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As one of the most important representatives for the edge intelligence, Unmanned Aerial Vehicle (UAV) can be used to provide the intelligent edge service, such as performing data collection, computation and transmission tasks in the air-to-ground networks. In the considered Sensed Data Collection Networks (SDCNs), the UAV is in charge of collecting and computing the sensed data from mobile ground nodes, and multiple performance metrics need to be jointly optimized, including the collected data’s Age of Information (AoI) and the Signal to Interference plus Noise Ratio (SINR) at the UAV from mobile sensor nodes, the data collection areas’ completeness, and the mobile sensor nodes’ energy consumption. Due to the absence of such a joint optimization framework, this paper formulates the mobile sensing nodes deployment problem in the UAV-aided SDCNs. Afterwards, the formulated problem is transformed into a utility maximization problem from the perspective of Coalition Formation Game (CFG), where the orders, actions, rule, and stability in the coalitions are analyzed and proved. Then, two algorithms are put forward to reach the NE (Nash Equilibrium) of the CFG by determining the mobile sensor nodes’ coalitions and positions. Finally, extensive simulations are conducted with different parameters settings to evaluate the correctness and effectiveness of the proposed algorithms. Results have shown that the mobile sensor nodes, which adopt the proposed cooperative order to formulate the coalitions, could reach lower AoI, larger SINR, higher data completeness, and less energy consumption than the typical Pareto order.

This paper aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with non-uniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signaldependent clutter is first established. Then the two-dimensional range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant non-convex problem, the sequential greedy optimization (SGO) algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent (CD) framework and a Lagrangian based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

This letter proposes a novel interference mitigation technique for spectral coexistence between large-scale non-geostationary orbit (NGSO) satellite systems by optimizing satellite antenna beam pointing. Firstly, a dynamic exclusion zone of NGSO satellite antenna beam pointing is defined. Secondly, we transfer frequency compatibility problem between NGSO systems into a constrained nonlinear multivariate optimization problem considering the joint effect of link distances as well as angles between links and solve it using Genetic Algorithm (GA). Then we select actual working parameters of Starlink and OneWeb satellite systems to verify the effectiveness of proposed interference mitigation technique in the worldwide. The available time of the interfered system is up to 98.5% of total simulation run time after interference mitigation. The results show that the proposed interference mitigation technique can effectively mitigate the interference between NGSO satellite systems and guarantee signal to interference plus noise ratio (SINR) at the interfered earth station.

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.

Non-orthogonal multiple access (NOMA) and full-duplex (FD) have recently emerged as a potential candidate for the fifth-generation and beyond (B5G) cellular networks with the target of increasing s...

To meet the coverage requirement in the 5G cellular network, small cells are conceived as an emerging technology to increase coverage and satisfy the traffic demand. However, modeling and analysis coverage is the most important step in cell planning to explore the performance of system. Furthermore, with a large deployment of small cells in a Heterogeneous Network (Hetnet), the cross-tier interference management is a complex problem that needs to be studied. The main contribution of this paper is to analyze the downlink coverage performance for 5G Hetnet where the infrastructure is composed of Macro cell and Small cell. We model the received Signal to Interference plus Noise Ratio SINR at the user and we derive coverage probability according to Stochastic Geometry under different cognitive interference with and without coordination. Simulation result are provided to validate the proposed model.

In this paper, we are going to joint design of the continuous/discrete phase transmit waveform and receive space-time filter to maximize the Signal to Interference plus Noise Ratio (SINR) in collocated Multiple Input Multiple Output (MIMO) radar systems. The resulting problem is non-convex, so we devise an iterative algorithm based on alternating optimization that applies to two different sub-problems, which monotonically enhances the objective function. In each sub-problem, we benefit from an efficient procedure based on the coordinate descent (CD) framework, and iteratively tackle the non-convex constrained optimization problem.Also, Fast Fourier Transform (FFT) is used to reduce the computational complexity in designing set of discrete phase sequences. Numerical examples illustrate superior performance of the proposed algorithm in comparison with the state-of-the-art counterparts.

Symbiotic radio (SR) is a new paradigm of spectrum sharing that allows passive Internet-of-Things (IoT) device to transmit messages over the ambient RF signal. Considering that the reader needs to collect information from multiple IoT devices in practice, and the traditional multiple access schemes that require coordination among users are unaffordable for the passive devices, we propose a random code assisted multiple access scheme, in which each backscatter device (BD) independently chooses a random code to spread its signal and the incident signal is backscattered. To avoid the need of the instantaneous information, including the specific codes and the instantaneous channel state information of the backscatter links, and to optimize the transmit power and the BD reflection coefficients, we derive the asymptotic BD signal to interference plus noise ratio (SINR) by employing the large-dimension random matrix theory. We formulate the joint optimization problem to maximize the minimum asymptotic SINR among BDs with guaranteeing the primary target rate. By investigating the property of the problem, the original problem is transformed and solved by the proposed iterative-based algorithm. Extensive simulations are executed to verify our theoretical analysis and demonstrate the outperformance of the proposed algorithm.

Dynamic spectrum sharing and cognitive radio networks were proposed to enhance the Radio Frequency (RF) spectrum utilization. However, there are several challenges to realize them in real systems, such as sensing uncertainty causing issues to licensed users, business models for licensed service providers. Wireless virtualization is regarded as a technology that leverages service level agreements to sublease unused or underutilized RF spectrum that addresses aforementioned issues and helps to significantly enhance the utilization of the RF spectrum, offer improved coverage and capacity of networks, enhance network security and reduce energy consumption. With wireless virtualization, wireless networks' physical substrate is shared and reconfigured dynamically between virtual wireless networks through Mobile Virtual Network Operations (MVNOs). Wireless virtualization with dynamic configurable features of Wireless Infrastructure Providers (WIPs), virtualized wireless networks are vulnerable to a multitude of attacks, including jamming attacks and eavesdropping attacks. This paper investigates a means of defense through the employment of coalition game theory when jammers try to degrade the signal quality of legitimate users, and eavesdroppers aim to reduce secrecy rates. Specifically, we consider a virtual wireless network where MVNO users' job is to improve their Signal to Interference plus Noise Ratio (SINR) while the jammers target to collectively enhance their Jammer Received Signal Strength (JRSS), and an eavesdropper's goal is to reduce the overall secrecy rate. Numerical results have demonstrated that the proposed game strategies are effective (in terms of data rate, secrecy rate and latency) against such attackers compared to the traditional approaches.

Two distributed power allocation schemes are proposed to improve the energy efficiency and throughput in cellular networks subject to outage probability limits. The effects of path loss, log normal shadowing, and Rayleigh fading are considered. Upper and lower bounds on the outage probability are derived based on the normalized signal to interference plus noise ratio (SINR). A power allocation problem for throughput maximization is presented and solved using the Lagrangian method. As this problem has high computational complexity, it is split into two subproblems. Then, a power allocation problem is proposed to improve the energy efficiency. This problem is in convex fractional programming form, so a parametric transformation is used to convert it into a subtractive optimization problem which can be solved iteratively. Results are presented which show that the proposed algorithms provide better performance than existing power allocation schemes.

Based on cyclostationarity properties of Orthogonal Frequency Division Multiplexing with Offset Quadrature Amplitude Modulation (OFDM/OQAM) signal, a novel blind signal-to-interference-plus-noise ratio (SINR) estimation method for OFDM/OQAM system in the presence of Radio Frequency Interference (RFI) is proposed. By analyzing the power distribution of cyclic correlation of transmitted signal, RFI and AWGN, by appropriately selecting the time delay variable and cyclic frequency of the OFDM/OQAM signal, the signal and interference plus noise power could be efficiently isolated in the power domain, which formed the SINR estimation method. Computer simulation results verify that the performance of the proposed method is superior to that of second-order and fourth-order moments (M2M4) method and the complexity of the new method is also lower than that of M2M4 method. The proposed method can effectively estimate SINR under the fast-fading Rayleigh channel and is robust to the density of RFI signal.

Link scheduling has always been a fundamental problem in wireless networks for its direct impacts on the performance of wireless networks such as throughput capacity, transmission delay, lifetime, etc. Existing work is mainly established under graph-based models, which are not only impractical but also incorrect due to the essentially fading characteristics of signals. In this paper, we study the shortest link scheduling problem under two more realistic models, namely the signal to interference plus noise ratio (SINR) model and the Rayleigh fading model. We propose a centralized square-based scheduling algorithm (CSSA) with oblivious power control under the SINR model and prove its correctness under both the SINR model and the Rayleigh fading model. Furthermore, we extend CSSA and propose a distributed square-based scheduling algorithm (DSSA). Note that DSSA adopts CSMA/CA so that a wireless node can compete for the wireless channel before starting its communication. We also show theoretical analysis and conduct extensive simulations to exhibit the correctness and efficiency of our algorithms.

In this article, power allocation in cellular networks considering Nakagami- ${m}$ fading is proposed. The objective is to optimize the network energy efficiency and throughput subject to user outage probability constraints. The moment generating function (MGF) is used to derive the exact outage probability over Nakagami- ${m}$ fading channels. Further, tight upper and lower bounds on the outage probability are derived using the Weierstrass, Bernoulli and exponential inequalities. These bounds are used to characterize the relationship between outage probability and normalized signal to interference plus noise ratio (SINR) in Nakagami- ${m}$ fading. Power allocation algorithms for throughput maximization and energy efficiency are proposed. The throughput maximization problem has a logarithmic form so a differential method is used to solve this problem. The proposed energy efficiency problem has a nonconvex fractional program form so a parametric transformation is used to convert it to a subtractive optimization problem which can be solved iteratively. Simulation results are presented which show that the proposed schemes provide better performance than existing methods in terms of power consumption, throughput, energy efficiency and outage probability.

Augmented vehicular reality (AVR) is one of the key technologies to realize intelligent transportation in the future, which can significantly improve traffic safety and transportation efficiency of autonomous driving. However, available computation and spectrum resources of vehicles are not well utilized to meet the requirements of cooperative perception of autonomous vehicles. In order to meet the needs of sharing sensing data to cooperatively perceive the surrounding environment, executing delay-sensitive and computationally intensive tasks of autonomous driving applications, we propose computation offloading and resource allocation optimization for AVR (CoroAVR) algorithm with hybrid sensing data fusion of cooperative perception to process the real-time data in fog-edge computing for maximizing system throughput and spectrum utilization on the premise of ensuring the quality of task completion. First, the minimum signal to interference plus noise ratio needed to complete the task is obtained with the given maximum computing resources. Second, the optimal power allocation is carried out by using convex optimization theory, and the throughput gain is calculated, and the offloading decision is made by comparing the throughput gain. Finally, the channel allocation problem is solved by using the maximum matching algorithm of the bipartite graph, and the computation resource allocation is studied. The simulation results show that the performance of the proposed algorithm is better than that of the contrast algorithm.

In this paper, we develop a physical/medium-access-control cross layer design to improve system throughput with the consideration of fairness for IEEE 802.11 WLAN. From PHY layer perspective, when an access collision occurs, the access point can still decode the corresponding data successfully if the received signal to interference plus noise ratio is larger than the threshold. This phenomenon is referred to as the capture effect. To improve system throughput, this work proposes a Differential Reception-Power Power Control scheme to take advantage of the capture effect. However, the proposed power control scheme cannot provide a fair transmission environment even though it improves the system throughput. To resolve this problem, this work proposes two methods: the adjustment of contention window size and the modification of probability mass function for the selection of the backoff value. The simulation results demonstrate that the proposed schemes can not only remarkably improve system throughput, but also provide a fair transmission environment.

Interference alignment (IA) is one of the most promising techniques to eliminate interference in multiuser multiantenna systems. Simultaneous wireless information and power transfer (SWIPT) technology is applied to harvest energy from radio frequency signals in green communications, while interference signal is assumed to be utilized as energy supply source in SWIPT. In this paper, an enhanced maximum signal to interference plus noise ratio (MAX-SINR) strategy with interference leakage power constraint is proposed, which realizes a tradeoff between information transmission and interference leakage in multiuser multiantenna SWIPT systems. Optimization problem is formulated and a three-step optimization solution is proposed. Finally, the problem can be transformed to a standard semi-definite programming (SDP) to obtain optimal power splitting factor and system sum-rate (spectral efficiency). Simulation results are presented to show that system sum-rate performance is enhanced with the increasing of receiving antenna numbers. The tradeoff between sum-rate and the harvested energy can be realized via the proposed IA strategy.

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling; it derives the optimum phase-only STAP, namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the $N$ -torus of phase-only complex vectors, where $N$ is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phase-only STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.

This paper proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order $N M^2$ is achieved, where $N$ is the antenna number and $M$ is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to $M$, while becomes proportional to $M^2$ as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance., Comment: Accepted to appear in IEEEE Transactions on Communications

A symbol-asynchronous uplink non-orthogonal multiple access (NOMA) model is considered, where K users transmit codewords to a common receiver according to their local clocks. As the complicated user location and the dynamic transmission environment, the exact time offsets of users are not always available at the base station (BS). To ensure the efficiency of the system, linear minimum mean square error algorithm and partial message passing (P-MP) algorithm are designed based on iterative detection. The signal to interference plus noise ratio is also applied to analysis the effect of estimated time offsets on the P-MP algorithm. Then simulation results show that when the variance of estimation error is 0.1 , the symbol-asynchronous NOMA systems with imperfect time offsets can achieve the same or even better bit error rate performance than the NOMA system.

This study addresses an observer-based model predictive control (MPC) algorithm for a networked control system (NCS) under denial of service (DoS) attacks and actuator saturation via an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. With few studies undertaking the cyber security problems in the research of MPC algorithm, this study considers the data transmission problem when DoS attacks occur. Under DoS attacks, signals in the communication networks will be interfered. The probability of error-free packet reception depends on signal-to-interference-plus-noise ratio of the wireless transmission networks. In order to reduce the on-line computation, an off-line fuzzy observer is devised to estimate the system states. Meanwhile, an on-line MPC algorithm is proposed to minimise the performance objective function and obtain the secure model predictive controller gain. Besides, the recursive feasibility is ensured by refreshing the bound of estimation error. Finally, illustrative examples certificate the effectiveness of the presented method.

Heterogeneous networks are used to meet both capacity and coverage demands in a cellular network. As the frequencies are reused due to the limited spectrum availability, there exists co-channel interference (CCI) between the high power and low power nodes in such networks apart from the CCI between high power nodes. Inter cell interference coordination (ICIC) is a big challenge towards heterogeneous network deployments since users located at the cell range expansion (CRE) area are more vulnerable to stronger interference caused by the signals from the high power nodes. To enhance capacity, achieve good user experience and improve spectral efficiency interference management techniques like fractional frequency reuse (FFR) with enhanced scheduling strategies and ICIC using expectation maximization algorithm are proposed to mitigate the effects of the CCI. The resource allocation in FFR is done for the users in the cell center (full reuse) and CRE (partial reuse) regions based on the optimum post processing signal to interference plus noise ratio threshold with different schedulers in the two regions. It is shown that a particular combination shows the best throughput performance for the proposed enhanced FFR scheme.

Discriminatory channel estimation (DCE) based on two-way training can effectively enhance physical layer security in multiple-input multiple-output (MIMO) systems. In this paper, considering an active eavesdropping scenario where the eavesdropper (Eve) can adaptively switch between passive eavesdropping, and active jamming modes, we propose a new DCE-based secure transmission scheme with cooperative jamming, and artificial noise (AN) insertion in massive MIMO decode-and-forward (DF) relay systems. To characterize the performance of the proposed scheme, firstly, the closed-form expressions for normalized mean squared error (NMSE) of legitimate channels, and wiretap channels are derived. Then, according to the NMSE obtained during channel estimation phase, we design different secure transmission schemes, and derive the approximative expressions of average security rate with respect to the behavior of active Eve, including malicious jamming, and passive eavesdropping, respectively. Finally, the power allocation at source, and relay is jointly optimized to minimize the average signal to interference plus noise ratio (SINR) at the Eve while ensuring that of the legitimate receivers to be large than a threshold for the quality-of-service (QoS) requirement. Numerical results show that the proposed DCE-based secure transmission scheme can effectively discriminate the CSI between legitimate receivers, and Eve even in the presence of an active Eve, and therefore achieve a better security performance than that without DCE.

Simultaneous wireless information and power transfer (SWIPT) is a promising solution for future wireless networks as it provides convenient and perpetual energy supplies to wireless users. This study proposes three new transceiver design schemes in two-way relay networks with SWIPT. Firstly, a transmit power minimisation solution is proposed with an aim to minimise the transmit power while meeting an energy harvesting requirement. Then, a harvested energy maximisation solution that has the same complexity as the first solution but provides a much better sum harvested energy is proposed. Finally, a signal-to-interference-plus-noise ratio maximisation solution is developed, which gives the best sum-rate performance for the two-way relay networks while ensuring a desired level of energy harvesting. Furthermore, the complexity of the three new transceiver solutions is discussed. Simulation results have demonstrated the effectiveness of the proposed transceiver design schemes in two-way relay networks with SWIPT.

This paper derives the outage and packet error probabilities of Non Orthogonal Multiple Access (NOMA) systems. In the first time slot, the Base Station transmits a combination of two symbols $$s_w$$ and $$s_s$$ dedicated for weak and strong users. This signal is received by the two users and a relay. In the second time slot, the relay amplifies the received signal to the two users. Both users use the signal with the highest Signal to Interference plus Noise Ratio among direct and relayed signals. The weak user detects only its signal. Strong user first detects symbol $$s_w$$ of weak user. After removing the contribution of weak user, strong user detects its own symbol $$s_s$$ . In this article, expressions for outage probability, Packet Error Probability and the throughput of cooperative NOMA are derived. We also optimize the power allocated to weak and strong users to maximize the system throughput.

In this paper, power allocation in cellular networks is proposed based on the outage probability and normalized signal to interference plus noise ratio (SINR). Upper and lower bounds on the outage probability are determined using the normalized SINR considering path loss, shadowing, and fading. The problems of minimizing the user power subject to outage probability and target SINR constraints are then considered as power allocation problems. These problems are solved using Perron-Frobenius theory and geometric programming (GP). The objectives are to efficiently provide users with flexible date rates and reduce the outage probability and user transmit power. Typically, only the path loss is considered in determining the outage probability whereas path loss, multipath fading and lognormal shadowing are considered in this paper along with the interference from other users. Results are presented which show that the proposed power allocation schemes provide better performance than the target SINR tracking power control (TPC), opportunistic power control (OPC), and temporary removal and feasibility check power control (DFC) algorithms.