Channel Sensing Research Articles

Compressed Channel Sensing Algorithm Based on Cluster Sparsity

According to cluster structure feature for wideband sparse channel, the Cluster Regularized Adaptive Matching Pursuit (CRAMP) algorithm is presented based on the study of the original compressed channel sensing estimation algorithm. The proposed algorithm can accurately reconstruct the channel by both the adaptive process which chooses the number of candidate clusters and the regularization process which Support for secondary screening of candidate clusters, although the cluster sparsity of the channel is unknown. Simulation results show that the proposed algorithm’ performance is better than the BP, OMP, BOMP algorithm.

Compressed Channel Sensing using Designated Null Subcarriers in Full Duplex Wireless OFDM Systems

In this paper, we examine the digital cancellation of self-interference in full duplex wireless orthogonal frequency division multiplexing systems. Since digital cancellation highly depends on the accurate estimation of self-interference channels, which tends to be sparse, we propose a compressed sensing based channel estimator that uses designated null subcarriers. Our numerical results show that, by providing more accurate estimates of self-interference channels, the proposed scheme outperforms the conventional least square methods in terms of the self-interference suppression.

Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, the optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed.

Cognitive Radio Based State Estimation in Cyber-Physical Systems

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

On the Optimality of Myopic Sensing in Multi-State Channels

We consider the channel sensing problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and resource constrained jamming. The same problem arises in many other areas of science and technology as it is an instance of restless bandit problems. The communication system consists of N channels. Each channel is modeled as a multi-state Markov chain. At each time instant a user selects one channel to sense and uses it to transmit information. A reward depending on the state of the selected channel is obtained for each transmission. The objective is to design a channel sensing policy that maximizes the expected total reward collected over a finite or infinite horizon. This problem can be viewed as an instance of restless bandit problems, for which the form of optimal policies is unknown in general. We discover sets of conditions sufficient to guarantee the optimality of a myopic sensing policy; we show that under one particular set of conditions the myopic policy coincides with the Gittins index rule.

Optimal Channel Sensing for Heterogeneous Cognitive Networks: An Analytical Approach

The problem of optimal channel sensing in heterogeneous cognitive networks is considered to maximize the system throughput performance. The characteristics of an optimal operating sensing point maximizing the overall system rate are investigated under several rate criteria including the sum rate, the minimum of the primary and secondary rates, and the secondary rate with a guaranteed primary rate. Under the sum rate criterion, it is shown that the loss by imperfect sensing is no greater than half of the sum rate achieved by the perfect time sharing approach in a two user case if the sensing point is optimally designed.

Redox Regulation of Transient Receptor Potential Channels

Environmental and endogenous reactive species such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and other electrophiles are not only known to exert toxic effects on organisms, but are also emerging as molecules that mediate cell signaling responses. However, the mechanisms underlying this cellular redox signaling by reactive species remains largely uncharacterized. Ca2+-permeable cation channels encoded by the transient receptor potential (trp) gene superfamily are characterized by a wide variety of activation triggers that act from outside and inside the cell. Recent studies have revealed that multiple TRP channels sense reactive species and induce diverse physiological and pathological responses, such as cell death, chemokine production, and pain transduction. TRP channels sense reactive species either indirectly through second messengers or directly via oxidative modification of cysteine residues. In this review, we describe the activation mechanisms and biological roles of redox-sensitive TRP channels, including TRPM2, TRPM7, TRPC5, TRPV1, and TRPA1. The sensitivity of TRP channels to reactive species in vitro has been well characterized using molecular and pharmacological approaches. However, the precise activation mechanism(s) and in vivo function(s) of ROS/RNS-sensitive TRP channels remain elusive. Redox sensitivity of TRP channels has been shown to mediate previously unexplained biological phenomena and is involved in various pathologies. Understanding the physiological significance and activation mechanisms of TRP channel regulation by reactive species may lead to TRP channels becoming viable pharmacological targets, and modulators of these channels may offer therapeutic options for previously untreatable diseases.

Distributed and Centralized Schemes for Channel Sensing Order Setting in Multi-user Cognitive Radio Networks

Channel sensing order setting is crucial for efficient channel exploration and exploitation in cognitive radio (CR) networks. This paper investigates the sensing order setting problem in multi-channel multi-user CR networks for both distributed scenario and centralized scenario. As the optimal solution is too complicated, two suboptimal greedy search algorithms with much less computational complexities are proposed. The channel availability, channel achievable rate, multi-user diversity and collisions among CR users are considered comprehensively in our proposed methods. For the distributed scenario, a novel potential function is proposed to represent the relative advantage of a channel used by a user among multi channels and multi users, based on which each user can get its own sensing order. For the centralized scenario, a sensing matrix is obtained by a coordinator for all the users. It is shown that, CR users’ average throughput increases and collision probability decreases with the number of channels due to increased transmission opportunities. The total network throughput increases with the number of user pairs due to multi-user diversity. Simulation results validate the efficacy of the proposed schemes in elevating CR users’ throughput and decreasing the probability of collision, and show the performance improvement of the proposed schemes by comparisons with existing works.

Spectrum handoff scheme based on recommended channel sensing sequence

We consider the spectrum handoff delay of a Secondary User (SU) in cognitive radio networks. We propose a spectrum handoff scheme based on Recommended Channel Sensing Sequence (RCSS), which aims to optimise the spectrum handoff delay subject to the sensing reliability and link maintenance constraints. There are two cases that should be considered: 1) the SU performs spectrum handoff successfully during the current frame, and 2) the SU successfully performs the spectrum handoff using several frames. We develop a dynamic programming algorithm for RCSS to identify the optimal sensing sequence for the first case, and an updating algorithm for RCSS to improve the handoff performance for the second case. The numerical results confirm that the proposed RCSS handoff scheme can achieve better handoff delay performance than others when the received signal-to-noise ratios of the Pus' signals on different channels are non-identical.

A Joint Mechanism of Adaptive Bayesian Compressed Channel Sensing Based on Optimized Measurement Matrix

该文采用基于概率模型的贝叶斯压缩感知方法，从最大后验概率角度，给出了压缩信道感知的一般流程。在此基础上，利用自适应贝叶斯压缩感知将信号的重构和观测矩阵的设计结合，使这两个环节不再相互独立。同时，提出一种基于最优观测矩阵的自适应贝叶斯压缩感知联合机制，通过减少观测矩阵的相关度以及对观测矩阵的自适应设计，使得信道的重构效果更佳。另外可利用重构过程中得到的差错栏，对重构精确度进行衡量。仿真表明：在相同的实验条件下，该联合机制相比传统的重构算法，具有更好的抗噪声能力和重构精度。

ZigBee: Simulation and Investigation of Star and Mesh Topology by Varying Channel Sensing Duration

ZigBee was developed by IEEE 802.15.4 Task Group and ZigBee Alliance. ZigBee (IEEE 802.15.4‐2006 standard) is a category in the IEEE 802 family and ZigBee alliance is responsible for ZigBee standard which uses the transported services of the 802.15.4 network specification therefore ZigBee is a specification for a suite of high level communication protocols using small, low-power digital radios based on an IEEE 802 standard for personal area networks. ZigBee devices are often used in mesh network form to transmit data over longer distances, passing data through intermediate devices to reach more distant ones. This allows ZigBee networks to be formed ad-hoc, with no centralized control or high-power transmitter/receiver able to reach all of the devices. In this research paper, the effect of channel sensing duration on star and mesh topologies is analyze by varying Channel Sensing duration by using OPNET Modeler. The performance is compared in terms of Data Traffic send, Data Traffic Received and Mac Delay.

Voltage sensor ring in a native structure of a membrane-embedded potassium channel

Voltage-gated ion channels support electrochemical activity in cells and are largely responsible for information flow throughout the nervous systems. The voltage sensor domains in these channels sense changes in transmembrane potential and control ion flux across membranes. The X-ray structures of a few voltage-gated ion channels in detergents have been determined and have revealed clear structural variations among their respective voltage sensor domains. More recent studies demonstrated that lipids around a voltage-gated channel could directly alter its conformational state in membrane. Because of these disparities, the structural basis for voltage sensing in native membranes remains elusive. Here, through electron-crystallographic analysis of membrane-embedded proteins, we present the detailed view of a voltage-gated potassium channel in its inactivated state. Contrary to all known structures of voltage-gated ion channels in detergents, our data revealed a unique conformation in which the four voltage sensor domains of a voltage-gated potassium channel from Aeropyrum pernix (KvAP) form a ring structure that completely surrounds the pore domain of the channel. Such a structure is named the voltage sensor ring. Our biochemical and electrophysiological studies support that the voltage sensor ring represents a physiological conformation. These data together suggest that lipids exert strong effects on the channel structure and that these effects may be changed upon membrane disruption. Our results have wide implications for lipid-protein interactions in general and for the mechanism of voltage sensing in particular.

MULTICHANNEL COGNITIVE CROSS LAYER OPTIMIZATION FOR IMPROVED VIDEO TRANSMISSION

Multimedia applications particularly real-time video transmission in wireless networks, envisions end to end user perceived video quality as an important QoS parameter to be achieved. Cognitive Radio promising efficient spectrum utilization combined with Cross layer optimization is seen as a powerful combination to achieve the desired video quality. This study proposes Optimal Channel Sensed Multichannel Cognitive MAC (OCSM-CMAC), a QoS driven cross layer system for the joint optimization of different network parameters along the network protocol stack for the improved video transmission. The primary network activity and wireless propagation dependent channel quality are modeled. Depending on the availability of the primary channel and channel condition as provided by an optimal sensing scheme and the encoder parameter in the application layer, cognitive MAC scheduling and PHY layer modulation and coding for the secondary user are optimized to achieve the required QoS. The simulation of channel and the cognitive user activity is done in MATLAB, while the application video coding is performed by H.264/AVC JM 15.1 codec to obtain the results. The results of the proposed OCSM-CMAC scheme demonstrate that improved PSNR and delay performance is achieved under the optimal channel sensing scheme compared to the random sensing scheme.

Erratum to: Optimal Channel Sensing Order for Various Applications in Cognitive Radio Networks

Erratum to: Optimal Channel Sensing Order for Various Applications in Cognitive Radio Networks

Optimal Channel Sensing Order for Various Applications in Cognitive Radio Networks

In cognitive radio (CR) networks, the channel sensing order is crucial for the CR users to find an available channel as fast as possible. In this paper, besides the primary user activities, the statistics of Signal-to-Noise Ratio for each channel are explored using pilot signals. Based on the fluctuating nature of heterogeneous channels as well as the QoS requirements of various applications, two channel sensing order methods are proposed. For real-time applications, a minimum delay-based channel sensing order is proposed to find an idle channel which meets the sustainable rate constraint as fast as possible. For best-effort applications, a maximum capacity-based channel sensing order is proposed to maximize the transmission rates for the CR users, and two different stopping rules are considered. One is that a CR user should stop and transmit at the first free channel, while for the other one, a q-stage look-ahead stopping problem is considered. The simulation results show that the opportunity-discovery delay is reduced for real-time applications. For best-effort applications, the second stopping method is better than the first one when the time cost of the pilot signal is small.

Local and Global Interpretations of a Disease-Causing Mutation near the Ligand Entry Path in Hyperpolarization-Activated cAMP-Gated Channel

Hyperpolarization-activated, cAMP-gated (HCN) channels sense membrane potential and intracellular cAMP levels. A mutation identified in the cAMP binding domain (CNBD) of the human HCN4 channel, S672R, severely reduces the heart rate, but the molecular mechanism has been unclear. Our biochemical binding assays on isolated CNBD and patch-clamp recordings on the functional channel show that S672R reduces cAMP binding. The crystal structure of the mutant CNBD revealed no global changes except a disordered loop on the cAMP entrypath. To address this localized structural perturbation at a whole protein level, we studied the activity-dependent dynamic interaction between cAMP and the functional channel using the patch-clamp fluorometry technique. S672R reduces the binding of cAMP to the channels in the resting state and significantly increases the unbinding rate during channel deactivation. This study on a disease-causing mutation illustrates the important roles played by the structural elements on the ligand entry-exit path in stabilizing the bound ligand in the binding pocket.

Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing

Resources in cognitive radio networks (CRNs) should dynamically be allocated according to the sensed radio environment. Although some work has been done for dynamic resource allocation in CRNs, many works assume that the radio environment can perfectly be sensed. However, in practice, it is difficult for the secondary network to have the perfect knowledge of a dynamic radio environment in CRNs. In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the power and channel allocation problem as a mixed-integer programming problem under constraints. The computational complexity is enormous to solve the problem. To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Voltage-Dependent Gating of hERG Potassium Channels

The mechanisms by which voltage-gated channels sense changes in membrane voltage and energetically couple this with opening of the ion conducting pore has been the source of significant interest. In voltage-gated potassium (Kv) channels, much of our knowledge in this area comes from Shaker-type channels, for which voltage-dependent gating is quite rapid. In these channels, activation and deactivation are associated with rapid reconfiguration of the voltage-sensing domain unit that is electromechanically coupled, via the S4–S5 linker helix, to the rate-limiting opening of an intracellular pore gate. However, fast voltage-dependent gating kinetics are not typical of all Kv channels, such as Kv11.1 (human ether-à-go-go related gene, hERG), which activates and deactivates very slowly. Compared to Shaker channels, our understanding of the mechanisms underlying slow hERG gating is much poorer. Here, we present a comparative review of the structure–function relationships underlying activation and deactivation gating in Shaker and hERG channels, with a focus on the roles of the voltage-sensing domain and the S4–S5 linker that couples voltage sensor movements to the pore. Measurements of gating current kinetics and fluorimetric analysis of voltage sensor movement are consistent with models suggesting that the hERG activation pathway contains a voltage independent step, which limits voltage sensor transitions. Constraints upon hERG voltage sensor movement may result from loose packing of the S4 helices and additional intra-voltage sensor counter-charge interactions. More recent data suggest that key amino acid differences in the hERG voltage-sensing unit and S4–S5 linker, relative to fast activating Shaker-type Kv channels, may also contribute to the increased stability of the resting state of the voltage sensor.

Unsaturated Throughput Analysis of IEEE 802.11 DCF under Imperfect Channel Sensing

In this paper, throughput of IEEE 802.11 carrier-sense multiple access (CSMA) with collision-avoidance (CA) protocols in non-saturated traffic conditions is presented taking into account the impact of imperfect channel sensing. The imperfect channel sensing includes both missed-detection and false alarm and their impact on the utilization of IEEE 802.11 analyzed and expressed as a closed form. To include the imperfect channel sensing at the physical layer, we modified the state transition probabilities of well-known two state Markov process model. Simulation results closely match the theoretical expressions confirming the effectiveness of the proposed model. Based on both theoretical and simulated results, the choice of the best probability detection while maintaining probability of false alarm is less than 0.5 is a key factor for maximizing utilization of IEEE 802.11.

Energy-Efficient Design of Sequential Channel Sensing in Cognitive Radio Networks: Optimal Sensing Strategy, Power Allocation, and Sensing Order

Energy-efficient design has become increasingly important to battery-powered wireless devices. In this paper, we focus on the energy efficiency of a cognitive radio network, in which a secondary user senses the channels licensed to some primary users sequentially before it decides to transmit. Energy is consumed in both the channel sensing and transmission processes. The energy-efficient design calls for a careful design in the sensing-access strategies and the sensing order, with the sensing strategy specifying when to stop sensing and start transmission, the access strategy specifying the power level to be used upon transmission, and the sensing order specifying the sequence of channel sensing. Hence, the objective of this paper is to identify the sensing-access strategies and the sensing order that achieve the maximum energy efficiency. We first investigate the design when the channel sensing order is given and formulate the above design problem as a stochastic sequential decision-making problem. To solve it, we study another parametric formulation of the original problem, which rewards transmission throughput and penalizes energy consumption. Dynamic programming can be applied to identify the optimal strategy for the parametric problem. Then, by exploring the relationship between the two formulations and making use of the monotonicity property of the parametric formulation, we develop an algorithm to find the optimal sensing-access strategies for the original problem. Furthermore, we study the joint design of the channel sensing order and the sensing-access strategies. Lastly, the performance of the proposed designs is evaluated through numerical results.