Cooperative Channel and Optimized Route Selection in Adhoc Network

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is enormous.Its uses have expanded in lockstep with its growth.Due to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is essential.In this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the confidence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization technique.At that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated forwarder.The simulation work is validated in this section using the MATLAB program.Additionally,steps show how the node acts as a confident forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between them.We cal-culate the confidence of the node at the start of the network by combining the reliability report for thefirst hop and the reliability report for the secondary hop.We then refer to the same node as the confident node in order to operate as a forwarder.As a result,we witness an increase in the leftover energy in the output.The percentage of data packets delivered has also increased.

Anti-jamming channel access in 5G ultra-dense networks: a game-theoretic learning approach

This paper investigates the Quality of Experience(QoE)oriented channel access anti-jamming problem in 5th Generation Mobile Communication(5G)ultra-dense networks.Firstly,considering that the 5G base station adopts beamforming technology,an anti-jamming model under Space Division Multiple Access(SDMA)conditions is proposed.Secondly,the confrontational relationship between users and the jammer is formulated as a Stackelberg game.Besides,to achieve global optimization,we design a local cooperation mechanism for users and formulate the cooperation and competition among users as a local altruistic game.By proving that the local altruistic game is an Exact Potential Game(EPG),we further prove the existence of pure strategy Nash Equilibrium(NE)among users and Stackelberg Equilibrium(SE)between users and jammer.Thirdly,to obtain the equilibrium solutions of the proposed games,we propose an anti-jamming channel selection algorithm and improve its convergence speed through heterogeneous learning parameters.The simulation results validate the convergence and effectiveness of the proposed algorithm.Compared with the throughput optimization scheme,our proposed scheme obtain a greater network satisfaction rate.Finally,we also analyze user fairness changes during the algorithm convergence process and get some interesting conclusions.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

A New Infrared Atmospheric Sounding Interferometer Channel Selection and Assessment of Its Impact on Met Office NWP Forecasts

A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.

Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing

A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.

A new layered space-time detection algorithm for frequency selective fading multiple-input multiple-output channels based on particle filter

In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.

Multi-Agent Few-Shot Meta Reinforcement Learning for Trajectory Design and Channel Selection in UAV-Assisted Networks

Unmanned aerial vehicle(UAV)-assisted communications have been considered as a solution of aerial networking in future wireless networks due to its low-cost, high-mobility, and swift features. This paper considers a UAV-assisted downlink transmission,where UAVs are deployed as aerial base stations to serve ground users. To maximize the average transmission rate among the ground users, this paper formulates a joint optimization problem of UAV trajectory design and channel selection, which is NP-hard and non-convex. To solve the problem, we propose a multi-agent deep Q-network(MADQN) scheme.Specifically, the agents that the UAVs act as perform actions from their observations distributively and share the same reward. To tackle the tasks where the experience is insufficient, we propose a multi-agent meta reinforcement learning algorithm to fast adapt to the new tasks. By pretraining the tasks with similar distribution, the learning model can acquire general knowledge. Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation. Furthermore, our proposed multiagent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.

A HIGH SENSITIVITY AND WIDE DYNAMIC RANGE ZERO-IF RF RECEIVER FOR COGNITIVE RADIO APPLICATION

This paper presents an RF receiver of zero-Intermediate Frequency(IF) architecture for Cognitive Radio(CR) communication systems.Zero-IF architecture reduce the image reject filter and IF filter,so it is excellent in low cost,compact volume,and low power dissipation.The receiver employs three digital attenuator and a high gain,high linearity low noise amplifier to achieve wide dynamic range of 70 dB and high receiving sensitivity of-81 dBm.A fully balanced I/Q demodulator and a differential Local Oscillator(LO) chips are used to minimize the negative effects caused by second-order distortion and LO leakage.In order to select an 8 MHz-channel from 14 continuous ones located in UHF band(694-806 MHz) accurately,approach of channel selectivity circuits is proposed.The RF receiver has been designed,fabricated,and test.The measured result shows that the noise figure is 3.4 dB,and the error vector magnitude is 7.5% when the input power is-81 dBm.

Peak-to-Average Power Ratio Performance Analysis for Orthogonal Chirp Division Multiplexing Multicarrier Systems Based on Discrete Fractional Cosine Transform

In doubly selective fading channels, the orthogonal frequency division multiplexing (OFDM) multicarrier system may fail. Chirp like basis (fractional Fourier transform-fractional cosine transform) may be used instead of complex exponential basis in this case to improve the system performance. However, in multicarrier transmission, the high peak to average power ratio (PAPR) of the transmitted signal is one of the difficult problems that face both the chirp and the exponential basis. In this paper, an evaluation for the PAPR performance of a multicarrier system based on the fractional cosine transform (FrCT) is introduced and then compared with DFrFT and FFT. Moreover, applying the SLAM technique over these systems is provided to understand the behaviour of these systems when applying SLAM. Simulations verify that this system obtains a better PAPR performance. Moreover, further PAPR reduction can be gained using the well-known PAPR reduction methods. Moreover, applying SLAM technique improves the performance of (dB) by 4 dB to 5 dB and all systems become as competitive to each other when SLAM is applied. Finally, BER performance comparison among OFDM, Discrete Cosine Transform MCM (DCT- MCM), Discrete Hartley Transform MCM (DHT-MCM), DFrFT-OCDM and DFrCT- OCDM MCM systems was done by means of simulation over 100,000 multicarrier blocks for each one and showed that our proposed scenario gave the best performance.

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

Performance analysis of hybrid carrier DS CDMA communication system

A hybrid carrier (HC) DS CDMA communication system is proposed, combining 4-weighted fractional Fourier transform (4-WFRFT) with code division multiple access (CDMA) technique. The signals are modulated in a certain order fractional Fourier domain and transformed by 4-WFRFT to compose the hybrid carrier signals. In the time domain CDMA technique is adopted for multiple accesses and time diversity gain. Compared to orthogonal frequency division multiplexing (OFDM) system, in which Fourier transform is adopted, the signal energy in HC system is distributed on the time-frequency plane more evenly and symmetrically. Thus, when there is a deep fading notch or single-frequency interference in the channel, the proposed method can split the interference to a broader range in order to reduce the influence, resulting in the better system performance. Moreover, the performances of the proposed system, such as peak-to-average power rate (PAPR) and security are also discussed in the paper.

Handover Mechanism Based on Underwater Hybrid Software-Defined Modem in Advanced Diver Networks

For the past few decades,the internet of underwater things(IoUT)otained a lot of attention in mobile aquatic applications such as oceanography,diver network monitoring,unmanned underwater exploration,underwater surveillance,location tracking system,etc.Most of the IoUT applications rely on acoustic medium.The current IoUT applications face difficulty in delivering a reliable communication system due to the various technical limitations of IoUT environment such as low data rate,attenuation,limited bandwidth,limited battery,limited memory,connectivity problem,etc.One of the significant applications of IoUT include monitoring underwater diver networks.In order to perform a reliable and energy-efficient communication system in the underwater diver networks,a smart underwater hybrid softwaredefined modem(UHSDM)for the mobile ad-hoc network was developed that is used for selecting the best channel/medium among acoustic,visible light communication(VLC),and infrared(IR)based on the criteria established within the system.However,due to the mobility of underwater divers,the developed UHSDMmeets the challenges such as connectivity errors,frequent link failure,transmission delay caused by re-routing,etc.During emergency,the divers are most at the risk of survival.To deal with diver mobility,connectivity,energy efficiency,and reducing the latency in ADN,a handover mechanism based on pre-built UHSDM is proposed in this paper.This paper focuses on(1)design of UHSDM for ADN(2)propose the channel selection mechanism in UHSDM for selecting the best medium for handover and(3)propose handover protocol inADN.The implementation result shows that the proposed mechanism can be used to find the new route for divers in advance and the latency can be reduced significantly.Additionally,this paper shows the real field experiment of air tests and underwater tests with various distances.This research will contribute much to the profit of researchers in underwater diver networks and underwater networks,for improving the quality of services(QoS)of underwater applications.

Doubly selective fading channel estimation in MIMO OFDM systems

High data transmission rates and high mobility give rise to time- and frequency-selectivity in wireless communication channels. This paper investigated time and frequency doubly selective channel estimation using pilot tones among Multi-Input Multi-Output （MIMO） orthogonal frequency division multiplexing （OFDM）. Firstly, a complex exponential basis expansion channel model （BECM） was introduced to represent doubly selective channel during an OFDM symbol period; then, based on BECM, an effective MIMO-OFDM doubly selective channel estimation method was presented; finally, the optimality in designing pilot tones parameters was done according to the minimization of channel estimation MSE, mainly including the number, the placement and the structure of pilot tones. Simulation results show that the proposed estimation method has good performance in doubly selective channel scenarios and confirms the theoretical analysis findings.

A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks

In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring （QoM） of the network. In this paper, a particle swarm optimization （PSO）-based solution is proposed to achieve the optimal channel selection. A 2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO （MC-PSO） algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.

A high linearity multi-band and gain adjustable channel-select filter for TV-tuner application

This paper presents a channel-select filter that employs an active-RC bi-quad structure for TV-tuner application. A design method to optimize the IIP3 of the bi-quad is developed. Multi-band selection and gain adjustment are implemented using switching resistors in the resistor array and capacitors in the capacitor array. Q-factor degradation is compensated by a tuning segmented resistor. A feed-forward OTA with high gain and low third-order distortion is applied in the bi-quad to maximize linearity performance and minimize area by avoiding extra compensation capacitor use. An RC tuning circuit and DC offset cancellation circuit are designed to overcome the process variation and DC offset, respectively. The experimental results yield an in-band IIP3 of more than 31 dBm at 0 dB gain, a 54 dB gain range with 6 dB gain step, and a continuous frequency tuning range from 0.25 to 4 MHz. The in-band ripple is less than 1.4 dB at high gain mode, while the gain error and frequency tuning error are no more than 3.4% and 5%, respectively. The design, which is fabricated in a 0.18 μm CMOS process, consumes 12.6 mW power at a 1.8 V supply and occupies 1.28 mm2.

Channel selection against electrode shift enables robust myoelectric control without retraining

Myoelectric controlled interfaces driven by muscle activities have achieved good performance in ideal conditions and showed many potential medical-related and industrial applications.However,in practical applications,the performance could be drastically degraded due to the electrode(sensor)shift,which is inevitable in donning and doffing the system.In this study,we presented a novel channel selection method against electrode shift for robust pattern-recognition based myoelectric control.The proposed method was evaluated on twenty-four subjects,including twenty-two able-bodied subjects and two amputees,and compared with two traditional channel selection methods,i.e.,uniform selection(UNI)and sequential feature selection(SFS).We demonstrated that the offline error rates of the proposed method were significantly lower than those of the other two methods(P<0.05),and its online performance in shift conditions was comparable to that in ideal conditions.These outcomes benefit the practical applications of robust myoelectric controlled interfaces.

Channel List Selection Based on Quality Prediction in WirelessHART Networks

WirelessHART is one of the most widely used technologies in industrial wireless networks.However,its performance is highly influenced by the quality of wireless channels.To improve the reliability of wireless communications,WirelessHART employs channel blacklisting and channel hopping mechanisms,which highlights the importance of channel assessment.Traditional methods generally resort to packet reception ratio(PRR)of the previous time slot to assess and allocate channels,but this is not accurate.In this paper,we propose a learning-based framework for predicting the PRR,and on the basis of the predicted PRR,we develop a heuristic channel selection algorithm to confirm the channel list,which takes into account the balance of channel diversity and route diversity.Simulation results demonstrate that our algorithm outperforms existing ones in terms of achieved reliability.

Iterative estimation of doubly selective channels with ICI suppression for OFDM using KL-BEM

Channel estimation for orthogonal frequency division multiplexing （OFDM） systems operating over doubly selective channels is complicated by the existence of intercarrier interference （ICI）.This paper extends the idea of estimating time-variant channel impulse response （CIR） via time-averaged CIR proposed by Hijazi,and proposes a low-complexity channel estimation scheme with ICI suppression based on Karhunen-Loeve basis expansion model （KL-BEM）,which is in a more general form.In the proposed scheme,the KL-BEM coefficients are obtained through their numerical relationship with the time-averaged CIR.The estimation performance is improved by adopting an expectation-maximization （EM）-based iterative structure combined with a low-complexity ICI equalizer and canceller.Theoretical analysis and simulation results show that the proposed scheme achieves superior performance over the original one with lower computational complexity.

A binding-block ion selective mechanism revealed by a Na/K selective channel

Mechanosensitive （MS） channels are extensively stud- ied membrane protein for maintaining intracellular homeostasis through translocating solutes and ions across the membrane, but its mechanisms of channel gating and ion selectivity are largely unknown. Here, we identified the Ynal channel as the Na^＋/K^＋ cation-selec- tive MS channel and solved its structure at 3.8 A by cryo- EM single-particle method. Ynal exhibits low conduc- tance among the family of MS channels in E. coil, and shares a similar overall heptamer structure fold with previously studied MscS channels. By combining structural based mutagenesis, quantum mechanical and electrophysiological characterizations, we revealed that ion selective filter formed by seven hydrophobic methionine （Ynal^Met158） in the transmembrane pore determined ion selectivity, and both ion selectivity and gating of Ynal channel were affected by accompanying anions in solution. Further quantum simulation and functional validation support that the distinct binding energies with various anions to Ynal^Met158 facilitate Na^＋/K^＋ pass through, which was defined as binding-block mechanism. Our structural and functional studies provided a new perspective for understanding the mechanism of how MS channels select ions driven by mechanical force.