Interference Channel Research Articles

Chaos Synchronization in a Dual-Laser Chaotic Multiplexing System Based on Nanolasers

Nanolaser (NL), as an important optical source device, has a significant impact on photonic integrated circuits and has become a research hotspot in recent years. This study investigates the synchronization performance of a dual-channel laser chaotic multiplexing system based on NLs and employs an active-passive decomposition to enhance signal processing and multiplexing efficiency. By establishing a rate equation model, the synchronization characteristics of the system were analyzed, focusing on the effects of two key parameters—the Purcell factor (F) and the spontaneous emission coupling factor (β)—as well as system parameters, single-parameter mismatches, and multi-parameter mismatches. Numerical simulations show that, with proper parameter configurations, the two master NLs can maintain low correlation, ensuring the "pseudo-orthogonal" of chaotic signals while achieving high-quality chaotic synchronization with their paired slave NLs. The study found that both the Purcell factor (F) and the spontaneous emission coupling factor (β) significantly influence the synchronization performance of the system, and the optimal parameter ranges for achieving high-quality synchronization were identified. Additionally, the effects of feedback strength and frequency detuning were explored, revealing that frequency detuning plays a more critical role in the synchronization between the master NLs. The impact of parameter mismatches on system synchronization performance was also emphasized. The system exhibits robustness against single-parameter mismatches, with minimal impact on master-slave synchronization quality. However, multi-parameter mismatches introduce more complex effects. Compared to traditional semiconductor laser systems, this system can maintain "pseudo-orthogonal" over a wider parameter range, achieving higher security and lower channel interference. This research lays a theoretical foundation for chaos synchronization based on NLs and provides new insights for designing secure, stable, and efficient optical communication systems.

Unique MIMO System Using Gaussian Signals and the Advantage of These Signals in Sensing CSI and Multipath Fading

ABSTRACTTo improve communication network efficiency, researchers must look at all aspects of transmission and the mechanisms that regulate their evolution as a whole. These features include solutions for dealing with the channel's noise and interference. To decrease interference and increase spectrum efficiency, orthogonal frequency division multiple access (OFDMA) systems employ orthogonal signals. While transmitting and receiving signals, noise and numerous feeds can be done in diverse ways. It has become increasingly common to use 256 quadratic modulation (QAM), which is more vulnerable to noise and has a higher bit error rate (BER). BERs in OFDM systems were high when multiple feeds and noise were present, as demonstrated in this article. Starting with the transmission and reception of Gaussian subband signals, an improved system has been designed that includes numerous stages of development. Thus, the need for “orthogonally” of transmitted signals to increase spectrum efficiency has been eliminated, as has the effect of surrounding channels. We have created a header for every frame that has been transmitted. Several transmitters and numerous receivers send these frames in parallel so that the channel state information (CSI) attributes may be evaluated using parallel processing. Using the identical transmission conditions for both OFDM systems and the proposed system, the simulation results reveal a significant reduction in BER values. This results in BER values of fewer than 10−1 when there are two tabs and 10−1 when there are three tabs for multiple feeding in the OFDM system. This corresponds to BER values of 10−11 in a suggested system when there are three tabs. Some improvements have been made to the proposed design to make it distinctive and qualified to be regarded as a multiaccess system in today's contemporary communication infrastructures.

On the optimal integration of legacy and SDWN communication nodes in emergency scenarios

AbstractSpontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.

Study on Selection Diversity for MIMO 3-User Interference Channel with Interference Alignment

This paper explores selection diversity in the context of MIMO 3-user interference channels with interference alignment (IA), focusing on enhancing reliability through diversity order (DO) analysis. While degrees of freedom (DoF) have traditionally been emphasized for throughput optimization in IA, limited research has addressed diversity order to improve error performance. In this work, we define a conditional DO and propose a beamforming vector selection method that achieves a conditional DO of M2/2, where M is the number of antennas per transceiver. The proposed scheme employs a two-stage decoding approach, combining zero-forcing for interference cancellation with maximum likelihood (ML) decoding for desired signal recovery, which is augmented by orthogonalization techniques. Simulations demonstrate the superiority of the proposed scheme in both error probability and conditional DO values compared to conventional IA methods, particularly in scenarios with higher antenna counts. These results provide insights into optimizing IA for enhanced reliability and form the foundation for future exploration of advanced decoding and beamforming strategies.

Achievable Rate Region for Active RIS-Aided MISO Interference Channels

Achievable Rate Region for Active RIS-Aided MISO Interference Channels

Optimized TDMA framework for channel interference mitigation in IWSN based on self-adaptive affinity propagation clustering

Optimized TDMA framework for channel interference mitigation in IWSN based on self-adaptive affinity propagation clustering

IRS-Aided Multi-Antenna Wireless Powered Communications in Interference Channels

IRS-Aided Multi-Antenna Wireless Powered Communications in Interference Channels

Optimal design of communication scheme for auxiliary control terminal of test instrument without network coverage

Abstract To meet the diverse application needs of users, an optimization method for the communication scheme of auxiliary control terminals for testing instruments without network coverage has been designed. Signal transmission characteristics are extracted and the correlation matching degree of monitoring data is analyzed; a reliability distribution objective function is constructed and feature constraint points of monitoring data are extracted from the auxiliary control terminal of the testing instrument; dimensionality is reduced and a localization optimization fitness function under the condition of no network coverage is constructed; an error model based on delay accuracy is established and combined with positioning optimization results to obtain accurate interference position information, and the optimization effect of the communication scheme for the auxiliary control terminal of the testing instrument is improved. The experimental results show that this method has a small optimization error for channel interference location, high compatibility for communication scheme optimization data compression, and a good optimization effect.

SINGLE-CYCLE PULSE WAVEFORM RECOGNITION METHOD BASED ON SVM

The single-cycle pulse waves’ morphological characteristics can reflect rich physiological and pathological information about the human cardiovascular system, playing an important role in continuous blood pressure monitoring and cardiovascular disease risk warning. Pulse signals belong to weak physiological signals, and the acquisition process is easily affected by factors such as adjacent channel interference and motion artifacts, leading to abnormal cycles and affecting subsequent feature extraction. Therefore, based on support vector machines (SVM), a single-cycle pulse waveform recognition algorithm is proposed. Using a multi-channel pulse wave acquisition device to collect radial artery pulse signals from 150 subjects. Preprocess the signal to obtain a single-cycle pulse waveform. A total of 54 frequency domain and time-frequency domain feature parameters were extracted by using Fourier transform and wavelet transform to extract the frequency domain and time-frequency domain features. Use the principal components analysis (PCA) method to reduce parameter dimensions. Using the SVM for model training to achieve the classification of single-cycle effective pulse signals, pseudo noise and interference signals. The results show that the algorithm’s classification accuracy is 97.42%, indicating that the algorithm can effectively identify pulse signals, pseudo noise and interference signals, laying the foundation for modeling and suppressing interference signals in the next step.

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.

Concurrent Direct Inter-ONU and Upstream Communications in IMDD PONs Incorporating P2MP Flexible Optical Transceivers and Advanced Passive Remote Nodes

Driven by a large number of emerging diversified services, in the 5G and beyond era, concurrent direct inter-ONU and upstream communications inside a PON-based mobile access network are highly desirable to provide dynamic, ultra-dense, and fast ONU-to-ONU (without involving an OLT) and ONU-to-OLT connections. To cost-effectively deliver highly dynamic and low latency direct inter-ONU communications, this paper proposes and experimentally demonstrates novel concurrent direct inter-ONU and upstream communications in an upstream 27 km, >62.47 Gbit/s IMDD PON. For supporting inter-ONU communications between a large number of ONUs, an advanced passive remote node is also proposed. Based on different passive optical components, this remote node can be implemented using two approaches, which can, respectively, reduce the inter-ONU signal power losses by >12.2 dB and >16.6 dB (for 128 ONUs) in comparison with existing inter-ONU communication techniques’ remote nodes. In each ONU and OLT, a single pair of cascaded IFFT/FFT-based point-to-multipoint (P2MP) flexible optical transceivers are employed to simultaneously and dynamically establish multiple ONU-to-ONU and ONU-to-OLT communications according to actual users’ requirements. Experimental results show that the proposed network has excellent robustness against various transmission system impairments, including chromatic dispersion, the Rayleigh and Brillouin backscattering effects, and the channel interference effects. For each ONU, dynamic channel allocation can be made without compromising its overall performance.

Double Deep Q- energy aware Service allocation based on Dynamic fractional frequency reusable technique for lifetime maximization in HetNet-LTE network

The development of mobile communication in heterogeneous networks is incredible in providing various services through wireless cellular communication through advanced long-term evaluation networks. Increasing multi-concern services and frequencies in spectrum channels are highly layered to select the bandwidth to provide the fastest network without interference. Selecting the channel through macro cell selection is essential to improve network communication and provide the quickest service. Most frequency reuse techniques use service optimality and route selection-based protocols to enrich the packet flow. Still, the improper spectrum delights create more delay tolerance due to short-range service optimality due to energy loss by selecting the short spectrum signal to reuse, which doesn't support the lifetime improvement of the LTE network. To resolve these problems, we propose a Double Deep Q- energy-aware Service allocation based on a Dynamic fractional frequency reusable technique for lifetime maximization in the HetNet-LTE network. Initially, the heterogenous communication environment and node deplanement were carried out to construct the LTE network under the WCC. The communication logs are Route Table (RT), and its services are taken by all node LTE Communication Impact Rate (LTE-CIR). Then, the Backhaul Traffic Algorithm (BTA) is applied to predict the interference on traffic rate from the channel frequency margin. Select the balanced node using the Channel Interference Macro Cell Selection (CIMCS) technique. Considering frequency limits with the Double Deep Q- Network (DDQN) approach, energy-aware selects the optimal route to reuse the frequency level using Frequency Domain Packet Scheduling (FDPS) to improve communication. The proposed system improves the overall throughput by up to 97.8 % with adopted channel selection from the macro unit to improve the latency performance. Also, the interference frequency limits are dynamically reused at an energy optimal level with low-level delay tolerance to improve the link stability by up to 98.4 % with higher lifetime maximation in the LTE network.

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.

To Reduce Channel Interference in Mobile Ad-Hoc Networks through LSTM and GRU Prediction Algorithms

The demand for the applications that mobile ad-hoc networks (MANETs) can provide is rising along with their popularity. Two important features of MANETs are communication interference and connection. In MANET environments, the movement of mobile nodes has been rising. Co-channel or adjacent interference congestion is especially common due to the increasing mobility of mobile nodes. Due to the severe degradation in MANET performance caused by these interference events, they are becoming critical difficulties. There is a lack of accuracy in MANET's capacity to predict the resources that are required and accessible to prevent disrupting traffic patterns. The flow of traffic is one of its main drawbacks. One kind of deep learning is called the "deep channel," which learns sequentially using encoders and decoders. This model predicts variability in wireless signal quality based on past signal intensity. Two Deep channel versions share a core cell structure: long short-term memory (LSTM) and gated recurrent unit (GRU). A brief description of MANETs and their applications is given in this work. This study also presents the application of machine learning and deep learning in MANETs. The study demonstrated how signal interference issues may be resolved by using neural networks.

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.