Signal To Interference And Noise Ratio Research Articles

Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided.

Design and Implementation of Transparent Cross‐Polarization Interference Compensation in a Wideband Dual‐Polarization Satellite Receiver

ABSTRACTIn this paper, simultaneous transmission on two orthogonal antenna polarizations in a polarization division multiplexing (PDM) fashion is studied for wideband satellite communication links using dual‐polarization satellite receivers for the purpose of doubling the data rate. In order to mitigate the cross‐polarization interference (XPI), a new digital blind and transparent XPI compensation method is proposed, coined as XPI correlation learning estimation and adaptive reduction (XPI‐CLEAR). The received signal‐to‐noise‐and‐interference ratio (SNIR) and packet‐error rate (PER) performance with this non‐data‐aided and non‐decision‐directed method is assessed in a comprehensively modelled XPI channel with effects such as depolarization due to atmospheric conditions, imperfect cross‐polarization discrimination (XPD) of the antennas at the transmitter and the receiver, memory effects due to frequency selectivity of the XPD, and differential frequency offset (DFO) between the two channels. The application of the XPI‐CLEAR method presents considerable energy efficiency improvements for all the studied XPI channel effects, and is particularly beneficial for higher order modulation. A low‐complexity hardware implementation with symbol rates up to 500 MBaud validates the XPI‐CLEAR method as a practical solution to increase the data rates of the satellite air interface and to achieve the doubling of the throughput of the satellite link by the use of PDM.

Optimizing cell association in 5G and beyond networks: a modified load-aware biased technique

Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm’s complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques.

Performance analysis of massive MIMO offshore system with distributed antenna subarrays

In this paper, we propose a transmission scheme of analog precoding for an offshore communication system with distributed antenna subarrays to serve multiple vessels. The proposed analog precoding scheme relies only on line-of-sight (LOS) components of the fading channels, since the accurate estimation of small-scale fading information is challenging due to the varying sea state of maritime wireless channels, such as reflection, scattering, and ducting effect. We derive an approximate expression for the ergodic achievable rate, and emphasize the multiplexing gain analysis of the whole system with a large antenna configuration. In addition, we derive the probability density function (PDF) of signal-to-interference-and-noise ratio (SINR) and further obtain closed-form expressions of symbol error rate (SER). Monte Carlo simulations verify the theoretical analysis, and numerical results demonstrate that the performance of the proposed LOS-based analog precoding scheme for the distributed multiple-input multiple-output (MIMO) offshore system is extremely near to the limitation of system performance, i.e., the ideal situation without any interference.

An interference-conscious reduced routing overhead protocol for Device-to-Device (D2D) Networks

The Device-to-Device (D2D) communication has attracted much popularity in the recent years. D2D communication requires a multi-hop route to establish the communication between D2D users which are not in the communication range of each other. D2D users communicate at lower power to keep the interference to a minimum level which necessitates the need of an interference aware routing scheme. The existing interference-aware routing schemes for D2D communication either consider observed average interference or Signal-to-Interference-and-Noise-Ratio (SINR). However, to the best of our knowledge, there is no scheme in the literature that satisfies the network requirements of having better SINR and minimum interference simultaneously. In this paper, we propose a novel routing metric and novel route discovery mechanisms for D2D communication. The novel routing metric, MIIS (Metric for Interference Impact and SINR) selects routes with higher SINR and lower interference. The novel route discovery mechanism, reactive centralized routing, takes advantage of the presence of BS to establish D2D routes which reduces routing overhead as compared to distributed routing scheme. We also extend the reactive centralized routing to proactive centralized routing. The performance evaluation in OMNeT++ network simulator verifies that our proposed schemes outperform other schemes in terms of average hop count, routing overhead, packet loss ratio and end-to-end delay.

UAV swarm communication reliability based on a comprehensive SINR model

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

Reinforcement Learning for FDA-MIMO Radar Power Allocation in Congested Spectral Environments

This paper studies the coexistence problem of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and communication systems. The communication system and radar share the same frequency band, and the overlap of the frequency spectrum will interfere with each other's performance, which constitutes a dynamic non-cooperative coexistence problem. We model the FDA-MIMO radar interference suppression process as a Markov decision process (MDP). The interference signals received by FDA-MIMO radar are generated by the communication system, including constant interference, frequency-hopping interference, and intermittent interference. FDA-MIMO radar realizes flexible transmission waveform spectrum control through transmission power allocation. Reinforcement learning (RL) is applied to obtain the optimal power allocation strategy of FDA-MIMO radar, and it is compared with the Sense and Avoid (SSA) algorithm. The simulation results show that the FDA-MIMO radar power allocation method based on reinforcement learning can effectively suppress the communication system's interference and improve the radar's signal-to-interference-and-noise ratio (SINR) and range resolution.

Performance evaluation of pair selection algorithms in device-to-device communication using relay-assisted techniques

Communication services that are dependable are crucial, particularly during emergencies when the regular infrastructure for communication may be disrupted or nonexistent. In such situations, device-to-device (D2D) communication can be a helpful choice since it allows user equipment (UE) that is close to one another to connect directly, bypassing the cellular network infrastructure. The primary focus of this thesis is the application of D2D communication in a decentralized emergency scenario with a damaged eNodeB. The main objective is to find an appropriate strategy for finding and selecting D2D couples by simulating several methods in MATLAB. This study compares three D2D pair selection algorithms: distance-based, Signal-to-Interference and Noise Ratio (SINR)-based, and data rate-based distance-based. The simulation results show that the data rate-based strategy is the most effective method for selecting D2D couples in emergency scenarios. In contrast to algorithms that rely on distance and SINR, this one reduces the chance of an outage by 20%. Bit error rate (BER), capacity, spectral efficiency, and energy efficiency are the three types of links that are assessed: direct links, relay links, and UE relay links. The results show that, with the lowest BER and maximum data throughput, the direct link is the most reliable and efficient communication option. However, the relay connection and the UE relay link show better overall spectral efficiency in comparison to the direct link, indicating their ability to transport more data per unit of bandwidth. The option that consumes the least energy among the three is the direct link. The study demonstrates the great potential of D2D communication in emergency scenarios where conventional communication infrastructure may not be available. The direct link is the most dependable and effective alternative for communication, according to the data, although the UE link can still function effectively in the event that the direct link is compromised. The data rate-based method is a useful strategy for finding and choosing D2D partners. The results of this study can direct the development of D2D emergency communication solutions in 5G networks.

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.

Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving throughput, but not considering delay. However, delay is the main factors for trasnmiitng NRT packets. This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.

Developing a comprehensive model for underwater MIMO OCC system.

Due to its spatial modulation feature and potential applications, optical camera communication (OCC) has gained significant attention in recent years for a range of applications including underwater. Nonetheless, due to the low frame rates of the camera, the OCC data rate is rather low, which is why multiple-input-multiple-output (MIMO) has been adopted to compensate. In MIMO systems, however, the signal from one light emitting diode (LED) may result in interference on the image sensor (i.e., the camera) resulting in inter-pixel interference (IPI). This paper presents a comprehensive model of the underwater OCC (UOCC) and experimentally verifies its performance under IPI by comparing signal to interference and noise ratio (SINR). The effect of distance between LEDs according to LED diameter D on signal to interference ratio (SIR) is presented and results indicate that coastal water has the SIR gain ∼2.5 dB for the link span of 1 to 6 m, and for harbor water channel length from 0.4 to 1.4 m the gain increased from ∼2 to ∼5 dB for d of 2D compared with d of 0.5D.

Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

SummaryMultiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches.

RIS Aided NR-U and Wi-Fi Coexistence in Single Cell and Multiple Cell Networks on Unlicensed Bands

To address the scarcity of spectrum resource incurred by explosively increasing traffic load in existing fifth generation (5G) networks, we consider exploiting Reconfigurable intelligent surface (RIS) to harmonize the coexistence of cellular networks and WiFi system on the unlicensed bands. In particular, RIS is capable of not only improving the performance of new radio on unlicensed bands (NR-U) by increasing the signal to interference and noise ratio (SINR) of cellular users, but also decreasing the interference to the existing WiFi system caused by NR-U. Thus, we investigate the performance of RIS aided coexisting networks both in single cell and multiple cell scenarios. For the single cell scenario, we aim to improve the worst transmission rate of UEs in NR-U network. The problem is solved by a proposed alternating optimization-based solution. For the multiple cell scenario, cell cooperation is exploited jointly with multiple RISs to maximize the sum-rate of all UEs. Although the problem is more challenging than that of single cell case, we propose a multi-agent reinforcement learning (MARL) algorithm to solve it. Numeral results verify the effectiveness of our proposed schemes and show that deploying RISs is a promising solution for the enhancement of NR-U networks.

Evaluating the level of inteference in UMTS/LTE heterogeneous network system

<p>The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.</p>

Performance Optimization of Multi-Base Station Heterogeneous Network Based on New Energy Power Supply

The future mobile communication system will face a challenge of explosive growth of access devices, which leads to a sharp increase of energy consumption at base stations (BSs). How to gain signal to interference and noise ratio (SINR) of more users while assuring system stability becomes a problem that has to be solved urgently. In this article, a multi-BS heterogeneous network system model which is equipped with renewable energy (RE) production devices is constructed and a hybrid optimization algorithm which is composed of a linear programming (LP) algorithm and multiobjective genetic algorithm is proposed. The hybrid algorithm considers RE output and existing inventory of batteries of BSs comprehensively and maximizes the sum of SINR (sum-SINR) of users in the system through user scheduling and beam forming. First, the hybrid algorithm relaxes the binary parameters of user scheduling into a continuous value during user scheduling, thus changing the mixed integer nonlinear programming problem into an LP problem. As a result, user scheduling scheme of maximizing sum-SINR of the system is solved. Subsequently, the algorithm changes the maximum sum-SINR problem of the system into a multiobjective optimization (MOP) problem of maximum SINR of different BSs during beam forming. The fast nondominated sorting genetic algorithm II that carries the elitist strategy gains the Pareto frontier of MOP based on semidefinite programming constraint and finds the beam forming scheme of maximum sum-SINR of the system. According to stimulation results, the proposed hybrid optimization algorithm can increase sum-SINR of system users effectively while assuring the system stability.

MIMO Radar Waveform Design for Multipath Exploitation Using Deep Learning

This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver.

Outage probability of NOMA systems with receive antenna selection in [formula omitted]-[formula omitted] fading channels

This article considers power-domain non-orthogonal multiple access (NOMA) for downlink communication systems that involve multiple users. The base station, equipped with a single transmit antenna, communicates simultaneously with multiple users, each equipped with a single receive antenna. The article examines the system’s outage probability (OP) performance in α−μ generalized fading channels. The article first derives the instantaneous signal-to-interference-and-noise ratio (SINR) and then obtains the OP expression for each user in a closed form. The derived expression for OP is general and includes special cases such as Nakagami-m, Weibull, Gamma, Rayleigh, and Exponential distributions. This α−μ expression helps quantify the improvement of the OP performance. In order to improve the OP performance, multiple antennas are included for the users, and then receive antenna selection (RAS) scheme is employed. Finally, the theoretical OP expressions are verified via Monte Carlo simulations.

Efficient Secure Communication in Zigbee Network Using the DNA Sequence Encryption Technique.

Zigbee IoT devices have limited computational resources, including processing power and memory capacity. Therefore, because of their complicated computational requirements, traditional encryption techniques are inappropriate for Zigbee devices. Because of this, we proposed a novel, "lightweight encryption" method (algorithm) is based on "DNA sequences" for Zigbee devices. In the proposed way, we took advantage of the randomness of "DNA sequences" to produce a full secret key that attackers cannot crack. The DNA key encrypts the data using two operations, "substitution" and "transposition", which are appropriate for Zigbee computation resources. Our suggested method uses the "signal-to-interference and noise ratio (SINR)", "congestion level", and "survival factor" for estimating the "cluster head selection factor" initially. The cluster head selection factor is used to group the network nodes using the "adaptive fuzzy c-means clustering technique". Data packets are then encrypted using the DNA encryption method. Our proposed technique gave the best results by comparing the experimental results to other encryption algorithms and the metrics for energy consumption, such as "node remaining energy level", key size, and encryption time.

Uplink SINR and rate analysis in massive MIMO systems with two-layer linear receiver

In a previous work, a two-layer linear receiver was proposed as a low-complexity scheme that can achieve a good trade-off between performance and complexity in a massive multiple-input multiple-output (MIMO) context. For this scheme, the antenna array is split up into a number of subsets and multi-cell minimum mean-square-error (M-MMSE) combining is applied in each subset at the first layer. Then, the resulting outputs are combined using maximal-ratio combining (MRC) at the second layer. This paper presents a more complete performance analysis. First, we provide an entirely analytical derivation of the distribution of the output signal-to-noise-and-interference ratio (SINR) of first-layer processing based on multivariate statistical theory. The analysis includes both cases where the subset size is smaller or greater than the total number of signals. Furthermore, upper bounds and approximations on the average uplink SINR and achievable rate are derived. Numerical simulations confirm our analysis results.

Joint Optimization of Trajectory and User Association via Reinforcement Learning for UAV-Aided Data Collection in Wireless Networks

Unmanned Aerial Vehicles (UAVs) can be used as aerial base stations for data collection in next-generation wireless networks due to their high adaptability and maneuverability. This paper investigates the scenario where multiple UAVs cooperatively fly over heterogeneous ground users (GUs) and collect data without a central controller. With the consideration of signal-to-interference-and-noise ratio (SINR) and fairness among users, we jointly optimize the trajectories of UAVs and the GUs associations to maximize the total throughput and energy efficiency. We formulate the long-term optimization problem as a decentralized partially observed Markov decision processes (DEC-POMDP) and derive an approach combining the coalition formation game (CFG) and multi-agent deep reinforcement learning (MADRL). We first formulate the discrete association scheduling problem as a non-cooperative theoretical game and use the CFG algorithm to achieve a decentralized scheme converging to Nash equilibrium (NE). Then, a MARL-based technique is developed to optimize the trajectories and energy consumption continuously in a centralized-training but decentralized-execution manner. Simulation results demonstrate that the proposed algorithm outperforms the commonly used schemes in the literature, regarding the fair throughput and energy consumption in a distributed manner.