Adaptive Channel Research Articles

Adaptive Channel Assignment With Predictions of Sensor Results and Channel Occupancy Ratio in PhyC-SN

Applications related to the Internet of Things are widely diversified and require both low latency and the access of massive sensors to wireless sensor networks. In physical wireless parameter conversion sensor networks (PhyC-SN), a fusion center (FC) can recognize information from all sensors on the frequency spectrum of the received signals via conversion from sensor information to signal frequency. The higher resolution of sensor information of PhyC-SNs requires securing more frequency bandwidth. Therefore, spectrum sharing between PhyC-SNs and other systems is essential. For this study, we assume that primary systems (PSs) refer to other wireless systems and that secondary systems (SSs) refer to the PhyC-SN. The SS detects any access from the PS and immediately stops access to an FC. This results in a loss of sensor information. Thus, the accuracy of the gathered sensor information by an FC is degraded. This paper proposes an adaptive channel assignment based on two predictions; sensor information and channel occupancy rate. In the proposed method, the predicted error caused by the cessation of channel access is calculated and the assignment is constructed by minimizing this predicted error. Since the sensor can select channels in accordance with the error of an instantaneous sensor result, the proposed channel assignment can utilize awareness of the instantaneous sensor result. The proposed method achieves high accuracy of gathered sensor information while delivering less frequent sensor information to the FC, thereby improving the utilization efficiency of frequency channels.

Power optimization algorithm for OFDM underwater acoustic communication using adaptive channel estimation

An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing (OFDM) underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information. This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result, which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application; based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio, a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed, which realizes underwater communication with the low bit error rate through higher energy efficiency. The validity of the research content is verified through simulation and field experiments.

Design of Rate-Compatible Protograph LDPC Codes for Spin-Torque Transfer Magnetic Random Access Memory (STT-MRAM)

Thanks to its superior features of non-volatility, fast read/write speed, high endurance, and low power consumption, spin-torque transfer magnetic random access memory (STT-MRAM) has become a promising candidate for the next generation non-volatile memories (NVMs) and storage class memories (SCMs). However, it has been found that the write errors and read errors caused by thermal fluctuation and process variation severely degrade the reliability of STT-MRAM. Moreover, process imperfection also causes a diversity of the raw bit error rate (BER) among different dies of STT-MRAM. In this paper, we propose the design of novel rate-compatible protograph low-density parity-check (RCP-LDPC) codes to correct memory cell errors and mitigate the raw BER diversity of STT-MRAM. In particular, to deal with the asymmetric property of the STT-MRAM channel, we first apply an independent and identically distributed (i.i.d.) channel adapter to symmetrize the STT-MRAM channel. We then present a modified protograph extrinsic information transfer (P-EXIT) algorithm for the symmetrized STT-MRAM channel. We further propose a combined guideline, including the modified P-EXIT algorithm, the asymptotic weight enumerator (AWE) analysis, as well as the actual error rate performance, for designing protograph LDPC codes with short information word lengths for STT-MRAM. By further applying a code extension approach, we design novel RCP-LDPC codes that can work with a single encoder/decoder. Simulation results show that our proposed RCP-LDPC codes outperform the well-known rate-compatible AR4JA protograph codes as well as the fixed-rate quasi-cyclic (QC) LDPC codes in terms of both the error rate performance and the convergence speed over the STT-MRAM channel.

Comparison of reconstruction and acquisition choices for quantitative T2* maps and synthetic contrasts

Aim and scopeA Gradient Echo Plural Contrast Imaging technique (GEPCI) is a post-processing method, which can be used to obtain quantitative T2* values and generate multiple synthetic contrasts from a single acquisition. However, scan duration and image reconstruction from k-space data present challenges in a clinical workflow. This study aimed at optimizing image reconstruction and acquisition duration to facilitate a post-processing method for synthetic image contrast creation in clinical settings. Materials and methodsThis study consists of tests using the American College of Radiology (ACR) image quality phantom, two healthy volunteers, four mild traumatic brain injury patients and four small vessel disease patients. The measurements were carried out on a 3.0 T scanner with multiple echo times. Reconstruction from k-space data and DICOM data with two different coil-channel combination modes were investigated. Partial Fourier techniques were tested to optimize the scanning time. ConclusionsSum of squares coil-channel combination produced artifacts in phase images, but images created with adaptive combination were artifact-free. The voxel-wise median signed difference of T2* between the vendor’s adaptive channel combination and k-space reconstruction modes was 2.9 ± 0.7 ms for white matter and 4.5 ± 0.6 ms for gray matter. Relative white matter/gray matter contrast of all synthetic images and contrast-to-noise ratio of synthetic T1-weighted images were almost equal between reconstruction modes. Our results indicate that synthetic contrasts can be generated from the vendor’s DICOM data with the adaptive combination mode without affecting the quantitative T2* values or white matter/gray matter contrast.

Chinese competition: intra-industry and intra-firm adaptation

This paper quantifies how a small and open economy adapts and thereby shapes to what extent it is exposed to competition from China on the world market. Starting from granular firm-commodity-destination-level sales data for the universe of Danish manufacturing firms we construct counterfactual competition exposure, measured as sales weighted Chinese import shares in the home and all export markets while holding the industry composition, the product as well as the destination mix constant. Without adaptation the competition shock after China’s WTO accession would have been more than one and a half times of what has been readily observable. Besides broad industry-level structural change, intra-industry reallocations and intra-firm adjustments through product and destination switching are important adaptation channels. Furthermore, we find some evidence for dynamic quality differentiation as a relevant adaptation margin.

Adaptive Digital Channelization of Sparse Wideband Analog Signals at Sub-Nyquist Rate

Considering that the conventional digital channelized receiver requires a high sampling rate for a wideband signal and produces a cross-channel problem due to unknown prior information of the received signals, a method of adaptive digital channelization based on compress sensing (CS) theory is proposed in this paper. The method consists of three parts, i.e., modulated wideband converter (MWC) sampling system, sparse recovery algorithm, synthesis filter banks. MWC sampling system is applied to receive sparse wideband analog signals at sub-Nyquist sampling rate. Sparse recovery algorithm such as sparsity adaptive matching pursuit (SAMP) is chosen to recover the channelized signals. Based on the adjacent channels containing signals, the complete wideband signal can be obtained by the synthesis filter banks adaptively, thus solving the cross-channel problem. Finally, the numerical experiments demonstrate that the proposed method can implement adaptive channelization of sparse wideband signal at sub-Nyquist sampling rate.

An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach

Due to the fast increase of sensing data and quick response requirement in the Internet of Things (IoT) delivery network, the high speed transmission has emerged as an important issue. Assigning suitable channels in the wireless IoT delivery network is a basic guarantee of high speed transmission. However, the high dynamics of traffic load (TL) make the conventional fixed channel assignment algorithm ineffective. Recently, the software defined networking-based IoT (SDN-IoT) is proposed to improve the transmission quality. Besides this, the intelligent technique of deep learning is widely researched in high computational SDN. Hence, we first propose a novel deep learning-based TL prediction algorithm to forecast future TL and congestion in network. Then, a deep learning-based partially channel assignment algorithm is proposed to intelligently allocate channels to each link in the SDN-IoT network. Finally, we consider a deep learning-based prediction and partially overlapping channel assignment to propose a novel intelligent channel assignment algorithm, which can intelligently avoid potential congestion and quickly assign suitable channels in SDN-IoT. The simulation result demonstrates that our proposal significantly outperforms conventional channel assignment algorithms.

SAMU: design and implementation of frequency selectivity-aware multi-user MIMO for WLANs

The traffic demand of wireless networks is expected to increase 1000-fold over the next decade. In anticipation of such increasing data demand for dense networks with a large number of stations, IEEE 802.11ax has introduced key technologies for capacity improvement including Orthogonal Frequency-Division Multiple Access (OFDMA), multi-user multi-input multi-output (MU-MIMO), and greater bandwidth. However, IEEE 802.11ax has yet to fully define a specific scheduling framework, on which the throughput improvement of networks significantly depends. Even within a 20 MHz of bandwidth, users experience heterogeneous channel orthogonality characteristics across sub-carriers, which prevents access points (APs) from achieving the ideal multi-user gain. Moreover, frequency selectivity increases as bandwidth scales and correspondingly severely deteriorates multi-user MIMO performance. In this work, we develop a novel channel adaptation scheme, named selectivity-aware multi-user MIMO (SAMU), to combat the issue of frequency selectivity and support coexistence among users in the network by jointly assigning subsets of sub-carriers to selected users and implementing downlink MU-MIMO. To do so, we first investigate the channel characteristics of an indoor environment. We then consider the frequency selectivity of current and emerging WiFi channel bandwidths to optimize multi-user MIMO by dividing the occupied sub-carrier resources into equally sized sub-channels according to the level of frequency selectivity. In our design, each sub-channel is allocated according to the largest bandwidth that can be considered frequency-flat, and an optimal subset of users is chosen to serve in each sub-channel according to spatial orthogonality. As a result, we support more simultaneous users than current 802.11 designs and achieve a significant performance improvement for all users in the network. Additionally, we propose a selectivity-aware high efficiency (SA-HE) mode, which is based on and fully backward compatible with the existing IEEE 802.11ax standard. Finally, over emulated and real indoor channels, we show that SAMU can achieve as much as 84.8% throughput improvement compared to existing multi-user MIMO schemes in IEEE 802.11ax.

The Measuring System for the Thomson Scattering Diagnostics of the GOL-3 and GDT Facilities

The measuring system for Thomson scattering diagnostics of the GOL-3 and GDT facilities at the Budker Institute of Nuclear Physics is described. The system has a modular architecture and is based on eight-channel measuring subsystems, including photodetectors, two-channel recorders (ADC12500), synchronization modules, and the Ethernet/UART communication channel adapter. The photodetectors are based on avalanche photodiodes and are operable in the frequency range of 0–50 MHz. The ADC12500 recorders are used to measure scattering signals with a duration of ~20–30 ns at a sampling rate of to 500 MHz and an amplitude resolution of 12 bits and accumulate data in a buffer memory with a capacity as large as 2 MB.

An adaptive channel assignment in wireless mesh network: The learning automata approach

In wireless mesh networks, random changes in the environment can increase the complexity of the multi-channel assignment. In this work, a new channel assignment scheme based on learning automata is proposed, which adaptively improves the network's overall performance by predicting network dynamics. First, we use a practical utility function that reflected the user's preference regarding the signal-to-interference-and-noise ratio is applied. In the multi-automata learning algorithm, each user evaluates a channel selection strategy by computing a utility value in a stochastic iterative procedure. The utility function that potentially reflects a measure of satisfaction is used by every node as an environmental response to the current selected strategy. In the proposed algorithm, by changing network traffic pattern, the channel allocation varies adaptively with dynamic conditions of the network. Extensive simulation-based evaluation of our algorithm demonstrates that the proposed algorithm converges to an equilibrium point, which is also optimal for channel assignment policy.

Saliency Detection by Adaptive Channel Fusion

Saliency detection has received tremendous attention from the research community and has become an increasingly important preprocessing tool in many computer vision tasks, such as object recognition. Saliency detection in the frequency domain possesses great advantages compared with that in the spatial domain due to its computational efficiency and succinct model design. Many excellent frequency domain based models, such as SR, phase spectrum of quaternion Fourier transform, hypercomplex Fourier transform (HFT), have made great progress in predicting human eye fixations. However, there still exists a vital drawback among them—ignoring the different contributions of image channels to saliency detection. In our observation, chrominance component of an image may play an unequally important role as luminance component in different situations. In this letter, we propose a frequency domain based framework, which exploits HFT -based saliency algorithm and finds a way to weigh the importance of different channels in saliency detection across space. In the first step, saliency maps corresponding to different channels are generated independently. In the second step, a combination of both saliency maps is performed using an adaptive entropy-based uncertainty weighting approach. We provide an extensive evaluation and show that our proposed method outperforms nine outstanding eye fixation prediction models on four most commonly-used datasets.

The Study of Customer Split-Flow Based on Data Mining

In this paper, we established the business preference model to identify who often applied the low-value businesses in service hall, and the channel adapter model was built, which was used by data mining, and we made this model determine the suitability customers for them to apply the low-value business by electronic channels. Three categories were divided that fit the specific recommendation, required patience to guide, and not suitable recommended. Finally, according to the actual business data, we validated the model and obtained the satisfactory results for customer split-flow.

Concatenated physical layer encryption scheme based on rateless codes

A novel concatenated physical-layer encryption (CPLE) scheme is proposed in this study, where better security can be achieved as well as reliability advantage. In CPLE scheme, the encryption is embedded in rateless encoding. The secret key, which is generated from wireless channel, controls the random degree and random linear combination. The rateless codes are also concatenated with other conventional channel codes, which can achieve better reliability. Different from security codes, both the security and reliability in the proposed scheme can be improved. Furthermore, there is no extra computation requirement on decoding process. Security analysis is presented including time complexity comparison and attacks resistance in practical application. Compared to other PLE schemes, the proposed scheme achieves lower complexity, and higher channel adaptation and performance advantage. On the basis of National Institute of Standards and Technology statistical test suite, CPLE scheme can achieve randomness as the traditional cipher system such as advanced encryption standard. Furthermore, simulation results show that the CPLE scheme outperforms the conventional concatenated codes at the same coderate.

Position-deviation-compensation demodulation method for multi-channel polarized low-coherence interferometry

A position-deviation-compensation demodulation method is proposed to improve the channel adaptability of Fabry-Perot (F-P) sensor in a multi-channel optical fiber F-P sensing system. By combining the envelope peak position (EPP) retrieval process and the position compensation process, the proposed method enables the accurate demodulation of F-P sensors in all channels. Thereinto, the EPP retrieval process uses the phase information to recover the EPP with high precision; the position compensation process compensates the position deviation by an optical-path-based model, which is established to illustrate the principle of the position deviation between different channels. We carried out the pressure experiment to verify the effectiveness of the proposed method. The experiment results showed that the demodulation errors of all channels are no more than 0.13 kPa, which demonstrated that our approach is reliable for improving the channel adaptability of F-P sensors.

Adaptive Channel Estimation Scheme Based on LTE Uplink in V2V Environments

Vehicle communication can facilitate efficient coordination among vehicles on the road and enable future vehicular applications such as vehicle safety enhancement, infotainment, or even autonomous driving. In the 3rd Generation Partnership Project (3GPP), many studies focus on long term evolution (LTE)-based vehicle communication. Because vehicle communication is closely related to safety, it requires low latency and improved reliability. However, vehicle speed is high enough to cause severe channel distortion in vehicle-to-vehicle (V2V) environments. We can utilize channel estimation methods to approach a reliable vehicle communication systems. Conventional channel estimation schemes can be categorized as least-squares (LS), decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, we propose a smart channel estimation scheme in LTE-based V2V environments. The channel estimation scheme, based on an LTE uplink system, uses a demodulation reference signal (DMRS) as the pilot symbol. Unlike conventional channel estimation schemes, we propose an adaptive smoothing channel estimation scheme (ASCE) using quadratic smoothing (QS) of the pilot symbols, which estimates a channel with greater accuracy and adaptively estimates channels in data symbols. In simulation results, the proposed ASCE scheme shows improved overall performance in terms of the normalized mean square error (NMSE) and bit error rate (BER) relative to conventional schemes.

Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems

Aiming at a massive multi-input multi-output (MIMO) system with unknown channel path number, a sparse adaptive compressed sensing channel estimation algorithm is proposed, which is the block sparsity adaptive matching pursuit (BSAMP) algorithm. Based on the joint sparsity of subchannels in massive MIMO systems, the initial set of support elements can be quickly and selectively selected by setting the threshold and finding the maximum backward difference position. At the same time, the energy dispersal caused by the non-orthogonality of the observation matrix is considered, and the estimation performance of the algorithm is improved. The regularization of the elements secondary screening is deployed, in order to improve the stability of the algorithm. Simulation results show that the proposed algorithm can quickly and accurately recover massive MIMO channel state information with unknown channel sparsity and high computational efficiency compared with other algorithms.

Low-Complexity Single-Channel Blind Separation of Co-Frequency Coded Signals

Single-channel blind source separation (SCBSS) of uncoordinated, non-spread, co-frequency interfering signals with non-zero carrier frequency offset and timing offset, and without training sequence for channel estimation, is a challenging task. Iterative SCBSS of coded signals leads to good performance but is computationally expensive as it involves the turbo processing of multi-user per-survivor processing and soft-input soft-output channel decoding. In this letter, we propose a low-complexity SCBSS (LC-SCBSS) scheme, which reduces the computational complexity of the conventional iterative SCBSS by using interference-cancellation from the second iteration onward, and adaptive channel truncation for certain users. Simulation results show that the proposed LC-SCBSS reduces the computational complexity by more than 99%, with only a marginal degradation in performance.

Channel estimation and long-range prediction of fast fading channels for adaptive OFDM system

ABSTRACTThis correspondence presents the channel estimation and long-range prediction technique for adaptive-orthogonal-frequency-division-multiplexing (AOFDM) system. The efficient channel loading is accomplished by feeding the accurately predicted channel-state-information (CSI) back to transmitter. The frequency-selective wireless fading channel is modelled as a tapped-delay-line-filter governed by a first-order autoregressive (AR1) process; and an adaptive channel estimator based on the generalised-variable-step-size least-mean-square (GVSS-LMS) algorithm tracks AR1 correlation coefficient. To compensate for the signal fading due to channel state variations, a modified-Kalman-filter (MKF)-based channel estimator is utilised. In addition, channel tracking is also performed for predicting future CSI at receiver, based on the numeric-variable-forgetting-factor recursive-least-squares (NVFF-RLS) algorithm. Subsequently, adaptive bit allocation for AOFDM system is employed by using predicted CSI at transmitter. Here, the proposed combination of GVSS-LMS and MKF algorithms for robust channel estimation and the NVFF-RLS algorithm for efficient channel prediction is incorporated. The performance validation of presented method is carried out by using different channel realisations through simulation, and also by comparing it with fixed step-size LMS, MKF and fixed forgetting-factor RLS algorithm based conventional techniques. Eventually, the reliable performance of underlying AOFDM system can be achieved in terms of the lower mean squared estimation/prediction errors and alleviated symbol error rate.

Blind adaptive channel mismatch equalisation method for GNSS antenna arrays

A blind adaptive channel mismatch equalisation method for global navigation satellite system (GNSS) antenna arrays is proposed. The anti-jamming performance of the antenna arrays is limited by the channel mismatch that is a relative characteristic of each radio frequency channel but not an absolute non-ideal characteristic. To pre-process the received data in the frequency domain, the least mean square (LMS) algorithm is used. The reference signal in LMS is the received data in the reference element. The purpose of pre-processing is to make the received data in the other elements approach the reference signal, which, in essence, is to equalise the mismatched channel. Without any auxiliary sensors and calibration source, this method can estimate the channel characteristics relative to the reference elements. Simulations and experimental results show that the proposed method is significant in improving the anti-jamming performance.

IBM z14: Advancing the I/O storage and networking channel adapter

The IBM z14 system introduces a new input/output adapter that supports two storage protocols as well as a networking protocol. This is the first time three protocols have been supported on one application-specific integrated circuit (ASIC). The three protocols supported are Fibre Connection, Fibre Channel Protocol, and Ethernet. In addition to consolidation, enhancements included substantial performance improvements, reductions in power, and updates to both the technology and multiple intellectual property interface standards. Due to the increased complexity of this ASIC, new verification methodologies were introduced that also incorporated firmware running on programmable sequence engines. This enabled testing earlier in the development cycle, referred to as a shift-left transformation, so our firmware and software development teams could start coding even before the actual hardware was built.