Signal To Interference And Noise Ratio Research Articles

Quantized Massive MIMO Systems With Multicell Coordinated Beamforming and Power Control

In this paper, we investigate a coordinated multipoint (CoMP) beamforming and power control problem for base stations (BSs) with a massive number of antenna arrays under coarse quantization at low-resolution analog-to-digital converters (ADCs) and digital-to-analog converter (DACs). Unlike high-resolution ADC and DAC systems, non-negligible quantization noise that needs to be considered in CoMP design makes the problem more challenging. We first formulate total power minimization problems of both uplink (UL) and downlink (DL) systems subject to signal-to-interference-and-noise ratio (SINR) constraints. We then derive strong duality for the UL and DL problems under coarse quantization systems. Leveraging the duality, we propose a framework that is directed toward a twofold aim: to discover the optimal transmit powers in UL by developing iterative algorithm in a distributed manner and to obtain the optimal precoder in DL as a scaled instance of UL combiner. Under homogeneous transmit power and SINR constraints per cell, we further derive a deterministic solution for the UL CoMP problem by analyzing the lower bound of the SINR. Lastly, we extend the derived result to wideband orthogonal frequency-division multiplexing systems to optimize transmit power and beamformer for all subcarriers. Simulation results validate the theoretical results and proposed algorithms.

Local large deviation principle, large deviation principle and information theory for the signal-to-interference-plus-noise ratio graph models

Given devices space D, an intensity measure λm ϵ (0, ∞), a transition kernel Q from the space D to positive real numbers ℝ+, a path-loss function (which depends on the Euclidean distance between the devices and a positive constant a), we define a Marked Poisson Point process (MPPP). For a given MPPP and technical constants , we define a Marked Signal-to- Interference and Noise Ratio (SINR) graph, and associate with it two empirical measures; the empirical marked measure and the empirical connectivity measure. For a class of marked SINR graphs, we prove a joint large deviation principle(LDP) for these empirical measures, with speed λ in the τ-topology. From the joint large deviation principle for the empirical marked measure and the empirical connectivity measure, we obtain an Asymptotic Equipartition Property(AEP) for network structured data modelled as a marked SINR graph. Specifically, we show that for large dense marked SINR graph one requires approximately about λ 2 H(Q × Q)/log 2 bits to transmit the information contained in the network with high probability, where H(Q × Q) is a properly defined entropy for the exponential transition kernel with parameter c. Further, we prove a local large deviation principle (LLDP) for the class of marked SINR graphs on D, where , with speed λ from a spectral potential point. From the LLDP we derive a conditional LDP for the marked SINR graphs. Note that, while the joint LDP is established in the τ-topology, the LLDP assume no topological restriction on the space of marked SINR graphs. Observe also that all our rate functions are expressed in terms of the relative entropy or the kullback action or divergence function of the marked SINR on the devices space D.

Full-Duplex Nonorthogonal Multiple Access With Layers-Based Optimized Mobile Relays Subsets Algorithm in B5G/6G Ubiquitous Networks

In view of noteworthy communications performance improvements for future B5G/6G ubiquitous networks (such as cognitive Internet of Things (IoT) network, UAV communications, air-space-ground integration network, and so on), cooperative communications (CCs) diversity with relays selection algorithms has been extensively studied to significantly improve communications quality. In light of unsolved millennium issue in CC—nondeterministic polynomial (NP) and NP-hard problems have not been solved efficiently for relays subsets selection, in this article, theorems of relays subsets with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -layers power allocation standard have been further put forward to explore better performance in B5G/6G networks. We propose unified layers-based optimized mobile relays subsets algorithms for full-duplex (FD) nonorthogonal multiple access (NOMA) to greatly improve transmission rates. After taking into account fundamental properties of relays, such as mobile relays nodes state, relays locations, fading characteristics, and so on, optimized FD-NOMA algorithm based on these relays features has been presented to improve transmission rates, and a related series of relays subsets theorems has been derived and proved, then minimum upper bound of maximum transmission rates is estimated to reveal two-way balanced optimal transmission conclusion for FD-NOMA. Furthermore, simulation results show that proposed algorithm has 1–3 dB advantage than other relays subset algorithms for signal to interference and noise ratio (SINR), and it can more efficiently transform NP-hard problem to P problem for relays selection. Importantly, under revealed two-way balanced optimal transmission phenomenon for FD-NOMA, the proposed scheme with FD-NOMA has more than two times minimum device-to-device (D2D) transmission rates higher than other classical relays selection algorithms.

Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

A Machine Learning Approach for 5G SINR Prediction

Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption.

COMMUNICATION NETWORK AND QOS EVALUATION FOR FORMATION CONTROL OF UNMANNED SURFACE VEHICLES

The main purpose of this article is to design efficient communication networks for the formation control of multiple distributed Unmanned Surface Vehicles (USVs). First, a hybrid communication network architecture, combining remote communication and wireless Ad hoc network technology is proposed. Second, an improved Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is adopted to prolong the life cycle of the communication network of the USV fleet. Subsequently, some QoS indicators of the USV communication network are evaluated by establishing wireless network channel model and Signal to Interference and Noise Ratio (SINR) model. In particular, the packet error ratio, average time delay and connectivity under different formation architecture are investigated. Finally, some discussions and future work on the QoS of the USV communication network are concluded.

Low-Complexity Beamformer Design for Joint Radar and Communications Systems

Joint radar and communication (JRC) system is expected to significantly improve the utilization of spectrum resources, system performance, and reduce the equipment cost and size. In this letter, a JRC system containing a collocated multi-input multi-output (MIMO) radar and a multi-user communication base station is considered, which can serve several downlink users and detect multiple targets simultaneously. The radar signal covariance matrix and the communication physical precoder are jointly designed by matching the desired beampattern for radar detection while maximizing the downlink communication signal to interference and noise ratio (SINR). By relaxing the original problems, the iterative process with closed-form expressions is proposed to obtain the sub-optimal solutions of the system. Compared with solving the optimization problems directly, the proposed algorithm can significantly reduce the computational complexity with a little loss of system performance. Finally, simulation results show the effectiveness of the proposed algorithm.

DNN‐based interference mitigation beamformer

In classical beamforming techniques such as minimum mean squared error and minimum variance distortion-less response, for computing optimum weight vector of an antenna array, direction of arrival (DoA) of desired signal should be known as prior knowledge. In this study, a deep neural-network-based beamformer is proposed for estimating the desired signal in presence of noise and interference without requiring prior knowledge on the DoA of the desired signal. In the proposed beamformer, a bidirectional long short-term memory (bi-LSTM) is employed for estimating samples of interferences. On the other hand, samples of the desired signal are estimated by either another bi-LSTM or a convolutional neural network. The signal to interference and noise ratio (SINR) at the output of the proposed beamformer is 10 dB higher than the SINR at the output of the classical beamformers when the number of available snapshots is as low as 100. The proposed beamformer has promising performance when the input interference to signal ratio is as high as 34 dB and the input signal-to-noise ratio is as low as −10 dB.

An Optimum User Association Algorithm in Heterogeneous 5G Networks Using Standard Deviation of the Load

Fifth-generation (5G) wireless networks and beyond will be heterogeneous in nature, with a mixture of macro and micro radio cells. In this scenario where high power macro base stations (MBS) coexist with low power micro base stations (mBS), it is challenging to ensure optimal usage of radio resources to serve users with a multitude of quality of service (QoS) requirements. Typical signal to interference and noise ratio (SINR)-based user allocation protocols unfairly assign more users to the high power MBS, starving mBS. There have been many attempts in the literature to forcefully assign users to mBS with limited success. In this paper, we take a different approach using second order statistics of user data, which is a better indicator of traffic fluctuations. We propose a new algorithm for user association to the appropriate base station (BS) by utilizing the standard deviation of the overall network load. This is done through an exhaustive search of the best user equipment (UE)–BS combinations that provide a global minimum to the standard deviation. This would correspond to the optimum number of UEs assigned to every BS, either macro or micro. We have also derived new expressions for coverage probability and network energy efficiency for analytical performance evaluation. Simulation results prove the validity of our proposed methods to balance the network load, improve data rate, average energy efficiency, and coverage probability with superior performance compared with other algorithms.

Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming

Multiple-input multiple-output (MIMO) antenna scheme is an effective technique for future terrestrial broadcasting systems such as digital video broadcasting-next-generation handheld (DVB-NGH) to overcome the limits on information theory of traditional single-antenna wireless communications without any additional bandwidth or increased transmit power. In this paper, we propose a hybrid beamforming scheme for dual-polarized MIMO spatial multiplexing DVB-NGH broadcasting systems. The system of interest makes use of phase shifters and amplitude attenuators for the digital-analog precoder at beamforming stage of the transmitter end to maximize the signal-to-noise ratio to increase the channel capacity of the DVB-NGH systems. At the receiver end, the maximum likelihood (ML) detector is used to evaluate the system performance. We consider the signal-to-interference-and-noise ratio (SINR) and the achievable average capacity for the DVB-NGH MIMO with various FFT sizes, number of transmit antennas, and different modulation schemes. The performance results on bit error rate, channel capacity, and beampatterns show that the proposed hybrid beamforming and dual-polarized MIMO spatial multiplexing schemes provide more robustness against signal interference by beamforming and/or nulling techniques. The simulation results also show that the proposed system achieves higher capacity than the existing MIMO schemes for the DVB-NGH systems.

Block Diagonal Hybrid Precoding and Power Allocation for QoS-Aware BDMA Downlink Transmissions.

Beam Division Multiple Access (BDMA) with hybrid precoding has recently been proposed for multi-user multiple-input multiple-output (MU-MIMO) systems by simultaneously transmitting multiple digitally precoded users’ data-streams via different beams. In contrast to most existing works that assume the number of radio frequency (RF) chains must be greater than or equal to that of data-streams, this work proposes a novel BDMA downlink system by first grouping transmitting data-streams before digitally precoding data group by group. To fully harvest the benefits of this new architecture, a greedy user grouping algorithm is devised to minimize the inter-group interference while two digital precoding approaches are developed to suppress the intra-group interference by maximizing the signal-to-interference-and-noise ratio (SINR) and the signal-to-leakage-and-noise ratio (SLNR), respectively. As a result, the proposed BDMA system requires less RF chains than the total number of transmit data-streams. Furthermore, we optimize the power allocation to satisfy each user’s quality of service (QoS) requirement using the D.C. (difference of convex functions) programming technique. Simulation results confirm the effectiveness of the proposed scheme.

An improved water-filling algorithm based on power allocation in network MIMO

Inter-Cell Interference (ICI) could affect the performance of the LTE-Advanced (LTE-A) system and can be evaluated according to the user’s Signal to Interference and Noise Ratio (SINR). Owing to this, the power and frequency allocation schemes can be utilized in order to attain an optimum trade-off within the obtained SINR and the resulting interference. In this study, power allocation was integrated into the Dynamic Fractional Frequency Reuse (DFFR) scheme and Network MIMO (NetMIMO) to reduce ICI and enhance the performance of LTE-A downlink. Moreover, this study focuses on the design and development of a novel power allocation algorithm, which is an extension to the previous Improved Water-Filling algorithm (IWF), namely, Low-Complexity Improved Water-Filling algorithm (LC-IWF). Results indicated a reduction in computations and enhancement in interference rejection performance in comparison to the existing algorithms by removing the Lagrange multiplier and water level. Moreover, simulation results revealed that the proposed algorithm enhances the throughput and fairness of up to 20% and 25%, respectively. Furthermore, the power consumption was reduced by 41% and the running time was 5–10 times faster than the IWF algorithm while attaining the best solution. The proposed power allocation algorithm provided a significant improvement in the performance of the NetMIMO LTE-A system, which can be extended for future 5G networks.

Resource Allocation and 3-D Placement for UAV-Enabled Energy-Efficient IoT Communications

As the commercial launch of the fifth-generation (5G) wireless communications gets near, the trend from the Internet of Things (IoT) to the Internet of Everything (IoE) is emerging. Due to the advantages of the high mobility, high Line-of-Sight (LoS) probability and low labor cost, unmanned aerial vehicles (UAVs) may play an important role in the future IoT communication networks, e.g., data collection in remote areas. In this article, we study the 3-D placement and resource allocation of multiple UAV-mounted base stations (BSs) in an uplink IoT network, where the balanced task for the UAV-BSs, the limited channel resource, and the signal interference are taken into consideration. In the considered system, the total transmission power of IoT devices is minimized, subject to a signal-to-interference-and-noise ratio (SINR) threshold for each device. First, aiming to balance the task of each UAV, we propose a clustering algorithm based on an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means method to divide IoT devices into several groups so that the number of devices in each group is roughly the same. Then, based on matching theory, a modified-Hungarian-based dynamic many–many matching (HD4M) algorithm is designed for assigning subchannels to IoT devices, which can efficiently mitigate the interference. Finally, we jointly optimize the transmission power of IoT devices and the altitudes of UAVs via an alternating iterative method. The simulation results show that the total transmission power decreases significantly after applying the proposed algorithms.

Self-interference cancelation in the presence of non-linear power amplifier and receiver IQ imbalance

In the in-band full-duplex(FD) systems, the self-interference (SI) power can be more than 100 dB higher than the power of the received data signal. In order to enable the FD transmission, several SI cancelation stages are needed in a FD transceiver. By combining the cancelation at the radio frequency (RF) with a specially designed antenna and cancelation circuitry and SI cancelation at the digital baseband, the required level of SI cancelation can be achieved even with a non-linear power amplifier. In this paper, a FD transceiver architecture is modeled with simulation tools that allow to use realistic antenna and analog transceiver models and at the same time enable algorithm studies. The analog SI cancelation at the RF is controlled by the baseband digital processing unit, and the tuning of the RF canceler is performed with an automatic gain control enhanced iterative algorithm. The combined cancelation performance of the antenna and RF canceler varies between 62 and 82 dB depending on the studied cases. The digital baseband SI cancelation is based on the Hammerstein model in order to take the power amplifier non-linearity into account. The coefficients of the Hammerstein model are estimated with a self-orthogonalizing adaptive algorithm. When realistic phase noise and IQ imbalance values are taken into account, the SI after all the cancelation stages can decrease the signal-to-interference-and-noise-ratio (SINR) by few decibels (dB). In order to further enhance the SI cancelation, the Hammerstein based SI canceler is extended to cancel also the effect of the receiver IQ imbalance. With the extended baseband canceler, the cancelation performance is mainly limited by the phase noise

Empirical Feasibility Analysis for Energy Harvesting Intravehicular Wireless Sensor Networks

Vehicle systems currently utilize wired networks for power delivery and communication between nodes. Wired networks cannot practically accommodate nodes in moving parts and with the increasing functional complexity in vehicles, they require kilometer-long harnesses, significantly increasing fuel consumption, manufacturing, and design costs. Alternatively, energy harvesting intravehicular wireless sensor networks (IVWSNs) can accommodate nodes in all locations and they obviate the need for wiring, significantly lowering costs. This article empirically analyzes the feasibility of such an IVWSN framework via extensive in-vehicle measurements for communications at 2.4 GHz, ultrawideband (UWB), and millimeter-wave (mmWave) together with radio frequency (RF), thermal, and vibration energy harvesting. Our analyses indicate mmWave performs best for short Line-of-Sight (LoS) links in the engine compartment with performance close to UWB for LoS links in the chassis and passenger compartments considering worst case signal-to-interference-and-noise ratio (SINR). For non-LoS links, which appear mostly in the engine compartment and chassis, UWB provides the highest security and reliability. 2.4 GHz suffers heavily from interference in all compartments while UWB utilizes narrowband suppression techniques at the cost of lower bandwidth; mmWave inherently experiences very low interference due to its propagation characteristics. On the other hand, RF energy harvesting provides up to 1 mW of power in all compartments. Vibration and thermal energy harvesters can supply nodes consuming <; 10 mW in the engine compartment and <; 5 mW nodes in the chassis. In the passenger compartment, thermal harvesting is not available due to low temperature gradients, but vibration and RF sources can supply <; 1 mW nodes.

Connectivity optimization in interference-limited random DSA networks

Network connectivity in dynamic spectrum access (DSA) networks has been well studied–most of which are under the unit disk model. However, the disk model does not capture the primary-secondary and secondary-secondary interference; hence signal to interference and noise ratio (SINR) based models are more appropriate. Moreover, in the SINR regime, there is no unique way to characterize connectivity and hence its maximization becomes even more challenging.In this paper, we develop the long eluding network connectivity objective function which we use to build three connectivity optimization techniques each of which targets a particular network setup. The proposed techniques are: i) Fittest deployment density, ii) Fittest receive-only ratio, and iii) Fittest TDMA slotting.To develop the aforementioned objective function, we start by addressing the lack of any relation between deployment density and network connectivity in interference-limited DSA networks. Next, percolation theory in conjunction with the Boolean model are utilized to develop such a relationship between the density of the percolation visible nodes and the network connectivity in interference limited environments. Finally, we use that relation to build the objective function for connectivity maximization along three optimization techniques. Theoretical findings are validated by simulating networks under various scenarios. Results provide a blueprint to establish and maximize connectivity using physical layer parameters (density, coverage radius, etc.) which can be used in conjunction with higher layer techniques. Also, tackling the connectivity problem at the physical layer relieves the other higher layers like the MAC layer from excess signaling and complex protocol designs.

Adaptive Interference Suppression of Phase-Only Thinned Arrays via Convex Optimization

For antenna arrays, the conventional beamformer (CB) is perhaps the simplest kind of beamforming techniques with only phase shifters connected to the antenna elements. However, the output signal to interference and noise ratio (SINR) performance is usually quite away from the optimum. Besides, as the number of antenna elements increases, the hardware cost and power consumption are still too high for many applications. In this article, based on the phase-only CB, we propose different ways for thinning this kind of antenna arrays, which can be efficiently solved via common convex-optimization solvers. Specifically, the first-derived phase-only thinned array can largely increase the output SINR with fewer active antenna elements than the CB. The second one can keep the output SINR performance as that of the CB with a significantly smaller number of active antenna elements than that required by the CB. The third criterion is to add an additional constraint on the number of active antenna elements while maximizing the output SINR, which may benefit the hardware planning. Simulation results confirm the feasibilities of these methods, and the above-stated tasks are all fulfilled. Therefore, the proposed phase-only thinned arrays have the flexibility to provide various levels of array performance with reduced hardware resources.

Uplink Performance Analysis for Millimeter Wave Cellular Networks With Clustered Users

The upcoming wireless cellular networks will make use of higher segments of the frequency spectrum, i.e., millimeter wave (MMW) bands, for enabling extremely high data rates to support content-rich applications. In this paper, we investigate the uplink performance of MMW cellular networks with clustered users. By modeling the locations of users as a Poisson Cluster Process (PCP), we derive tractable expressions to evaluate the signal-to-interference-and-noise-ratio (SINR) coverage probability for two user association strategies, i.e., the closest selection (CS) strategy, where the nearest LoS BS serves the user and the strongest-selection (SS) strategy, where the user communicates with the BS that provides the most significant received signal among all BSs. Numerical results are provided to validate the accuracy of the analytical model under various system parameters. The results show that regarding SINR coverage probability, the SS strategy outperforms the CS strategy in the environment with dense blockages. The results also demonstrate that the moderate fractional power control (FPC) factors should be used at the low SINR thresholds, while full FPC is optimal for the high SINR thresholds.

Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks

Massive multiple-input-multiple-output (MMIMO) in the mmWave band is an essential technique to achieve the desired performance for 5G new radio (NR) systems. To employ mmWave MMIMO technology, an important challenge is maintaining seamless mobility to users because we need to consider beam-switching within a cell besides the handover between cells. For mobility management in 5G NR systems, 3GPP specified a beam-level-mobility scheme that includes beam pairing and maintenance between a transmitter (Tx) and receiver (Rx) pair. We propose a unific-measurement report based mobility management scheme for improved radio-link-failure (RLF) rate and the accuracy of the Tx-Rx-beam-pair (TRP) selection with low overhead in 5G mmWave MMIMO networks where both handover and beam-switching are required. Furthermore, we modeled a finite-state-machine (FSM) for a user terminal to evaluate performance gain based on a system-level-simulation (SLS). We use the FSM-based Monte-Carlo SLS for the experiment and compare the performance of the proposed scheme with that of existing schemes in the scenario where both beam and cell-level-mobility are necessary. We show that the proposed scheme achieves an improvement in terms of the 3-dB loss probabilities representing the accuracy of the TRP selection, signal-to-interference-and-noise-ratio (SINR), and RLF rates with a lower signaling overhead compared to existing methods.

Graph-based multi-user scheduling for indoor cooperative visible light transmission.

Recent progress in the indoor visible light communication (VLC) has shown promising signs of alleviating an increasing strain on the radio frequency spectrum and enhancing transmission capacity. Nevertheless, the indoor VLC usually suffers from inter-channel interference (ICI) due to the dense light-emitting diode (LED) deployment. The ICI is considered as a key factor affecting signal to interference and noise ratio (SINR) and spectral efficiency. To address this challenge, an efficient multi-user scheduling framework that employs interference coordination and cooperative transmission is investigated based on the graph theory. To effectively mitigate ICI and maximize benefit of the cooperative transmission, the cell-centric (CC) and user-centric (UC) clustering are introduced for cooperative transmission. For the CC clustering, the multi-user scheduling problem under the proportional fairness criterion is formulated to maximize spectral efficiency while ensuring user fairness. Such a problem is solved by linear programming and greedy algorithms after transforming it into a maximum weighted independent set problem with the aid of a modified interference graph. For the UC clustering, the multi-user scheduling problem under the max-min criterion is formulated and solved by a proposed heuristic approach based on the bipartite graph theory. Numerical results show that the proposed graph-based scheduling is capable of providing up to 7.7 dB gain in SINR over the non-cooperative transmission. The bipartite graph scheduling offers high spectral efficiency and service fairness index. In the worst case with an occlusion probability of 1, a small SINR penalty of up to 1 dB is observed with the greedy algorithm. It is shown that the graph-based scheduling is robust to receiver rotation and occlusion in terms of spectral efficiency, SINR, and user fairness.