Interference Channel Research Articles

Resource allocation optimization of device-to-device communication using machine learning algorithms

Device-to-device communication is envisioning next-generation wireless communication. The utility of the device-to-device communication model encourages emerging communication systems such as 5G and the Internet of Things. The allocation of resources and channel interference are major challenges in device-to-device communication. This paper proposes machine-learning-based algorithms for resource allocation and optimization of device-to-device communication. The proposed machine learning algorithm is a cascaded support vector machine. The cascaded support vector machine mapped the parameters of CUEs and DUEs. We create an iterative algorithm to achieve low-power, energy-efficient resource allocation with mode selection by formulating a novel optimization problem to maximize energy efficiency using the subtractive form method to solve a fractional objective function. We obtain data samples from a suboptimal algorithm to train the cascaded algorithm and verify the trained algorithm. Our numerical results show that the proposed cascaded machine learning-based transmission algorithm's accuracy reaches about 88%–95% despite its simple structure due to the limitation in computing power. The analysis of results suggests that the proposed algorithm is more efficient than SVM, DSVM, and the reinforcement learning (RL) algorithm.

Multi-scale context-aware and boundary-guided image manipulation detection method

Aiming at the problems of traditional image manipulation detection methods, such as fuzzy boundaries, single scale of extracted features, and ignoring background information, this paper proposes an image manipulation detection method based on multi-scale context-aware and boundary-guided. First, spatial details and base features of manipulated images are extracted using an improved pyramid vision transformer. Second, information related to the edge of the falsified region is explored by an edge contextaware module to generate an edge prediction map. Again, the edge guidance module is utilized to highlight the key channels in the extracted features and reduce the interference of redundant channels. Then, the rich contextual information of the manipulated region is learned from multiple sensory fields through the multi-scale context-aware module. Finally, the feature fusion module is utilized to accurately segment the manipulated region by focusing alternately on the foreground and background of the manipulated images. Comparing this paper's method quantitatively and qualitatively on five commonly used public image manipulation detection datasets, the experimental results show that this paper's method can effectively detect manipulated regions and outperforms other methods.

A channel resilient RFF extraction scheme for cyclic prefix contained systems

Radio frequency fingerprint (RFF) based identification technique has been proved efficient for ensuring the validity of devices connected to network. However, it is still a tough task to extract robust RFF in the scenarios with multi-path channel and moving terminals. To solve this problem, this paper proposes a channel-resilient RFF extraction scheme which can effectively reduce the influences from complex channel condition and retain robust device fingerprint. In the proposed system, blind synchronization and symbol-scale carrier frequency offset (CFO) estimation are designed for signal preprocessing for preparations of the following RFF extraction. A cyclic-prefix based de-channel algorithm (CPDCA) which can effectively weaken channel interference is proposed to meet the channel robustness of our system. Additionally, symbol-scale feature stacking algorithm (SFSA) is applied for RFF denoising, which can further enhance the performance of proposed system. Experiments using practical dataset collected from Long Term Evolution (LTE)-V2X communication system has been carried out under different signal-to-noise ratio (SNR). The results demonstrate that the proposed scheme has the ability to extract channel-robust RFF and to achieve reliable classification performance under complex channel conditions.

Using ranging for collision-immune IEEE 802.11 rate selection with statistical learning

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing approach and empirically assess the transmission success probability for each available rate. However, a transmission failure can also be caused by frame collisions. Thus, each collision leads to an unnecessary decrease in the data rate. We avoid this issue by resorting to the fine timing measurement (FTM) procedure, part of IEEE 802.11, which allows stations to perform ranging, i.e., measure their spatial distance to the AP. Since distance is not affected by sporadic distortions such as internal and external channel interference, we use this knowledge for data rate selection. Specifically, we propose FTMRate, which applies statistical learning (a form of machine learning) to estimate the distance based on measurements, predicts channel quality from the distance, and selects data rates based on channel quality. We define three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. Then, with a thorough performance evaluation using simulations and an experimental validation with real-world devices, we show that our approach has several positive features: it is resilient to collisions, provides near-instantaneous convergence, is compatible with commercial-off-the-shelf devices, and supports pedestrian mobility. Thanks to these features, FTMRate outperforms existing solutions in a variety of line-of-sight scenarios, providing close to optimal results. Additionally, we introduce Hybrid FTMRate, which can intelligently fall back to a probing-based approach to cover non-line-of-sight cases. Finally, we discuss the applicability of the method and its usefulness in various scenarios.

On Superposition Lattice Codes for the K-User Gaussian Interference Channel.

In this study, we work with lattice Gaussian coding for a K-user Gaussian interference channel. Following the procedure of Etkin et al., in which the capacity is found to be within 1 bit/s/Hz of the capacity of a two-user Gaussian interference channel for each type of interference using random codes, we work with lattices to take advantage of their structure and potential for interference alignment. We mimic random codes using a Gaussian distribution over the lattice. Imposing constraints on the flatness factor of the lattices, the common and private message powers, and the channel coefficients, we find the conditions to obtain the same constant gap to the optimal rate for the two-user weak Gaussian interference channel and the generalized degrees of freedom as those obtained with random codes, as found by Etkin et al. Finally, we show how it is possible to extend these results to a K-user weak Gaussian interference channel using lattice alignment.

Evaluación de Interferencias en Escenarios Urbanos Interiores en Bandas Inferiores a 6 GHz

In an increasingly interconnected world, the unlicensed 2.4 and 5 GHz frequency bands have been essential for developing wireless applications such as Wi-Fi (IoT devices), home automation, and media streaming, among others. However, in highly populated urban environments, such as Guayaquil,Ecuador, the efficient administration of these spectrum swaths is a critical challenge that allows the development of smart and sustainable cities. The lack of comprehensive studies on interference in these bands in dense indoor environments causes a deterioration in network reliability, limiting the ability to use higher modulation schemes (MCS) which are essential for applications that require higher connection speeds. This study focuses on the precise collection of radio spectrum measurements in Guayaquil, specifically in the 2.4 and 5 GHz bands, to evaluate interference levels and channel availability in these densely urban environments. The results reveal more pronounced saturation in the 2.4 GHz band, underscoring the urgent need for more effective spectrum management in densely populated urban areas to ensure robust connectivity.

Investigation of vertex a1>>VP for hadronic tau decays

In the framework of U(3) symmetric quark model, triangular diagrams are calculated that describe the strongly interacting vertices a1>>rhopi and a1>>K*K. The divergences arising when considering quark loops are regularized by covariant cutoff parameter. Based on four-particle hadronic τ decays, the quark structure of the resonance with quantum numbers JPC=1++ was studied. Theoretical estimates of intermediate channels with the meson for the partial decay widths of tau>>pipipinu, tau>>KKpinu and tau>>KKetanu are obtained. Contributions from contact channels describing the direct production of 3pi, KKpi and KKeta mesons by τ lepton current are taken into account. The interference of contact and intermediate axial-vector channels in determining the integral decay widths is studied. The matrix elements of the processes are obtained in the leading approximation of 1/Nc expansion. It is shown that calculations on τ lepton mesonic decays confirm the quark-antiquark structure of the a1 meson and clarify the role of the intermediate non-strange axial-vector channel in determining the integral partial decay widths. The obtained results are compared with experimental data by BaBar and Belle collaborations at the BEPC II and KEK lepton colliders.

Sampling for Remote Estimation of the Wiener Process over an Unreliable Channel

In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions are unreliable and could take random time durations. Our objective is to devise an optimal causal sampling policy that minimizes the long-term average mean square estimation error. This optimal sampling problem is a recursive optimal stopping problem, which is generally quite difficult to solve. However, we prove that the optimal sampling strategy is, in fact, a simple threshold policy where a new sample is taken whenever the instantaneous estimation error exceeds a threshold. This threshold remains a constant value that does not vary over time. By exploring the structure properties of the recursive optimal stopping problem, a low-complexity iterative algorithm is developed to compute the optimal threshold. This work generalizes previous research by incorporating both transmission errors and random transmission times into remote estimation. Numerical simulations are provided to compare our optimal policy with the zero-wait and age-optimal policies.

Widely linear RLS equalizer with variable forgetting factor and UD factorization for mud pulse telemetry

To address the problems of time variations and high interferences in continuous-wave mud pulse telemetry channels, a widely linear recursive least squares decision feedback equalizer with a variable forgetting factor was proposed, a smaller steady-state error was achieved, and the tracking performance of the equalizer in time-varying mud channels was enhanced. Furthermore, employing UD factorization in the recursive algorithm improves the computational stability and ensures robust performance. The simulation results indicate that the proposed equalizer enhances the convergence speed by 46 %. The experimental findings demonstrate that within a 3000 m mud pulse telemetry channel, the proposed equalizer reduces the steady-state mean squared error by 3 to 4 dB and increases the error-free transmission rate from 3 bps to 6 bps. These advancements significantly enhance the performance of continuous wave mud pulse telemetry systems.

SpillR: spillover compensation in mass cytometry data.

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. Chevrier et al. introduce an experimental and computational procedure to estimate and compensate for spillover implemented in their R package CATALYST. They assume spillover can be described by a spillover matrix that encodes the ratio between the signal in the unstained spillover receiving and stained spillover emitting channel. They estimate the spillover matrix from experiments with beads. We propose to skip the matrix estimation step and work directly with the full bead distributions. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. Spillover correction is often a pre-processing step followed by downstream analyses, and choosing a flexible model reduces the chance of introducing biases that can propagate downstream. We implement our method in an R package spillR using expectation-maximization to fit the mixture model. We test our method on simulated, semi-simulated, and real data from CATALYST. We find that our method compensates low counts accurately, does not introduce negative counts, avoids overcompensating high counts, and preserves correlations between markers that may be biologically meaningful. Our new R package spillR is on bioconductor at bioconductor.org/packages/spillR. All experiments and plots can be reproduced by compiling the R markdown file spillR_paper.Rmd at github.com/ChristofSeiler/spillR_paper.

A deep model‐based channel interference mitigation for OTFS signals in ISAC systems

AbstractIn recent years, Orthogonal Time Frequency Space Modulation (OTFS) has gained popularity in integrated sensing and communications (ISAC) system due to its robustness against Doppler offset and delay changes. Traditional pilot‐based methods for accurate channel parameter estimation are complex and struggle with rapidly changing channel conditions. In this letter, a deep encode‐decode network (DED‐Net) is proposed. It uses DL to automatically learn and eliminate channel interference from OTFS signals. The framework employs a deep encoding and decoding network, similar to a filter, learning complex signal features to effectively remove interference. Our experiments demonstrate DED‐Net's ability to eliminate interference in OTFS modulation signals, offering an alternative to pilot‐based methods and showcasing DL's potential for ISAC systems.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

RNA interference of Sitobion avenae voltage-gated sodium channels for improved grain aphid resistance

RNA interference of Sitobion avenae voltage-gated sodium channels for improved grain aphid resistance

Performance analysis of an underwater wireless optical communication link with Lommel beam

In order to mitigate the stochastic interference of underwater channels and improve the quality of underwater communication systems, it is essential to study the performance of the underwater wireless optical communication (UWOC) links utilizing vortex beams with unique attributes. In this paper, the analytical formulae for the bit error rate (BER) and the average capacity of the UWOC link with diffraction-free Lommel beam are derived under the Rytov theory. Simulation results demonstrate that the system with a long wavelength, a high system signal-to-noise ratio(SNR), a small asymmetric parameter and receiving aperture diameter achieves a high average capacity and a low BER. Furthermore, in the underwater channel with a larger kinetic energy dissipation rate per unit mass of fluid and inner scale, a smaller mean-squared temperature dissipation rate, temperature salinity contribution ratio and transmission distance, the performance of the communication link can be improved. Meanwhile, it is found that the performance of the link with carrier Lommel beam are less sensitive to the topological charge, the scaling factor of the beam and the turbulent outer scale. These findings provide theoretical support for the design and implementation of an UWOC link utilizing the Lommel beam.

BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with goal-oriented communication

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE5.1’s constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW-Lite and BmmW-Lite+, both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW-Lite uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW-Lite+ extends BmmW-Lite by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW-Lite+ employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact semantic format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW-Lite+ can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy.

Minimization of dispersion and non-linear effects in WDM based long-haul high capacity optical communication systems

Abstract Chromatic dispersion and nonlinear impairments pose challenges in high-capacity optical transmission systems, leading to signal distortions, channel interference and diminished the transmission performances. This paper explores the utilization of dispersion compensating fibers (DCF) using different schemes to mitigate the dispersion that accumulates along the length of a fiber in a 16 × 10 Gbps high-capacity long haul WDM system over a range of 500 km. When different wavelengths of light pulses are transmitted through an optical fiber, they experience varying speeds due to the refractive index’s wavelength dependency. As a result, the light pulses become temporally spread out as they travel through the fiber, and this dispersion persists throughout the fiber’s length. Considering non-linear effects such as self-phase modulation (SPM) and cross-phase modulation (XPM), this paper analyze and compare the three compensation schemes of DCF in a 16 channel WDM system. Moreover the impact of non-linearities based on fiber length are also analyzed and compared. The results are evaluated by comparing metrics such as Q-factor, bit error rate (BER) and eye height. The analysis concludes that the symmetrical compensation scheme of DCF outperforms the pre- and post-compensation scheme. This finding suggests that the symmetrical compensation method offers a promising solution for high-capacity access networks, providing high spectral efficiency, cost-effectiveness, and improved flexibility.

Energy-efficient resource allocation for bidirectional wireless power and information transfer over interference channels

Energy efficiency is an important performance metric for the sustainable operation of energy-limited communication networks. This study investigates an energy-efficient resource allocation strategy to maximize the energy efficiency of wireless-powered bidirectional communication over interference channels. The receivers in the system harvest energy and decode information simultaneously using a time switching or power splitting policy from data signals sent by transmitters in the forward link. The harvested energy is subsequently used to send response signals back to the transmitters in the backward link. Our objective is to jointly optimize the transmit power of the transmitters and the energy harvesting ratio of the receivers to maximize the energy efficiency of the entire system while maintaining the minimum required data rates in both the forward and backward links. Considering the non-convexity and NP-hardness of this optimization problem, we first apply nonlinear fractional programming and a dual method to derive an analytical expression for each control parameter and then propose an iterative algorithm to find suboptimal solutions with low computational complexity. The simulation results show that the proposed algorithm adjusts both the transmit power and energy harvesting ratio adaptively considering the co-channel interference and thus achieves near-optimal energy efficiency performance within a short computation time.

SPIS: Signal Processing for Integrated Sensing Technologies Using 6G Networks with Machine Learning Algorithms

The proliferation of integrated sensing techniques in Sixth Generation (6G) networks is an increasingly significant aspect in facilitating efficient end-to-end communication for all users. The suggested methodology employs a digital signal processed with terahertz bandwidth to assess the impact of 6G networks. The primary focus lies in the design of 6G networks, emphasizing key parameters such interference, loss, signal strength, signal-to-noise ratio, and dual band channels. The aforementioned factors are combined with two machine learning algorithms in order to determine the extent of spectrum sharing among all available resources. Thus suggested approach for detecting signals in the terahertz communication spectrum is evaluated using 10 devices across four situations, which involve interference, signal loss, strength, and time margins for integrated sensing. Also the assumptions are based on signal processing devices operating within millimeter waves ranging from 5 to 10 terahertz. Interference and losses in the specified spectrum are seen to be less than 1%, but the time margin for integrated sensing with 99% maximized signal intensity remains at 85%.

Image Super Resolution-Based Channel Estimation for Orthogonal Chirp Division Multiplexing on Shallow Water Underwater Acoustic Communications.

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.

Quantum reaction-limited reaction–diffusion dynamics of noninteracting Bose gases

We investigate quantum reaction–diffusion (RD) systems in one-dimension with bosonic particles that coherently hop in a lattice, and when brought in range react dissipatively. Such reactions involve binary annihilation ( A+A→∅ ) and coagulation ( A+A→A ) of particles at distance d. We consider the reaction-limited regime, where dissipative reactions take place at a rate that is small compared to that of coherent hopping. In classical RD systems, this regime is correctly captured by the mean-field approximation. In quantum RD systems, for noninteracting fermionic systems, the reaction-limited regime recently attracted considerable attention because it has been shown to give universal power law decay beyond mean field for the density of particles as a function of time. Here, we address the question whether such universal behavior is present also in the case of the noninteracting Bose gas. We show that beyond mean-field density decay for bosons is possible only for reactions that allow for destructive interference of different decay channels. Furthermore, we study an absorbing-state phase transition induced by the competition between branching A→A+A , decay A→∅ and coagulation A+A→A . We find a stationary phase-diagram, where a first and a second-order transition line meet at a bicritical point which is described by tricritical directed percolation. These results show that quantum statistics significantly impact on both the stationary and the dynamical universal behavior of quantum RD systems.