Low Signal To Interference Plus Noise Ratio Research Articles

Low complexity detection algorithms based on ADMIN for massive MIMO

This paper proposes the alternating direction method of multipliers-based infinity-norm (ADMIN) with threshold (ADMIN-T) and with percentage (ADMIN-P) detection algorithms, which make full use of the distribution of the signal to interference plus noise ratio (SINR) for an uplink massive MIMO system. The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm, in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity. The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function (CDF) of SINR and a percentage, respectively. The symbols in higher SINR part are detected by MMSE. The interference of these symbols is then cancelled by successive interference cancellation (SIC). Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN. The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms. In addition, the computational complexity of ADMIN-T and ADMIN-P are significantly reduced. Furthermore, in the case of same number of transceiver antennas, the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

Secrecy Rate Maximization in THz-Aided Heterogeneous Networks: A Deep Reinforcement Learning Approach

Densely deployed femtocells in heterogeneous networks (HetNets) improve the quality-of-service (QoS) and quality-of-experience (QoE) for both next-generation networks and end users, respectively. However, users near the femtocell edge, i.e., femto edge users (FEUs), which are far away from the Femto base station (FBS) may receive a low signal-to-interference-plus-noise ratio (SINR). Moreover, due to the distributed and dynamic nature of HetNets, femtocells may not resist various eavesdropping attacks from different attackers. Thus, it is essential to ensure the wireless channel's secrecy through which FEUs communicate with FBS since they may face eavesdropping attacks and may have low SINR and achievable data rates. Aiming to enhance the secrecy rate of femtocells, in this article, we formulate a joint optimization problem of beamforming vectors, artificial noise (AN), and power control for terahertz (THz)-enabled femtocells. Addressing this non-convex optimization problem is challenging due to the system's complexity and dynamic nature of FEUs. Thus, we translate the optimization problem into a multi-agent reinforcement learning (MARL) problem using the Markov decision process (MDP). Then, to solve the MDP with continuous action space, two deep reinforcement learning (DRL) based schemes, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Deterministic Policy Gradient-based Secrecy Maximization (DDPG-SM)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Asynchronous-Advantage-Actor-Critic based Secrecy Maximization (A3C-SM)</i> are proposed to maximize the secrecy rate of femtocells. The secrecy rate performance of the proposed schemes is compared and analyzed with Advantage-Actor-Critic (A2C) and state-of-the-art Perfect Secrecy Rate Maximization (PSRM) schemes. Simulation results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> schemes achieve 37.73% and 22.64% better average secrecy rate in comparison to the PSRM scheme. Also, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> scheme outperforms all other <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> , A2C, and PSRM schemes in terms of average secrecy rate, received SINR, and energy-efficiency of femtocells.

A Simple and Practical Underlay Scheme for Short-Range Secondary Communication

The unprecedented growth in wireless Internet-of-Things and WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection– which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. This paper proposes a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary’s interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Experiments using a software radio testbed reveal that, for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible for signal to interference plus noise ratio (SINR) in the range of −20 to −40 dB. The approach works with unknown time-varying channels, digital or analog modulation, it is immune to carrier frequency offset, and, as a side-benefit, it provides means for accurate synchronization of the secondary user even at very low SINR.

FMCW-MIMO Radar-Based Pedestrian Trajectory Tracking Under Low- Observable Environments

Pedestrian trajectory perception is always a challenging task for autonomous urban driving since the weak echoes from pedestrians may be masked by strong background clutters, for example, road infrastructures and other vehicles. As a consequence, this article presents a frequency-modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar-based trajectory tracking method to realize continuous pedestrian detection and tracking under low-observable environments. More specifically, the keystone transform-constant false alarm ratio (KT-CFAR) is first proposed to maximize the output signal-to-interference plus noise ratio (SINR) depending on the time–space correlations of a sequence of consecutive echoes, for the sake of improving the pedestrian detection probability under low-observable conditions. Then, the multiple signal classification (MUSIC) algorithm is employed to provide a high azimuth resolution to improve the multitarget resolving ability. After that, a pedestrian trajectory tracking framework is constructed by means of Bayesian theory and sequential Monte Carlo (SMC) sampling. Finally, simulation and experimental results show that the proposed method can obtain high-precision pedestrian trajectory tracking under low SINR conditions compared with conventional Kalman filter (KF)-based tracking methods.

An Anti-Jamming Multiple Access Channel Game Using Latency as Metric

We consider a multi-access channel problem, where several selfish users communicate with a base station (BS) in the presence of a jammer. The jammer wishes to degrade the communication by producing interference. The communication metric is the inverse signal-to-interference-plus-noise ratio (SINR), which, for low SINR, reflects communication delay. The problem is formulated in Nash and Stackelberg game frameworks. The equilibrium is derived in closed form for both models. The usability of the latency metric even in the presence of a jammer is proven by showing that the equilibrium is unique.

Prior-Guided Deep Interference Mitigation for FMCW Radars

In this paper, the interference mitigation problem is tackled as a regression problem. A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. Considering the complex-valued nature of radar signals, complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. The average SINR of interfered signals has been improved from -9.13 dB to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Seamless connectivity with 5G enabled unmanned aerial vehicles base stations using machine programming approach

AbstractDeployment of a small unmanned aerial vehicle (UAV) mounted 5G base station is a promising solution for providing seamless network connectivity to users in a modern, data‐centric thrust areas. The key challenge is to find the location, the height and the optimum number of mounts. A machine programming based approach is proposed here for optimal placement of UAV‐mounted base station. The location of the deployment is determined using three clustering algorithms such as K‐means, K‐medoids, and fuzzy cluster means. Different sets of UAV‐mounted base stations have been deployed with variable user density at different heights. The impact on the network performance has been quantified through measurements of received power, signal to interference plus noise ratio (SINR), and path loss per active user equipment (UEs). To gain further insights, a scenario where UEs are connected only to the terrestrial base station, that is, the network is devoid of any sort of UAV mounted base station is evaluated. Numerical computations reaffirm that the proposed technique reduces the average path loss of the active UEs. Moreover, the use of height‐mounted base station also alleviates the issues arising due to low SINR values. The said technique shows immense potential in terms of seamless connectivity to end users in events of emergency and remote deployment scenarios, where ground‐based base station is not possible. The big transition of on‐ demand connectivity for 5G networks shall be benefitted from purpose‐built UAV infrastructure with specific locations or areas in mind.

An efficient adaptive modulation technique over realistic wireless communication channels based on distance and SINR

Abstract A growing trend has been observed in recent research in wireless communication systems. However, several limitations still exist, such as packet loss, limited bandwidth and inefficient use of available bandwidth that needs further investigation and research. In light of the above limitations, this paper uses adaptive modulation under various parameters, such as signal to interference plus noise ratio (SINR), and communication channel’s distances. The primary goal is to minimize bit error rate (BER), improve throughput and utilize the available bandwidth efficiently. Additionally, the impact of Additive White Gaussian Noise (AWGN), Rayleigh and Rician fading channels on the performance of various modulation schemes are also studied. The simulation results demonstrate that our proposed technique optimally improves the BER and spectral efficiency in the long-range communication as compared to the fixed modulation schemes under the co-channel interference of surrounding base stations. The results indicate that the performance of fixed modulation schemes is suitable only either at high SINR and low distance or at low SINR and high distance values. Moreover, on the other hand, its performance was suboptimal in the entire wireless communication channel due to high distortion and attenuation. Lastly, we also noted that BER performance in the AWGN channel is better than Rayleigh and Rician channels with Rayleigh channel exhibiting poor performance than the Rician channel.

Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization

Unmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative. This paper presents a novel variable height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as BSs. We propose a multi-tier variable height UAV-based network deployment and compare its performance with the state-of-the-art equal height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. The results show that our proposed method is more energy-efficient in a multi-cell UAV network than the most widely used height optimization method in the literature. In UAV networks, users at the cell edges can receive very poor signal-to-interference-plus-noise ratio (SINR) levels due to interfering UAVs. To cope with this problem, we adopt a fractional frequency reuse (FFR) scheme to compensate low SINR levels. We optimize the SINR threshold corresponding to each cell to maximize their spectral efficiency (SE), thereby improving the network’s area spectral efficiency (ASE). The numerical results show that the proposed deployments provide significant gains in coverage density, SINR coverage probability, rate coverage, and ASE compared to equal height benchmark scheme. As the number of UAVs increases, the number of tiers need to increase to preserve the rate coverage of the network. Moreover, the performance of the proposed variable height model is expected to converge to that of equal height cellular design for a large number of UAVs.

On Throughput Improvement Using Immediate Re-Transmission in Grant-Free Random Access With Massive MIMO

To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has a low SINR although it does not experience preamble collision to exploit re-transmission diversity (RTD) gain. To see the performance of the proposed approach, we perform throughput analysis with certain approximations and assumption. Since the proposed approach can be unstable due to immediate re-transmissions, conditions for stable systems are also studied. Simulations are carried out and it is shown that analysis results reasonably match simulation results.

Coverage analysis of cell‐edge users in heterogeneous wireless networks using Stienen's model and RFA scheme

SummaryIn heterogeneous wireless networks, signal‐to‐interference‐plus‐noise ratio (SINR) suffers degradation due to strong interference received by users from offloaded macro base station (mBS). Similarly, cell‐edge users experience low SINR due to their distant locations. Moreover, small base stations (sBSs) located in the vicinity of mBS experience reduced coverage due to the high transmit power of mBS. To overcome these limitations, we use Stienen's model as a base station deployment strategy to improve network performance gain. More specifically, we use reverse frequency allocation (RFA) as an interference management scheme together with Stienen's model to significantly improve SINR, enhance edge user coverage, and avoid sBS deployment near the mBS. In the proposed set‐up, the available coverage region is divided into two noncontiguous regions, ie, center region and outer region. Furthermore, mBSs are uniformly distributed throughout the coverage region using independent Poisson point processes, while sBSs are deployed only in outer region using Poisson hole process (PHP). Closed‐form expressions for coverage probabilities are characterized for the proposed model. Numerical results show that the proposed scheme yields improved SINR with enhanced edge user coverage and requires fewer number of sBSs.

Energy Harvesting Communications Under Explicit and Implicit Temperature Constraints

With a motivation to understand the effects of temperature sensitivity on wireless data transmission performance, we consider an energy harvesting communication system, where the temperature dynamics are governed by the transmission power policy. Different from the previous work, we consider a discrete time system where transmission power is kept constant in each slot. We consider two models that capture different effects of temperature. In the first model, the temperature is constrained to be below a critical temperature at all time instants; we coin this the explicit temperature constrained model . We investigate throughput optimal power allocation for multiple energy arrivals under general, as well as temperature and energy limited regimes. We show that the optimal power allocation for the temperature limited case is monotone decreasing. In the second model, we consider the effect of the temperature on the channel quality via its influence on additive noise power; we coin this the implicit temperature constrained model . In this model, the change in the variance of the additive noise due to previous transmissions is non-negligible. In particular, transmitted signals contribute as interference for all subsequent slots and thus affect the signal to interference plus noise ratio (SINR). In this case, we investigate throughput optimal power allocation under general, as well as low and high SINR regimes. We show in the low SINR regime that the optimal allocation dictates the transmitter to save its harvested energy till the last slot. In the high SINR regime, we show that the optimal power sequence is monotone increasing. Finally, we consider the case in which implicit and explicit temperature constraints are simultaneously active and we show under certain conditions that the optimal power sequence is monotone decreasing.

Wireless Secure Communication With Beamforming and Jamming in Time-Varying Wiretap Channels

For physical layer security with multiple antennas over wireless channels, we consider an artificial noise-aided secure beamforming system. A transmitter can send the confidential information to the legitimate user more securely without eavesdropping when an artificial jamming signal interfering eavesdroppers is transmitted with the confidential information signal. The transmitter splits its transmit power for both the information and jamming signals with a power splitting factor. Under such a system model, we investigate the impacts on secrecy performance of a power splitting factor, the numbers of antennas and eavesdroppers, and noise variance at the legitimate receivers and eavesdroppers by analyzing the expected secrecy rate. The optimal power splitting factor and limiting secrecy rate with large number of antennas are also derived. Moreover, we examine the expected secrecy rate loss caused by channel variation over time. It is shown that the secrecy rate loss is independent of system parameters like a power splitting factor, the numbers of antennas and eavesdroppers in a high signal to interference plus noise ratio (SINR) region. In a low SINR region, on the other hand, the secrecy rate loss changes with system parameters. Simulation results verify these observations on ergodic secrecy rate and its loss due to time-varying channels.

Coverage probability analysis of three uplink power control schemes: Stochastic geometry approach

In cellular networks, each mobile station adjusts its power level under control of its base station, i.e., through uplink transmit power control, which is essential to reach desired signal-to-interference-plus-noise ratio (SINR) at the base station and to limit inter-cell interference. The optimal levels of transmit power in a network depend on path loss, shadowing, and multipath fading, as well as the network configuration. However, since path loss is distance dependent and the cell association distances are correlated due to the cell association policies, the performance analysis of the uplink transmit power control is very complicated. Consequently, the impact of a specific power control algorithm on network performance is hard to quantify. In this paper, we analyze three uplink transmit power control schemes. We assume the standard power-law path loss and composite Rayleigh-lognormal fading. Using stochastic geometry tools, we derive the cumulative distribution function and the probability density function of the uplink transmit power and the resulting network coverage probability. It is shown that the coverage is highly dependent on the severity of shadowing, the power control scheme, and its parameters, but invariant of the density of deployment of base stations when the shadowing is mild and power control is fractional. At low SINRs, compensation of both path loss and shadowing improves the coverage. However, at high SINRs, compensating for path loss only improves coverage. Increase in the severity of shadowing significantly reduces the coverage.

Rate-Optimal Relay Selection for Average Interference-Constrained Underlay CR

Cooperative relaying combined with selection exploits spatial diversity to improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. While a relay improves the signal-to-interference-plus-noise ratio (SINR) of the secondary network, it requires two hops and also generates interference to the primary network. We present a novel, optimal relay selection rule that maximizes the fading-averaged transmission rate of an average interference-constrained underlay secondary network. It differs from the several ad hoc incremental relaying schemes proposed in the literature, while requiring a feedback overhead that is comparable to them. We then analyze the average rate of the optimal rule. We also present insightful high and low SINR asymptotic analyses, which bring out the extent to which the use of the relays improves the average rate as a function of the system parameters. Our numerical results show that the proposed rule outperforms several known relay selection schemes for CR, and also characterize the regimes in which some of these schemes are near-optimal.

Weak harmonic signal detection method from strong chaotic interference based on convex optimization

Noticing that the second-order matrix of chaotic signal is stationary, we propose a method for detecting weak harmonic signal from strong chaotic interference based on convex optimization. After a data matrix in frequency domain is composed by combining reference cells and the detection cell, we detect weak harmonic signals by searching each frequency channel based on an optimization filter. Then, the signal detection problem is boiled down to an optimization problem. Further, we design an optimization filter based on second-order cone programs. The filter can maintain the gain of signal from current frequency channel and suppress signals from other frequency channels. The harmonic signal can be detected by the output signal-to-interference-plus-noise ratio (SINR) of each frequency channel. Compared with the neural network methods, the proposed method has following advantages: (1) It can detect weak harmonic signal under lower SINR and (2) it is robust against white Gaussian noise.

3D Massive MIMO Systems: Modeling and Performance Analysis

Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. Recently, the trend is to enhance system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the characterization of 3D channels. We present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles. Based on this model, we provide analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime. The result is extended to systems with finite receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of MI distribution is derived for the large number of transmit antennas and paths regime. We corroborate our analysis with simulations that study the performance gains realizable through meticulous selection of the transmit antenna downtilt angles, confirming the potential of elevation beamforming to enhance system performance. The results are directly applicable to the analysis of 5G 3D-Massive MIMO-systems.

Harmonic signal detection method from strong chaotic background based on optimal filter

It is of great significance to study the weak harmonic signal detection from strong chaotic background. Current detection methods mainly use the chaotic phase space reconstruction method based on Takens theory, among which the neural network method has attracted the most attention. However, these methods require high signal-to-interference-plus-noise ratio (SINR) and are sensitive to Gaussian white noise, etc. Noticing the fact that the second-order statistical properties of chaotic signals are stationary, we propose a harmonic signal detection method from strong chaotic background based on optimal filter. We first construct a data matrix, whose rows are the detection signal and reference signals. The reference signals only contain chaotic interference. Then we calculate the one-dimensional fast Fourier transformation of the data matrix to make each column of the matrix form a frequency channel. The harmonic signal can be detected by searching each frequency channel in the frequency domain, thus the signal detection problem is converted into an optimization problem. Further, we use the optimization theory to design a filter such that it can maintain the gain of the signal from the current frequency channel and suppress signals from other frequency channels as far as possible. Finally, the harmonic signal can be obtained by calculating the output SINR of each frequency channel. In order to reduce the calculation, we can further design a local region optimal filter. We choose part of frequency channels to constitute a local area, thus the dimension of the chaotic interference covariance matrix is greatly reduced. Theoretically speaking, the more the number of auxiliary frequency channels, the better the detection results are. However, in the practical application, choosing two channels on the left and right side of current channel each can obtain a very good detection effect. After obtaining the chaotic interference covariance matrix, we can further achieve the output SINR of each frequency channel. Compared with the traditional methods, the proposed method has the following advantages: 1) it can detect a weak harmonic signal under lower SINR; 2) it can detect a greater range of signal amplitude; 3) it is robust against white Gaussian noise. The simulation results with taking Lorenz system as the strong chaotic background show that the proposed method still has a very good detection effect when SINR =-81.03 dB, and the stronger the harmonic signal, the better the detection effect is, while the neural network method can work under the condition of SINR higher than -67.03 dB; the proposed method still can correctly detect the target signal in the case that the SNR is as low as -20 dB, but the neural network method has a poor detection effect under the same condition.

Relaying Robust Beamforming for Device-to-Device Communication With Channel Uncertainty

In this letter, we study robust beamforming design in the scenario that user equipments (UEs) relay the messages from a base station (BS) to a destination UE out of the cellular networks, which is considered as a high-priority device-to-device (D2D) scenario in the 3rd Generation Partnership Project (3GPP) Long-Term-Evolution (LTE) standardization. We assume that another D2D UE, which receives data from another D2D transmission group, coexists in the network, and it is interfered by D2D relay UEs. Moreover, the channel links between D2D relay UEs and the interfered UE are supposed to suffer from Gaussian errors due to low accuracy of channel estimation. Our optimization objective is to minimize the total transmission power at the BS and D2D relay UEs, while satisfying the signal-to-interference-plus-noise ratio (SINR) requirement of the destination D2D receiver and keeping the probability of low SINR event of the interfered D2D receiver smaller than a predefined threshold. Finally, we present the joint BS and D2D relay UE beamforming design, the effectiveness of which is verified by computer simulations.

Efficacy of coverage radius‐based power control scheme for interference mitigation in femtocells

A novel coverage radius-based downlink power control scheme to mitigate interference in densely deployed femtocells is presented. A femtocell access point (FAP) self-update algorithm is implemented, which determines the coverage radius of the femtocell with respect to its farthest served femtocell user equipment (FUE). Based on varying coverage radii, a max/min function is used to adjust the downlink transmit power value of a FAP. System-level simulations are performed to compare the performance of the presented scheme with the existing fixed coverage radius schemes. Even though the proposed scheme results in better cross-tier signal to interference plus noise ratio (SINR) values, due to a low co-tier SINR it is found that the efficacy of adaptive power control schemes based on the pilot power of a FAP is less significant if FUEs are located close to the neighbouring FAPs in densely deployed urban femtocells.