower ones. In the past, layering has enabled fast development of interoperable systems, but at the same time limited the performance of the overall architecture, due to the lack of coordination among layers. This issue is particularly relevant for wireless networks, where the very physical nature of the transmission medium introduces several performance limitations for protocols designed for wired networks. To overcome these limitations, a modification of the layering paradigm has been proposed, namely, cross-layer design, or “cross-layering.” Several cross-layering approaches have been proposed in the literature so far. Nevertheless, little formal characterization of the cross-layer interaction among different levels of the protocol stack is available yet. A clear need exists for identifying approaches able to analyze and provide quantitative guidelines for the design of cross-layer solutions, and, more importantly, to decide, in each case, whether cross-layering represents an effective solution or not. This chapter provides a detailed survey of the state-of-the-art and future directions in the usage of formal methods for cross-layer modeling and optimization of wireless networks. The text starts by detailing the principles of layered (ISO/OSI and TCP/IP) protocol stacks as well as the cross-layer paradigm. An overview of the architectures of existing and perspective wireless networks is presented along with an analysis of the potential limitations deriving from the layering approach and detailed description of possible optimization solutions enabled by cross-layer design. Subsequent sections are devoted to the issues of modeling and optimization of wireless networks. The remaining sections cover performance optimization as well as architecture optimization (specifically in terms of signaling). The chapter ends with a summary and outlines about future directions of research on the topic.

In nonsmooth stochastic optimization, we establish the nonconvergence of the stochastic subgradient descent (SGD) to the critical points recently called active strict saddles by Davis and Drusvyatskiy. Such points lie on a manifold M, where the function f has a direction of second-order negative curvature. Off this manifold, the norm of the Clarke subdifferential of f is lower-bounded. We require two conditions on f. The first assumption is a Verdier stratification condition, which is a refinement of the popular Whitney stratification. It allows us to establish a strengthened version of the projection formula of Bolte et al. for Whitney stratifiable functions and which is of independent interest. The second assumption, termed the angle condition, allows us to control the distance of the iterates to M. When f is weakly convex, our assumptions are generic. Consequently, generically, in the class of definable weakly convex functions, SGD converges to a local minimizer. Funding: The work of Sholom Schechtman was supported by “Région Ile-de-France”.

Cross layer design is a popular approach in mobile networks to combat the fast time-varying wireless characters. Cognitive network is also used for enhancing the networks' adaptation. In this paper, we proposed a novel cross layer design scheme based on principal component analysis (PCA) in cognitive network. In purpose of making networks adaptive to the changes of the environment, the statistics method is employed in cross layer design. In this paper, the PCA is proposed to analyze the parameters in network and their influence on the network performance. By this way, the cross layer design in cognitive network can be more effective and accurate.

In this article, we quantify the achievable spatial throughput of a multi-antenna Poisson cognitive radio network (CRN) collocated with a Poisson multi-antenna primary network. CR users employ Slotted-ALOHA medium access control. The success probability (SP) of a primary link is quantified in the presence of the secondary and primary interferers. It is demonstrated that two fold gains are experienced by employing multiple antennas at primary, i.e., (i) the fixed high desired SP threshold is met; (ii) CRs can also be accommodated without QoS deterioration. Further in this paper, the maximum permissible medium access probability (MAP) for CRN is derived from the link SP and primary users QoS constraint. The impact of the number of antennas and modulation employed at the primary on the permissible MAP of the CRN is also explored. Assuming that CR users employ multi-hop communication, QoS aware relaying with a radian sector forwarding area is studied. The average forward progress (AFP) and isolation probability for a CR user with QoS based connectivity is characterized under the permissible MAP. The spatial throughput for the CRN is quantified by the analysis of the AFP and the permissible MAP. It is shown that there exists an optimal MAP which maximizes the spatial throughput of the CRN. This optimal MAP is coupled with the permissible MAP, density of users, number of antennas and modulation schemes employed in both primary and secondary networks. Lastly, a few important design questions are investigated for multi-hop MIMO underlay CRNs.

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large.

Cognitive Radio (CR) is viewed as a revolutionary potential radio technology capable of offering dynamic spectrum access. CR Networks (CRNs) with such spectrum aware devices is a hopeful solution to the spectrum scarcity issue in wireless communication sector. In this paper, an effective routing solution with a cross layer design is proposed for the multi-hop CRNs. Most of the existing work uses Ad-hoc On-demand Distance Vector (AODV) protocol as the routing protocol for CRNs. In this work, a novel routing algorithm based on Routing Protocol for Low power and lossy networks (RPL) is being proposed for the CRNs. Routing is accomplished through the formation of colored Destination Oriented Directed Acyclic Graphs (DODAGs) of which the link frequencies are represented using colors. Hop count and adjacent link interference are counted as the routing metrics. Dynamic spectrum allocation is also done along with routing with the help of RPL. The CRN scenario is vulnerable to link failures due to the appearance of Primary Users (PUs). So route repairing using the Trickle algorithm offered by RPL is also implemented in this work. To implement this algorithm, a traffic aware scheduling is used. A MATLAB simulation is done to analyze the effectiveness of the proposed algorithm.

Cognitive Radio (CR) is an intelligent radio that can dynamically access the radio spectrum. Secondary users in Cognitive Radio Network (CRN) can access the licensed spectrum without causing harmful interference to primary users. The performance of cognitive radio networking functionalities depends on the properties of the spectrum band in use. This necessitates a crosslayer design in the entire CR networking protocol stack. Current researches are investigating different techniques of using cognitive radio to reuse more locally unused spectrums to increase the total system capacity. This paper provides a comprehensive survey of cross-layer design in cognitive radio network. The cross-layer design approach jointly considers the functions of the layers to maximize the performance of CR networks.

This paper considers the problem of cooperative spectrum sensing in cognitive radio networks (CRN). Communication in CRNs may be disrupted due to the presence of malicious secondary users (SU) or channel impairments such as shadowing. This paper proposes a spatio-frequency framework that can detect and track malicious users and anomalous measurements in CRNs. The joint problem of spectrum sensing and malicious user identification is posed as an optimization problem that aims to exploit the sparsity inherent to both, spectrum occupancy and malicious user occurrence. Proposed scheme obtains improved performance by utilizing node location information, and can handle missing or inaccurate location information, and noisy SU reports. A distributed block-coordinate descent-based algorithm is proposed, that outperforms the state-of-the-art PCA-based approach, and is flexible enough to defeat a variety of attacks encountered in SU networks. Simulation results demonstrate the efficacy of the proposed algorithm.

Outlier-aware cooperative spectrum sensing in cognitive radio networks

The cognitive smart grid (SG) communication paradigm aims to mitigate quality of service (QoS) issues in obsolete communication architecture associated with the conventional electrical grid. This paradigm entails the integration of advanced information and communication technologies (ICTs) into power grids, enabling a two-way flow of information. However, due to the exponential increase in wireless applications and services, also driven by the deployment of the Internet of Things (IoT) smart devices, SG communication systems are expected to handle large volumes of data. As a result, the operation of SG networks is confronted with the major challenge of managing and processing data in a reliable and secure manner. The existing works in the literature proposed architectures with the objective to mitigate the underlying QoS issues such as latency, bandwidth, data congestion, energy efficiency, etc. In addition, a variety of communication technologies have been analyzed for their capacity to support stringent QoS requirements for diverse SGs environments. This notwithstanding, a standard architecture designed to mitigate the aforementioned issues for SG networks remains a work-in-progress. The main objective of this paper is to investigate the emerging technologies such as cognitive radio networks (CRNs) as part of the Fifth-Generation (5G) mobile technology for reliable communication in SG networks. Furthermore, a hybrid architecture based on the combination of fog computing and cloud computing is proposed. In this architecture, real-time latency-sensitive information is given high priority, with fog edge based servers deployed in close proximity to home area networks (HANs) for preprocessing and analyzing of information collected from smart IoT devices. In comparison to the recent works in the literature, which are mainly based on CRNs and 5G separately, the proposed architecture in this paper incorporates the combination of CRNs and 5G for reliable and efficient communication in SG networks.

This paper investigates the power allocation between the cognitive transmitter (CT) and the cognitive relay (CR) in a cognitive Amplify-and-Forward (AF) full-duplex relay network. To maximize the cognitive capacity, we firstly obtain a closed form expression of the optimal power allocation with the instantaneous interference channel information. Then considering limited cooperation between the primary and the cognitive networks, we develop an efficient iterative search algorithm to obtain the optimal power allocation with the statistical rather than the instantaneous interference channel information. Furthermore, considering absolutely no cooperation between the primary and the cognitive networks, we develop a sensing-based iterative search algorithm to obtain the optimal power allocation without any interference channel information. Simulation results show the capacity of the cognitive AF full-duplex relay network may achieve up to 50% improvement compared with the cognitive AF half-duplex relay network on the condition that the mean value of the self-interference channel gain is lower than −25 dB.

Cognitive radio is a promising technology for sharing the underutilized frequency bands that have been licensed to primary users. However, due to the uncertainty in detecting the existence of the primary user, the secondary user may interfere with the primary users when both primary and secondary users are active simultaneously. Therefore, understanding the interference and its consequences on the cognitive network is critical. Unlike the statistical models previously reported in the literature that aim at approximation of the interference, based on the solid mathematical analysis, we propose an accurate model for describing the co-channel interference with probability density function, cumulative distribution function, mean, and variance of the interference suffered by the primary users. The proposed model not only takes into account a number of factors, such as the spectrum-sensing scheme, the spatial distribution of secondary users, and the channel conditions, including shadowing and Nakagami fading, but also gives an exact mathematical expression of the influences from these factors. The developed framework supports practical applications such as evaluating the cognitive network of any spatial shape and density of the secondary users and the methods of power control and spectrum sensing used by the secondary users. Simulation results are provided to verify the effectiveness of the analytical model.

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Cognitive Radio (CR) is an intelligent technology that has become popular in wireless networks due to its ability to solve problems such as the underutilization and inefficient utilization of the spectrum. The CR can learn from experience, adapt its internal states and modify its transmission parameters. Radio Frequency is a costly resource, which must be administrated and coordinated wisely in order to plan for future transformations. Cross layer design (CLD) is best characterized as a transformation from the reference design model that does not permit direct communication between non-adjacent layers or the sharing of information. CLD focusses on the system performance enhancement by empowering various layers of the communication stack to share state data or to facilitate cross layer communication to enhance the system performance. Therefore, the idea of CLD must be contrasted with the traditional layered approach. This paper compares the throughput performance of the three schemes; the Cognitive Ad Hoc On Demand Distance Vector, Cognitive Cross-layer Multipath Probabilistic Routing, and the Optimized Cognitive Cross-layer Multipath Probabilistic Routing of the CLD routing and Media Access Control protocols in Cognitive Radio Networks.

In this paper, we discuss the framework of cognitive radio network and OFDMA technology first, and analysis the motivations for cross-layer design in cognitive radio network (CRN). Moreover, we proposed the OFDMA-based cognitive radio transceiver. Finally, a novel architecture in which the dynamic channel allocation is achieved by a cross-layer design between the PHY and MAC layers for OFDMA-based cognitive radio network is proposed. The optimization of joint power control and link scheduling on the cross-layer architecture of OFDMA-based CRN has been simulated. Simulation shows it can improve network performance.