Interference Mitigation Scheme Research Articles

Low-complexity adaptive multipath interference and channel noise mitigation scheme based on optimized detection for bandwidth-limited IM/DD systems.

In this Letter, we propose a low-complexity adaptive multipath interference (MPI) and channel noise mitigation (AMCM) scheme in the receiver digital signal processing (DSP) for bandwidth-limited intensity modulation and direct detection (IM/DD) transmission systems. Following channel equalization, MPI and channel noise are distributed in the low- and high-frequency parts, respectively, and exhibit the characteristics of bandstop filtering. The proposed AMCM is designed based on optimized detection, which incorporates an adaptive bandpass filter (BPF) and a log-maximum a posteriori estimation with a lookup table-based fixed number of surviving states (LUT-based FS-MAP) decoder. The adaptive BPF is capable of mitigating the MPI and channel noise based on spectral distribution. Moreover, the LUT-based FS-MAP decoder can eliminate intersymbol interference (ISI) introduced by the BPF. The proposed AMCM is implemented in an O-band 56-Gbaud IM/DD optical 4-level pulse amplitude modulation (PAM-4) system with a 10.7-GHz bandwidth over a 10-km standard single-mode fiber with different linewidths. The results demonstrate that the proposed AMCM scheme can enhance signal-to-interference ratio (SIR) tolerance to 11 dB with only three real-valued multiplications per symbol, achieving a 7% hard-decision forward error correction threshold. To the best of our knowledge, for the first time, this represents the inaugural instance of optimized detection being employed for MPI mitigation.

Security, privacy and performance concerns in ultra dense networks

Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

SummaryWireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

Design and performance validation of CWT-MCA based interference mitigation for automotive radars

Interference mitigation has been a vital area of focus within the realm of automotive radar research, holding paramount importance for augmenting advanced driver-assistance capabilities, particularly in vehicle-dense scenarios. This paper presents a novel interference mitigation scheme based on continuous wavelet transform and morphological component analysis (CWT-MCA). This method, in essence, transforms the time-domain signal into the time-frequency domain, subsequently enabling the direct separation of target and interference components, thereby circumventing the necessity for interference detection and identification. Additionally, this paper introduces a series of MCA algorithms grounded in various transform domains, which are subsequently compared with the traditional linear prediction based approach. We show through comprehensive numerical simulations and real-data experiments that CWT-MCA algorithm exhibits markedly superior interference mitigation performance while offering lower computational complexity, which makes it well-suited for real-time implementation in automotive radar systems.

AI and game-based efficient resource allocation and interference mitigation scheme for D2D communication

Device-to-device (D2D) communication essentially means to communicate between two devices without the base station interfering. D2D communication is used in wireless 5G networks, the Internet of Things, Bluetooth and WiFi, and vehicular networks. D2D communication is useful as it increases spectral efficiency, network efficiency, user experience, and throughput, all of which are better than those available in cellular communication. Thus, D2D communication is a preferred mode of communication. However, D2D communication faces issues like mode selection, interference, resource allocation (RA), and security. The need for RA stands because multiple D2D users (DUs) compete for the same resources. Now, the goal should be to optimize RA to enhance the spectrum efficiency and network coverage. Still, the issues here are caused by eavesdroppers (ɛ), devices that can interfere with communication and encrypt/decrypt the messages. In our proposed scheme, we combined artificial intelligence (AI) and coalition game theory to resolve the issues of optimized RA and security. The coalition game is used for efficient RA, but it will take all the DUs that increase the overhead into the system. To mitigate this issue, we proposed an AI-based solution, which selects the best DUs based on their channel conditions. Our major evaluation parameters were accuracy, sum secrecy capacity and data rate.

Interference mitigation for FMCW radar via chirp rate estimation and signal separation

As a prevalent system in radar technology, the Frequency modulated continuous wave (FMCW) radar faces significant challenges in interference. This paper proposes a three-step interference mitigation strategy considering the time–frequency (TF) features of interference and time-domain properties of beat signals. Initially, the process involves statistical analysis of chirp rates from high-energy TF points, estimating the matched chirp rates of interference signals. Subsequently, the improved multisynchrosqueezing chirplet transform (IMSSCPT) is suggested for high-resolution TF characterization of interference signals. Utilizing the ridge extraction method, we accurately detect the TF region of interference signals. This precise localization enables the gradual separation of the reconstructed interference from the received signals. Finally, we perform parallel singular value decomposition on Hankel matrices, which are stacked from the mitigated subsequences to obtain the beat subspace and reconstruct the beat signal to reduce the negative effect of noise. A series of experimental comparisons of the proposed algorithm with existing methods have validated its exceptional performance, particularly under conditions of high-level noise.

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

An Improved Interference Mitigation Scheme for Femtocell Networks

Femtocell networks have gained significant attention in recent years due to their ability to improve indoor wireless coverage and capacity. However, they are susceptible to interference from neighboring cells, which significantly degrade their performance and quality of service to the end-user. The study introduced an adaptive power control technique that causes the power level of the femtocell base station to adjust dynamically to reduce the interference caused by adjacent femtocell networks, thereby adjusting the system bandwidth, carrier frequency, path loss and femtocell Base Station (BS) transmission power based on the request made by the users. The interference mitigation scheme was optimized by tuning the parameters of the power control algorithm, such as the transmit power levels and the interference thresholds. The algorithm was simulated with MATLAB simulation tools. Throughput, Signal to Interference Noise Ratio (SINR), coverage enhancement and user satisfaction were used as measurement metrics for the model using descriptive statistics. The results showed that the average performance obtained for the throughput increased by 25.88% from 3.40 X 1010 (bps) to 4.28 X 1010 (bps), and the SINR increased by 35.29% from 1.70 X 109 dB to 2.30 X109 dB for the benchmarked power control and improved power control techniques respectively. Coverage enhancement and user satisfaction were nearly 0, which indicates that the technique was of average performance. This study concluded that the new scheme enhances wider coverage to reach more users and mitigate interference while maintaining a higher level of femtocell user satisfaction.

Intra-Beam Interference Mitigation for the Downlink Transmission of the RIS-Assisted Hybrid Millimeter Wave System.

Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital baseband precoding techniques are limited in their ability to mitigate inter-user interference within identical beam directions, representing a fundamental constraint in mmWave downlink transmissions. This study introduces an innovative analog beamforming-based interference mitigation strategy for downlink transmissions in reconfigurable intelligent surface (RIS)-assisted hybrid analog-digital (HAD) mmWave systems. This is achieved through the joint design of analog beamformers and the corresponding coefficients at both the RIS and the BS. We first present derived closed-form approximation expressions for the achievable rate performance in the proposed scenario and establish a stringent upper bound on this performance in a large number of RIS elements regimes. The exclusive use of analog beamforming in the downlink phase allows our proposed transmission algorithm to function efficiently when equipped with low-resolution analog-to-digital/digital-to-analog converters (A/Ds) at the BS. The energy efficiency of the downlink transmission is evaluated through the deployment of six-bit A/Ds and six-bit pulse-amplitude modulation (PAM) signals across varying numbers of activated RIS elements. Numerical simulation results validate the effectiveness of our proposed algorithms in comparison to various benchmark schemes.

Reliability in Post-Disaster Networks: A Novel Interference-Mitigation Strategy

Reliability in Post-Disaster Networks: A Novel Interference-Mitigation Strategy

UE admittance and contention free resource allocation for interference mitigation in HetNet cognitive femtocells

Femtocells are designed to co-exist alongside macrocells providing higher spectrum efficiency, spatial frequency reuse, to name a few. However, when deployed in the two-tier architecture with macrocells, interference is imminent thereby it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning, adapting, and reasoning leading to informed decisions. This paper proposes a joint threshold power based admittance and contention free resource allocation scheme (known as Proposed SA) for interference mitigation in HetNet cognitive femtocells (CF). In the proposed SA, a CF sets a threshold value on the mutual interference between itself and a close-by mobile user equipment (MUE). To mitigate cross-tier interference, a CF classifies MUEs which fall above this threshold value (high interference value) as Undesired MUEs (UMUEs), whereas MUEs which fall below this threshold are classified as Desired MUEs (DMUEs). To mitigate co-tier interference, proposed SA introduces a scheduling engine which employs matching policy attributes and assigns resource blocks (RBs) of unique DMUEs to CFs to avoid any possible contention problems, thus providing improved co-tier interference. System level simulations have been performed to demonstrate working and effectiveness of proposed SA. Key performance indicators including choice of maximum transmit power for MUE, co and cross tier interference mitigation, as well impact of MUE mobility has been investigated. Performance analysis of the Proposed SA has also found that the scheme performs much better in relation to a random spectrum assignment (Random SA) scheme. The proposed SA caters the mobility of the MUEs very well allowing more and more admittance thereby providing improved interference results and making it a viable candidate for Femtocell Access points (FAPs) implementation.

AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks †

Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.

Interference Mitigation Strategy for D2D Communication in 5G Networks

Device-to-Device (D2D) communication is one of the most promising developments in 5G networks. D2D communication can reduce communication latency and increase spectrum efficiency and system capacity. However, implementing D2D communication in reuse mode with a traditional cellular network can result in severe interference that can significantly reduce network performance, as both networks share the same resources. It is essential to mitigate interference to improve network capacity when D2D communication coexists with traditional cellular networks. An effective power control strategy can help mitigate the potential detrimental impact of interference and maximize the potential benefits of D2D communication. In this study, a dynamic transmission power control strategy was proposed for D2D transmitters to allow them to dynamically adjust transmission power, aiming to minimize interference and maximize network capacity. The distance between and the number of D2D pairs that experience interference were the primary parameters considered for the proposed strategy, while its complexity was in polynomial time. A simulation was carried out to evaluate the proposed strategy and compare it with fixed and ranged-based approaches, and the results validated its effectiveness.

Practical Performance Analysis of Interference in DSS System

The 5G network is developing rapidly. However, due to spectrum resource limitation, it is expected to use the 5G network to ensure high resource utilization and network efficiency, while keeping part of 4G in the same band for existing 4G users. Dynamic spectrum-sharing (DSS) technology enables 4G/5G wireless networks to coexist in scarce spectrum resources and dynamically allocates spectrum resources in the same band. 4G/5G DSS has been successfully commercialized in some countries such as Germany and Brazil. However, complex 4G/5G DSS networks will introduce intra-frequency interference in the inter-system, which will affect network performance. Therefore, we innovatively proposed two interference mitigation schemes: buffer setting and rate matching. Furthermore, we have verified the practical performance of both schemes in a commercial network for the first time to determine the feasibility of the schemes. From theory, simulation, and practical analysis, both schemes can effectively mitigate the interference of the inter-system introduced by DSS: increasing the network rate by 60% in the interference environment and improving the user experience in the DSS architecture.

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the ϵ-greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

Energy efficient two-stage capacity allocation scheme for WBAN healthcare applications

Co-channel interference occurs in coexisting Wireless Body Area Networks (WBANs) and has an impact on the capacity allocation of the networks. The proposed Two Stage Capacity Allocation (TSCA) mitigates interference and maximizes the capacity of poor channel quality WBANs with high priority data. It uses traditional interference mitigation schemes such as transmission power level control, channel switching and relay-aided transmission in each instant of pause time. The proposed work introduces a metric called Emergency Residual Index (ER-Index) and chooses any one of the mitigation schemes. The ER Index value provides a trade-off between residual energy, emergency factor and channel metrics. Further, TSCA executes a two-stage capacity allocation method. The TSCA scheme is a combination of the Nash bargaining solution and the Min-Max algorithm. The mitigated interference is 45% compared to Dynamic link quality-based Sub Channel Allocation (DSCA) and 60% compared to Traffic Aware Medium Access Control (TA-MAC). The average energy consumption is decreased up to 37.82% compared to TA-MAC and 25.59% compared to the DSCA algorithm.

Improvements for Uplink Long Term Evolution (UL-LTE) in Heterogeneous Network using Dynamic Spectrum Allocation Technique

Several interference mitigation strategies can be used in LTE networks to meet the consumers' increasing need for faster data speeds. Dynamic Spectrum Allocation (DSA), which maintains spectrum in a converged radio system and distributes it across all participating radio terminals, is one of the most promising solutions. However, the fundamental obstacle to achieving increased network capacities is increased inter-cell interference. The aim of this paper is to analyse the effectiveness of a newly proposed DSA technique by comparing its bit error rate (BER) and throughput (TP) with a typical uplink LTE configuration. To maximise throughput in a heterogeneous network, the suggested DSA technique utilises the Nash Bargaining Solution (NBS) scheme, a cooperative game theory that could correlate with the bit error rate. The software tool GNU Radio, which offers signal-processing blocks to develop softwaredefined radios and signal-processing systems, was used to create the setups. After the simulations were performed, the BER values were recorded and compared, while the TP values were established and computed appropriately. According to the results, the uplink (UL-LTE) architecture with DSA has an improved TP and a lower BER value. As a result, spectral efficiency can also be improved.

D-SCAN: Toward collaborative multi-radio coexistence in mobile devices via deep learning

As the demand for efficient and reliable wireless connectivity continues to increase, mobile Internet-of-Things devices equipped with multiple heterogeneous radios including Wi-Fi and Bluetooth have become prevalent. However, collocated Wi-Fi and Bluetooth operate in the same 2.4 GHz industrial, scientific, and medical band and interfere with each other internally and externally. Although current devices avoid internal cross-technology interference by enabling either Wi-Fi or Bluetooth to transmit packets at any specific time slot in a time-division multiplexing manner, the dissonance with external interference mitigation schemes can result in severe performance degradation. In this paper, we present D-SCAN, a novel collaborative coexistence mechanism. D-SCAN infers nearby Wi-Fi information efficiently using a collocated Bluetooth radio, thereby offsetting the overhead of key Wi-Fi functions and preventing collisions between Wi-Fi and Bluetooth. To this end, D-SCAN adopts a data-driven approach that captures the unique temporal and spectral features of Wi-Fi signals from Bluetooth spectrum measurements by leveraging deep neural networks. A D-SCAN prototype in real-world experiments reduces the latency and energy consumption of legacy Wi-Fi scanning by 23% and 45%, respectively. It also promotes the agile interference avoidance of Bluetooth that coexists with Wi-Fi on a single device. Thus, D-SCAN demonstrates efficiency in resource management and effectiveness in mitigating cross-technology interferences.

Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion

In this letter, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration.

QoE-Driven Distributed Resource Optimization for Mixed Reality in Dynamic TDD Systems

With the full development of intelligent mobile communications, wireless mixed reality (MR) provides a more visually immersive experience and stronger interaction with environments than virtual reality (VR) and augmented reality (AR). However, the asymmetric characteristic of wireless MR traffic creates a huge challenge to current mobile networks. Dynamic time division duplex (D-TDD) is considered as a promising technology to improve wireless MR users’ quality of experience (QoE) due to its potentials and advantages in delivering asymmetric traffic. Therefore, in this paper, we propose a QoE-driven distributed multidimensional resource allocation (MRA) supplemented by inter-cell interference (ICI) mitigation scheme for wireless MR in multi-cell D-TDD systems. First, to improve QoE of MR users, we formulate the joint optimization of subframe configuration, channel assignment and computation offloading as a mixed-integer nonlinear programming problem. A novel fully-decentralized multi-agent deep Q-network (DQN) algorithm is developed to solve the problem. Then, to mitigate ICI, a water filling based power control algorithm is investigated to minimize the total power of each small base station and its associated MR users. Simulation results demonstrate that our proposed scheme improves QoE of MR users in a realizable way as compared to existing schemes.