Signal-to-interference Ratio 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.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Early versus late response to PD-1-based immunotherapy in metastatic melanoma

BackgroundImmune checkpoint inhibition (ICI) currently is the most effective treatment to induce durable responses in metastatic melanoma. The aims of this study are the characterization of patients with early, late and non-response to ICI and analysis of survival outcomes in a real-world patient cohort. MethodsPatients who received PD-1-based immunotherapy for non-resectable stage-IV melanoma in any therapy line were selected from the prospective multicenter real-world DeCOG study ADOREG-TRIM (NCT05750511). Patients showing complete (CR) or partial (PR) response already during the first 3 months of treatment (Early Responders, EarlyR) were compared to patients showing CR/PR at a later time (Late Responders, LateR), a stable disease (SD) and to patients showing progressive disease (Non-Responders, NonR). ResultsOf 522 patients, 8.2 % were EarlyR (n = 43), 19.0 % were LateR (n = 99), 37.0 % had a SD (n = 193) and 35.8 % were NonR (n = 187). EarlyR, LateR and SD patients had comparable baseline characteristics. Multivariate logbinomial regression analyses adjusted for age and sex revealed positive tumor PD-L1 (RR=1.99, 95 %-CI=1.14–3.46, p = 0.015), and normal serum CRP (RR=1.59, 95 %-CI=0.93–2.70, p = 0.036) as independently associated with the achievement of an early response compared to NonR. The median progression-free and overall survival was 46.0 months (95 % CI 19.1; NR) and 47.8 months (95 %-CI 36.9; NR) for EarlyR, NR (95 %-CI NR; NR) for LateR, 8.1 months (7.0; 10.4) and 35.4 months (29.2; NR) for SD, and 2.0 months (95 %-CI 1.9; 2.1) and 6.1 months (95 %-CI 4.6; 8.8) for NonR patients. ConclusionLess than 10 % of metastatic melanoma patients achieved an early response during the first 3 months of PD-1-based immunotherapy. Early responders were not superior to late responders in terms of response durability and survival.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.

Resource allocation-aware efficient interference management technique for ultra-dense Femto environment

Addressing the challenge of severe inter-layer and intra-layer interference in densely deployed Femtocells, we introduce an efficient resource allocation strategy centered on clustering. Initially, we employ the Fractional Frequency Reuse (FFR) technique to mitigate interference by segmenting cells into distinct zones. Subsequently, leveraging graph theory and convex optimization, we cluster the Femto Base Stations (FBS) to enhance efficiency. An algorithm prioritizing user fairness governs sub-channel allocation among FBSs. Additionally, a distributed power control algorithm dynamically adjusts FBS power levels, further enhancing system throughput. Comparative analysis reveals that our approach surpasses traditional non-clustered methods and three other comparison methods, exhibiting superior system throughput and Signal-to-Interference Ratio (SINR). Notably, our method significantly enhances user fairness, promoting greater satisfaction among Femto User Equipment (FUE) users.

3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Research on an Alpha Navigation Signal Detection Method Based on Multichannel Orthogonal Correlation

The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, the received Alpha navigation signal is susceptible to noise and mutual interference, which deteriorates signal detection performance. This paper presents a multichannel orthogonal correlation method for Alpha navigation signal detection. Once the three frequency signals of the Alpha navigation system are obtained using a notch filter, station identification is realized using a multichannel orthogonal correlation method and signal format. The selection of key parameters and the detection performance under noise and mutual interference are analyzed. This method’s detection probability exceeds 90% when the signal-to-noise ratio (SNR) is greater than −10 dB. The influence of mutual interference on the signal correlation peak is less than 1% when the signal-to-interference ratio (SIR) of the mutual interference is greater than −28 dB. The proposed method is verified using an actual signal collected using an Alpha receiver. The results show that an Alpha signal can be detected at an extremely low SNR. This method has strong practicability and satisfies the application requirements of an Alpha receiver.

An effective DOA estimation method for low SIR in small-size hydrophone array

The estimation ability of traditional direction of arrival (DOA) estimation methods is relatively fragile in small-size hydrophone arrays with limited space. Especially in low signal to interference ratio (SIR), the strong interference signals may submerge some weak signals of interest (SOI) and make DOA estimation difficult in response to this issue. This paper introduces an improved sparse DOA estimation method for practical multi-objective DOA estimation in complex scenarios. The main work is to introduce a noise weight constraint in the sparse iterative covariance process. It leads the algorithm to output sparse peaks and smooth spatial energy spectra and achieve faster fitting while reducing the probability of false peaks. The algorithm can complete DOA estimation of the multi-target reliably without prior information of sources. Then, we propose a fast region grid refinement method based on allocation reconstruction to increase angle resolution. The method increases the accuracy of multi-objective DOA estimation while reducing computational costs. Finally, simulation and experiment have verified the method's effectiveness.

Polarization adaptation to improve cell border area bitrates and system capacity in small cells

The target of this paper is to show the impact of polarization adaptation on the received signal quality in an outdoor small-cell deployment scenario. The signal-to-interference ratio (SIR) is the key factor in defining the achievable data rate, and the capacity of the cell. At the cell border area, the SIR value is typically low and causes a significant decrease in the system capacity and achievable data rates. These bad SIR areas at the cell border can be improved by using orthogonal polarizations in the neighboring cells rather than using all polarizations over the whole cell coverage area. For the research work of this paper, a series of measurements were carried out to measure a received signal power and a cross polarization discrimination (XPD) ratio while the signal is transmitted and received with a different set of polarizations. In the measurements, we have considered horizontal, vertical, +/− 45° slanted polarizations, and two different environments, urban street and open space, and three frequency bands, 970−1030 MHz, 2000−2030 MHz and 3364−3400 MHz. The measurement results revealed that at a different distance from the transmitter, for horizontal/vertical polarization, the average XPD is around 24.7 dB and 31.7 dB in the urban street and open area environments, respectively. For +/− 45° slanted polarization, the average XPD is around 11.5 dB and 12.1 dB in the urban street and open area environments, respectively. This paper goes on to propose polarization adaptation in each cell, where the primary polarization is valid for the whole service area of the cell, and secondary polarization is only used in close proximity of the base station antenna. Considering the results, it is emphasized that system capacity can be significantly improved by having only one channel with good SIR values compared to multiple channels with bad SIR values. However, MIMO channels with orthogonal polarizations or with spatial multiplexing can be utilized in the closed vicinity of the base station, i.e., in an area with good SIR values. It is shown that the overall cell capacity can be increased by almost 35% by utilizing polarization adaptation compared to MIMO 2 × 2.

A novel approach to schizophrenia Detection: Optimized preprocessing and deep learning analysis of multichannel EEG data

Schizophrenia diagnosis, characterized by cognitive deficits, hallucinations, and delusions, poses challenges due to its complex nature. Electroencephalogram (EEG) signals provide insights into underlying brain states, enabling efficient and accurate diagnosis. This study introduces a novel approach, utilizing the Mutation-boosted Archimedes Optimization (MAO) algorithm to optimize preprocessing steps for improved EEG data quality. The spatial and temporal patterns are extracted from multichannel EEG data through Convolutional Neural Networks (CNNs) based Long Short-Term Memory (LSTM) network. Entropy-related features, including spectral and permutation entropy, are integrated to capture EEG complexity. The Schizophrenia Detection layer leverages these features for classification. The fitness function combines classification accuracy and noise reduction, optimizing preprocessing and classification performance. Experimental evaluation on a diverse dataset validates the effectiveness of the proposed approach. Achieving accuracy of 98.2%, F1-score of 97.2%, specificity of 97.5%, precision of 98.5%, and sensitivity of 98.9%, the method surpasses existing techniques. Moreover, the MAO algorithm enhances signal quality, evident through improved Signal-to-Noise Ratio (SNR), Signal-to-Interference Ratio (SIR), and reduced artifact rejection rates. Overall, this research demonstrates the potential of the Mutation-boosted Archimedes Optimization algorithm in enhancing EEG data preprocessing for accurate schizophrenia detection. The integration of deep learning architectures and advanced optimization techniques presents a promising avenue for improving mental disorder diagnoses through EEG signal analysis.

A novel semi-blind source separation framework towards maximum signal-to-interference ratio

For the independence-based blind source separation (BSS) methods, effective modeling of the source signals is important. When applying the auxiliary function techniques, different source models normally result in different forms of the weighted covariance matrices, which in turn are used to compute the demixing filters. This paper proposes a new algorithmic framework termed minimum variance independent component analysis (MVICA), which is rigorously derived from the conventional BSS methods by determining the weighted covariance matrices with the maximum signal-to-interference ratio (SIR) criterion.The maximum-SIR weighted covariance matrix is approximately proved to be the interference covariance matrix. A deep neural network-supported implementation of the proposed MVICA algorithm is subsequently presented. Furthermore, we give a revealing insight into the relationships between MVICA and the existing speech separation approaches including conventional BSS methods and several mainstream beamforming techniques. Experimental results show the superiority of MVICA over the state-of-the-art BSS techniques and beamformers under various conditions, in terms of not only SIR but also signal-to-distortion ratio, speech intelligibility and perceptual quality and automatic speech recognition accuracy.

Accumulation and Elimination: A Hard Decision-Based Multi-User Interference Cancellation Method in Satellite Communication System

With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (C/N0) resulting from long-distance transmission poses a significant concern. The parallel interference cancellation (PIC) algorithm, utilized within the paradigm of multi-user detection (MUD), exhibits the capability to effectively mitigate the impact of MAI within the same system. Simultaneously, coherent accumulation serves as a means to substantially enhance the correct detection probability (Pcd) at low C/N0. In this study, a signal acquisition method for multi-user spread spectrum satellite receivers is proposed, which employs interference cancellation and coherent accumulation as its core mechanisms. Furthermore, we introduce a power estimation method based on the outcomes of signal acquisition, which can be integrated into the signal reconstruction module of PIC. Finally, we implement the aforementioned algorithms in both simulation and hardware platforms. Remarkably, we observe that when the interference-to-signal ratio (ISR) caused by MAI equals 20 dB, the improved algorithm attains a maximum Pcd of 0.95 within the high signal-to-noise ratio (SNR) region, closely approaching the theoretical limit for the bit error rate (BER). The experimental results prove the effectiveness and feasibility of the acquisition algorithm. In summary, the enhanced algorithm holds vast potential for widespread implementation in multi-user spread spectrum communication systems.

Clinical Features, Treatment Patterns and Outcomes of 864 Newly Diagnosed Hodgkin Lymphoma Patients in Latin America and the Impact of PET Scan Availability on Survival: A Study from the Grupo De Estudio Latinoamericano De Linfoproliferativos (GELL)

Background: Hodgkin lymphoma (HL) is a lymphoid neoplasm with high cure rates. Around 90% of patients (pts) will achieve response to first-line treatment. Real-world studies in Latin America (LATAM) are lacking. Moreover, inequity in the access to imaging technology and drugs represent a real challenge for Latin American countries with palpable influences in patient outcomes. Herein, we describe the clinical features, treatment patterns, and outcomes of HL pts managed in LATAM. Method: We conducted a retrospective cohort study of adults aged ≥17 years with newly diagnosed HL across academic centers in 7 LATAM countries from 2003 to 2022, with follow-up through July 2023. Medical records were manually reviewed, and data were abstracted in a standardized form. Cancer staging was performed by Ann Arbor and German HL Study Group criteria. Survival probabilities were estimated with the Kaplan-Meier method and compared with the Log-rank test. Multivariable Cox regression models were fitted by cancer stage stratification. A landmark analysis was performed to assess the lack of PET scan availability at the end of treatment (EOT) in LATAM. Results: Of 965 pts identified, 864 had sufficient data for analysis. Pts were young (56% <40 years; median 36 [17-88]) with slight male predominance (53%). 9 (3%) pts were HIV positive. Nodular sclerosing (62%) and mixed cellularity (22%) were the most common HL subtypes (p<0.01). Clinically, most pts had good performance status (ECOG≤1, 96%), no B symptoms (62%) and normal serum LDH (60%). Mediastinal presentation was seen in 14%, bulky mass (>10 cm) 23%, nodal involvement >3 sites 34% and extranodal involvement 25%. Advanced stage was common (57%); 22% and 21% had favorable and unfavorable limited stage HL, respectively. ABVD was the most common first-line regimen (96%); 8 pts received BEACOPP, and 1 BV-AVD. Radiation was utilized in 40% of limited and 21% of advanced stage HL. Only 63% and 48% had interim (iPET) and EOT PET scan, respectively. Most pts were managed at private than public institutions (59%, p<0.01). With a median follow up of 65 [59-71] months the 5-yr overall survival (OS) and progression-free survival (PFS) rates in all HL pts were 85% (82-88, 95% CI not reached, NR) and 64% (60-69, 95% CI NR), respectively. Better OS and PFS were seen in pts younger than 60 (p<0.01), ECOG≤1 (p<0.01) and limited stage HL (p<0.01). Overall response rate at the EOT assessed by either PET or CT scan was 89% (76% complete and 13% partial). In those assessed by iPET, results yielded 86% keeping same plan, 7% de-escalation and 3% escalation. In our cohort, 11% had refractory HL and 18% relapsed after achieving response. Given the inferior PFS to first-line seen in our LATAM HL pts compared to historical cohorts, we looked at possible factors associated to early relapse. Those assessed by PET at the EOT had significantly superior 5-yr PFS than those assessed by CT scan (PET vs CT: favorable HL 91 vs 71%; unfavorable 83 vs 35%; advanced 79 vs 51%) ( Figure). OS was inferior only in pts with unfavorable HL not assessed by PET (94 vs 76%, p=0.03). In the multivariable analysis, the lack of PET assessment at the EOT was associated with short PFS in unfavorable (aHR 7.81 [1.46-41.88], p=0.02) and advanced (aHR 17.35 [4.66-64.61], p<0.01), and a non-statistically significant worse PFSvin favorable HL (aHR 5.05 [0.56-45.72], p=0.15). Other factors associated to short PFS were extranodal disease (aHR 35.29 [5.62-209.07], p<0.01) in unfavorable, and high serum LDH (aHR 2.31 [1.2-4.46], p=0.01) in advanced HL. Interestingly, pts with advanced HL managed in public institution had less risk for relapse than those in private institutions (aHR 0.13 [0.03-0.51], p<0.01). Conclusion: To our knowledge, this is the largest cohort of newly diagnosed HL pts in LATAM in the real-world setting. We observed similar clinical features in LATAM HL pts than those previously reported. ABVD was widely utilized in LATAM, and the 5-yr OS of 85% in all pts aligns with international estimates. However, our findings underscore the impact of limited access to PET scan at the EOT in LATAM, leading to lower PFS outcomes compared to those reported in developed countries. Despite this challenge, salvage therapy seems to rescue our LATAM HL pts, thus, OS remains optimal. To improve outcomes and minimize late effects following treatment completion, increasing the use of PET-adapted therapy for managing adult pts with HL in LATAM should be prioritized.

Modification of Simple Antenna Pattern Models for Inter-Beam Interference Assessment in Massive Multiple-Input-Multiple-Output Systems.

The occurrence of cross-beam interference in the received signal is one of the main problems that limit the possibilities of massive multiple-input-multiple-output technology (massive-MIMO) in fifth-generation (5G) systems. Thus, the evaluation of the level of this interference is one of the most important procedures in the spatial planning of currently wireless networks. We propose a novel modification of simple antenna pattern models, which is based only on changing the directivity of real antenna system patterns. This approach is independent of the antenna system's type, structure, and analytical description. Based on the developed modification, the original methodology for assessing the signal-to-interference ratio (SIR) from adjacent beams of a common antenna system is presented. The change in the radiation direction and the accompanying change in the complex shape and parameters of the real antenna beam pattern is one of the problems that significantly hinders the evaluation of the analyzed interference. Hence, in the presented methodology, we propose using our modification. In this case, the modification is reduced to a proportional change in the directivity concerning the real antenna system, which results from a change in the beam direction. The simulation studies used a multi-ellipsoidal propagation model and a real massive MIMO antenna pattern description from 3GPP. For the SIR error analysis, the 3GPP pattern is used as a reference. The simulation results show that modifying simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under line-of-sight (LOS) and non-LOS conditions, respectively.

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)

To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corridors</i> , that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters and alleviating the burden on the ground transportation system. For safety, a UAM aircraft must use air-to-ground communications to report flight plan, off-nominal events, and real-time movement to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a suitable spatial model is proposed. For the considered setup, assuming that any given aircraft communicates with the closest GBS, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft’s turbulence model under different parameter settings, offering useful UAM design guidelines.

High cost-effective photonic assisted in-band interference cancellation scheme for centralized radio access network towards future 5G communication

A photonic-assisted scheme capable of self-interference cancellation (SIC) and image rejection mixing (IRM) is demonstrated to solve the intractable problem of in-band interference for in-band full duplex (IBFD) systems with high frequency and large bandwidth. In the proposed scheme, a single optical path configuration is constructed based on an integrated modulator applied as the main device. The SIC operation is implemented in the optical domain making it immune to the influence caused by fiber dispersion. Owing to the intermediate frequency (IF) signal amplitude regulation mechanism under the combined action of fiber dispersion induced phase and double sideband (DSB) modulation, the scheme can realize IRM without increasing the system complexity. Experimental results show that the 50 MHz 16-quadrature amplitude modulation (16-QAM) signal of interest (SOI) centered at different frequencies are down-converted to 2.5 GHz. The depth of SIC and image-rejection (IR) is over 35 dB and 25 dB after transmitted through a span of 10.1 km optical fiber. The received 16-QAM down-converted signal can be recovered with an error vector magnitude (EVM) which satisfies the requirement of 3GPP-specified EVM limit. The signal recovery performance, SIC depth and image rejection ratio (IRR) under different signal to interference ratio (SIR) is also investigated. In addition, a comparison of relevant literature with this scheme from the perspective of technical discussion is also presented. The proposed scheme can realize effective functional integration with compact structure, simple parameter tuning mechanism, improved stability and high cost-effectiveness to solve the in-band interference problem which is of great value to be applied in IBFD centralized radio access networks (CRAN) towards future 5G communication.

A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)

A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.

Switching mode allocation in planning paths for vehicular network communication

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

Maximizing Signal Quality for One Dimensional Cells In Mobile Communications

In this work, the cellular network performance based on other cell interference predictions is presented. It presents a mathematical model of co-channel interference analysis in hexagonal and linear cell shapes through a log-distance propagation model to investigate the effect of path loss exponent value on the received signal quality of the downlink. Simulation results obtained show that as the power exponent value increase, the interfering signals attenuation is increased resulting in received signal quality improvement. The signal-to-interference ratio (SIR) received by subscribers close to the cell edge will be less due to the contribution of the near-interfering cells especially when multiple layers of interfering cells are considered. The simulations confirmed that the impact of multi tiers of interfering cells cannot be ignored in systems of small cluster size as they may contribute to system performance degradation.

Fr-Aro: Secure interference aware fuzzy based clustering and hybrid optimization driven data routing in VANETs

Vehicular ad hoc network (VANET) is composed of a group of nodes or vehicles that enable communication with other connected vehicles despite a lack of infrastructure. To design a reliable routing protocol is a major challenge for ensuring effective data communication among vehicles. This research develops a fuzzy logic concept to cluster the nodes and a Cluster Head is selected based on the fitness constraints. Thereafter, the optimal path used to route the data packets are selected using the proposed Fr-ARO based on the fitness, like energy consumption, delay, distance, Signal-to-Interference Ratio (SIR), entropy-weighted model, and trust factors. Some of the trust parameters considered for the routing mechanism are direct trust, indirect trust, historical trust, recent trust, and availability factor. Moreover, the optimal path is computed by the developed Fr-ARO algorithm that is devised through the incorporation of Aquila Optimizer (AO) with Remora Optimization Algorithm (ROA). The proposed model attains higher performance with the metrics, like energy, trust, distance, and SIR of 0.5744 J, 0.6123, 3.062 m, and 38.4674.