Non-line-of-sight Channel Research Articles

A Design of NLOS Communication Scheme Based on SC-FDE with Cyclic Suffix for UAV Payload Communication

Non-line-of-sight (NLOS) communication with severe loss always leads to performance degradation in unmanned aerial vehicle (UAV) payload communication. In this paper, a UAV NLOS communication scheme based on single-carrier frequency domain equalization with cyclic prefix and cyclic suffix (CP/CS-SC-FDE) is designed. First, the reasons behind the generation of later intersymbol interference (LISI) in UAV NLOS communication are investigated. Then, the frame structure of conventional single-carrier frequency domain equalization with cyclic prefix (CP-SC-FDE) is improved, and the UAV NLOS communication frame structure based on cyclic prefix (CP) and cyclic suffix (CS) is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this UAV communication scheme can eliminate intersymbol interference (ISI) in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel and other standard NLOS channels. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization.

NLOS visible light positioning and communication based on LoRa modulation

With the rapid development of the Internet of Things, location-based services are becoming increasingly important, especially in indoor environments. Visible light positioning (VLP) has garnered widespread attention due to its high accuracy, low cost, and immunity to the radio frequency electromagnetic interference. However, traditional VLP relies on line-of-sight paths, making it impractical in complex and dynamic indoor environments. In this paper, we propose a non-line-of-sight (NLOS) visible light positioning and communication system based on LoRa modulation to address the issue of link obstruction. LoRa is employed to recover position information transmitted from light emitting diodes (LEDs) over NLOS links, enabling reliable communication under weak lighting conditions. We establish a geometric relationship between the LED and the virtual image of the photodetector (PD). Leveraging the NLOS channel model, we derive the relationship between the received signal strength and the distance from LEDs to the virtual image of the PD. Through this relationship, the trilateration method is applied to calculate the position of the receiver. Based on experimental results, the proposed system achieves 90th percentile localization accuracy of less than 25 and 35 cm for the PD heights of 60 and 80 cm, respectively.

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mm Wave Networks

We present DeepIA, a deep neural network (DNN) framework for fast and reliable initial access (IA) for artificial intelligence (AI)-driven 6G millimeter wave (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA&#x0027;s beam prediction accuracy. We show that DeepIA&#x0027;s accuracy saturates with the number of beams used, and also depends on the particular choice of the beams used. The choice of beams that are selected is consequential and improves accuracy by upto upto <inline-formula><tex-math notation="LaTeX">$35\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$70\%$</tex-math></inline-formula> in NLoS and LoS channels. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> accuracy in both LoS and NLoS conditions. We introduce interference into our models to understand impact on performance. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the baseline approach.

Analysis of 5.8 GHz Network for Line of Sight (LOS) and Non-Line of Sight (NLOS) in Suburban Environment

This paper presents the findings of radio wave characterization based on the measurement data at 5.8 GHz. The measurement data were collected by a testbed channel, which links with the following scenarios: a single tree, a row of trees, a row of trees and a road, a row of trees, a road, and a building. These experiments were conducted at University Teknologi Malaysia (UTM) Skudai, Johor to represent the suburban environment. The links consist of pairs of transmitting and receiving antennas that deploy the path of a line of sight (LOS) and non-line of sight (NLOS) radio propagation wave networks. Based on the measurement data analysis, the general issue concerning the statistical probability distribution and the characteristics of LOS and NLOS are examined and discussed. Note that 5.8 GHz technology can be used in both LOS and NLOS scenarios, but its performance varies based on the presence of obstacles and signal propagation characteristics. Other prominent experimental analysis methods, such as hypothesis testing and goodness of fit tests, are implemented to consolidate the findings. The analysis found that the empirical probability density function of LOS and NLOS channels follows Gaussian, Rayleigh, and Rician distribution. Predicting specific future technological developments, such as the availability of 5.8 GHz technology, is challenging because it depends on various factors, including research and development efforts, regulatory decisions, market demand, and technological advancements.

Analysis of 5.8 GHz Network for Line of Sight (LOS) and Non-Line of Sight (NLOS) in Suburban Environment

This paper presents the findings of radio wave characterization based on the measurement data at 5.8 GHz. The measurement data were collected by a testbed channel, which links with the following scenarios: a single tree, a row of trees, a row of trees and a road, a row of trees, a road, and a building. These experiments were conducted at University Teknologi Malaysia (UTM) Skudai, Johor to represent the suburban environment. The links consist of pairs of transmitting and receiving antennas that deploy the path of a line of sight (LOS) and non-line of sight (NLOS) radio propagation wave networks. Based on the measurement data analysis, the general issue concerning the statistical probability distribution and the characteristics of LOS and NLOS are examined and discussed. Note that 5.8 GHz technology can be used in both LOS and NLOS scenarios, but its performance varies based on the presence of obstacles and signal propagation characteristics. Other prominent experimental analysis methods, such as hypothesis testing and goodness of fit tests, are implemented to consolidate the findings. The analysis found that the empirical probability density function of LOS and NLOS channels follows Gaussian, Rayleigh, and Rician distribution. Predicting specific future technological developments, such as the availability of 5.8 GHz technology, is challenging because it depends on various factors, including research and development efforts, regulatory decisions, market demand, and technological advancements.

Statistical LOS/NLOS Classification for UWB Channels

Ultrawideband (UWB) technology has attracted a lot of attention for indoor and outdoor positioning systems due to its high accuracy and robustness in non-line-of-sight (NLOS) environments. However, UWB signals are affected by multipath propagation which causes errors in localization. To overcome this problem, researchers have proposed various techniques for NLOS identification and mitigation. One of the approaches is statistical LOS/NLOS classification, which uses statistical parameters of the received signal to distinguish between LOS and NLOS channels. In this paper, we formulated several techniques which can be used for effectively classifying Line of Sight (LOS) channel from a Non-Line of Sight (NLOS) channel. Various parameters obtained from Channel Impulse Response (CIR) like Skewness, Kurtosis, Root Mean Squared Delay Spread (RDS), Mean Excess Delay (MED), Energy, Energy Ratio and Mean of Covariance Matrix are used for channel classification. In addition to this, the Joint Probability Density Functions (PDFs) of various parameters are used to improve the accuracy of UWB LOS/NLOS channel classification. Two different criteria-Likelihood Ratio and Hypothesis Test are used for the identification of channel.

Scattering and Eavesdropping in Terahertz Wireless Link by Wavy Surfaces

Wireless communication at terahertz (THz) frequencies is viewed as one of the potentials for future 6G wireless systems. However, the path of secure data transmission by THz links is facing many complex challenges. Of that, scattering-induced eavesdropping threat remains one of the most critical but less explored. With the updating of carrier frequency into the THz range, the wavelength becomes comparable to the surface roughness of typical objects in our daily life. Surface scattering, which is negligible in current wireless networks, becomes serious and would lead to desperate signal eavesdropping. For a non-line-of-sight (NLOS) link achieved by surface reflection, how does the scattering affect link security at the physical layer? Such direct information is crucial for the inspection of security threats and proposing of possible solutions. Using metallic wavy surfaces, we characterize possibilities of signal eavesdropping caused by bistatic scattering, by several unmodulated channels at different frequencies. We also demonstrate a real-time data transmission for this eavesdropping configuration, with a data rate of 4 gigabits/s by 16 quadrature amplitude modulation. We observe that successful signal interception from an NLOS link happens, even when transceiver designs incorporating backscatter measures are considered. This is likely to be a common security threat for any THz communication systems in which NLOS channels are employed to achieve wider user coverage.

Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

Accurate Underwater Optical Wireless Communication Model With Both Line-of-Sight and Non-Line-of-Sight Channels

Underwater optical wireless communication (UOWC) systems have been widely studied to achieve high-speed wireless communications. To investigate and design UOWC systems, a mathematical model to characterize the accurate performance of UOWC systems is important. However, the previous mathematical models of UOWC systems have limitations, which mainly focused on the line-of-sight (LOS) link, and only considered the vertical incident background light or optical filters with constant transmittance for simplicity. To overcome these limitations, we establish an accurate UOWC system model, incorporating the previously overlooked impact of signal and sun lights incident angles and including both LOS and non-line-of-sight (NLOS) channels. Our proposed accurate UOWC system model is validated by Monte Carlo (MC) simulations. Results show that our proposed model can capture the changes of received powers of both signal and background lights with various transmitter and receiver alignments and orientations in both LOS and NLOS channels, which typically affect the signal-noise-ratio (SNR) and bit-error-rate (BER) performance of UOWC systems. Furthermore, we show that the incident angles of both signal and sun lights have significant impact on the system performance, and our proposed model can be used to optimize the PD rotation angle to improve the SNR performance. Therefore, the more accurate UOWC channel model established in this paper provides a fundamental framework for future UOWC system design and optimization.

Achievable Energy Efficiency in Massive MIMO: Impact of DAC Resolution and PAPR Reduction for Practical Network Topologies at mm-Waves

This letter explores key parameters of a massive MIMO system and their impact on energy efficiency at transmission. In particular, the effect of the digital to analog converter resolution and peak to average power ratio reduction techniques are addressed, in the context of practical user location distributions for both line and non line of sight channels. Results show interesting design trade-offs, and highlight the relevance of an accurate model for the user locations for the correct evaluation of the achievable performance.

Possibility of compromising the security of free space optics communications caused by scattering on fog particles.

The paper presents experimental verification of coexistence and entry into a free space optical link channel. A numerical model, which describes the properties of the LOS (Line of Sight) channel and NLOS (Non-Line of Sight) channel, was formulated and experimentally verified. Experimental work includes an outdoor fog experiment which confirms theoretical predictions. It has been shown that under certain optimal conditions (sufficient transmitting power, dense fog, optimal eavesdropper's receiver distance and optimal angle between the eavesdropper's receiver and the axis of the wireless optical link) unauthorized reception is possible.

Measurement Based Non-Line-of-Sight Vehicular Visible Light Communication Channel Characterization

Vehicular visible light communication (V-VLC) aims to provide secure complementary vehicle-to-everything-communications (V2X) to increase road safety and traffic efficiency. V-VLC provides directional transmissions, mainly enabling line-of-sight (LoS) communications. However, reflections due to nearby objects enable non-line-of-sight (NLoS) transmissions, extending the usage scenarios beyond LoS. In this paper, we propose wide-band measurement based NLoS channel characterization, and evaluate the performance of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) V-VLC scheme for NLoS channel. We propose a distance based NLoS V-VLC channel path loss model considering reflection surface characteristics and NLoS V-VLC channel impulse response (CIR) incorporating the temporal broadening effect due to vehicle reflections through weighted double gamma function. The proposed path loss model yields higher accuracy up to 14 dB when compared to single order reflection model whereas CIR model estimates the full width at half maximum up to 2 ns accuracy. We further demonstrate that the target bit-error-rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-3}$</tex-math></inline-formula> can be achieved up to 7.86 m, 9.79 m, and 17.62 m distances for black, orange and white vehicle reflection induced measured NLoS V-VLC channels for DCO-OFDM transmissions.

Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation

Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Signal processing techniques for channel estimation currently deployed in wireless devices do not scale well to this “high-dimensional" regime in terms of performance and pilot overhead. Meanwhile, training deep learning-based approaches for channel estimation requires large labeled datasets mapping pilot measurements to clean channel realizations, which can only be generated offline using simulated channels. In this paper, we develop a novel unsupervised over-the-air (OTA) algorithm that utilizes noisy received pilot measurements to train a deep generative model to output beamspace MIMO channel realizations. Our approach leverages Generative Adversarial Networks (GAN), while using a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations. We also present a federated implementation of the OTA algorithm that distributes the GAN training over multiple users and greatly reduces the user side computation. We then formulate channel estimation from a limited number of pilot measurements as an inverse problem and reconstruct the channel by optimizing the input vector of the trained generative model. Our proposed approach significantly outperforms Orthogonal Matching Pursuit on both LOS and NLOS channel models, and EM-GM-AMP – an Approximate Message Passing algorithm – on LOS channel models, while achieving comparable performance on NLOS channel models in terms of the normalized channel reconstruction error. More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.

CSI-based passive intrusion detection bound estimation in indoor NLoS scenario

Passive intrusion detection is important for many indoor safety applications. By utilizing widespread WiFi infrastructures, channel state information (CSI) based intrusion detection methods have attractive advantages such as low cost, non-line-of-sight (NLoS) support, and privacy protection. However, existing CSI-based methods lack in-depth and intensive analysis of intrusion detection bound. To the best of our knowledge, this is the first work that precisely characterizes and models CSI-based intrusion detection bound in typical indoor NLoS scenario. A bound is defined as an intruder’s farthest position from the transmitter during intrusion detection. To derive a model for the bound, we first derived an intrusion-disturbed NLoS channel model by analyzing the influence of human intrusion on the NLoS wireless channel. Subsequently, based on the channel model, we further derived an intrusion detection bound model. Based on the derived bound model, we proposed a practical system to estimate the real intrusion detection bound. Extensive experiments were conducted based on practical systems. The experimental and simulation results verified and demonstrated the effectiveness of the derived bound model. Our work not only reveals the fundamental performance limit of the basic intrusion detection method in an indoor NLoS scenario, but also provides a valuable reference for bound estimation for other fine-grained wireless sensing applications.

The Effect of Multipath Propagation on Performance Limit of mmWave MIMO-Based Position, Orientation and Channel Estimation

Millimeter-wave (mmWave) massive antenna array has shown great potential in gaining high-accuracy localization for user equipments (UEs). Yet, mmWave signals suffer from non-line-of-sight (NLOS) propagation and fast time-scale fading in practice, which will affect UE localization performance. It is non-trivial to reveal the impact of multipath propagation and fast time-scale fading on mmWave-based localization, and gain guidelines for harnessing NLOS propagation. In this paper, we aim to provide a unified framework for performance analysis of mmWave-based UE localization and channel estimation. Firstly, closed-form Cramer-Rao lower bounds for UE localization and channel estimation, respectively, are derived to shed lights on their performance limits. Secondly, the effect of system elements (e.g., channel correlation and fast time-scale fading) on UE localization performance is quantitatively analyzed. In addition, information contribution from NLOS channel is quantified to reveal the impact of multipath propagation on UE localization. Finally, we show that the NLOS propagation-caused localization error can be alleviated by using a NLOS model folding-based localization scheme <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which strikes a balance between computational complexity and localization performance.

Reconfigurable Intelligent Surface Assisted Multi-Carrier Wireless Systems for Doubly Selective High-Mobility Ricean Channels

Reconfigurable intelligent surfaces (RIS) constitute a revolutionary technique of beneficially reconfiguring the smart radio environment. However, despite the fact that wireless propagation is of time-varying nature, most of the existing RIS contributions focus on time-invariant scenarios for the following reasons. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Firstly</i> , it becomes impractical to instantaneously feed back the control signal based on the doubly selective non-line-of-sight (NLoS) fading scenario. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , channel estimation conceived for the high-mobility and high-dimensional RIS-assisted links has to take into account the spatial-domain (SD), time-domain (TD), and frequency-domain (FD) correlations imposed by the angle-of-arrival/departure (AoA/AoD), the Doppler and the orthogonal frequency-division multiplexing (OFDM) operations, respectively, where none of the existing solutions can be directly applied. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thirdly</i> , it is far from trivial to maximize the NLoS channel powers on all subcarriers by a common set of RIS reflecting coefficients. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fourthly</i> , in the face of double selectivity, it becomes inevitable to encounter either inter-symbol interference (ISI) or inter-channel interference (ICI) during the signal detection in the TD or in the FD, respectively. Against this background, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">firstly</i> , we focus our attention on line-of-sight (LoS) dominated unmanned aerial vehicle (UAV) scenarios. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Secondly</i> , we conceive new minimum mean squared error (MMSE) channel estimation methods for doubly selective fading, which perodically transmit pilot symbols embedded into the TD and FD over the SD in order to beneficially exploit the correlations in the three domains. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thirdly</i> , the RIS coefficients are optimized by a low-complexity algorithm based on the LoS representation of the end-to-end system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fourthly</i> , tailor-made interference cancallation techniques are devised for improving the signal detection both in the FD and in the TD. Our simulation results are examined in six frequency bands licensed in 5 G, which confirms that the employment of RIS is capable of achieving substantial performance improvements.

Time‐varying non‐line‐of‐sight (NLOS) satellite navigation channel modelling and simulation using multiple point‐scatterers

Recently, there has been an increasing interest in modelling and simulation of time-varying (TV) non-line-of-sight (NLOS) channels for global navigation satellite systems (GNSS) as an assisted means of resolving the GNSS urban canyon problem. In the above context, modelling and simulation of TV NLOS channels for GNSS have been investigated and studied due to their rapidly TV characteristics. However, conventional simulation approaches for TV multipath channels suffer from inconsistency and lack physical soundness, resulting in a non-smooth transition of the Doppler shift and low simulation output accuracy. The authors propose an improved satellite navigation channel modelling and simulation method for TV NLOS reception scenarios. The multiple-point-scatterer model framework is applied to the TV NLOS satellite navigation channel model. The major novelty of the proposed method is integrating the multiple-point-scatterer model with online planning of a kinematics trajectory to map changes in the continuous velocity to the Doppler shift, which can keep consistency and accuracy. As a side benefit of the authors' work, numerical integration based on the efficient composite trapezoidal rule is proposed to calculate the precise changes in signal path length, phase, and Doppler shift. The numerical simulation results show that the proposed modelling and simulation method can not only accurately simulate the TV characteristics of the channel multipath with the continuous phase and the Doppler shift, but also reproduce the Rayleigh fading statistics of the TV channel, thus effectively approximating the physical propagation model and generating the TV NLOS satellite navigation channel.

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Knowledge of channel state information (CSI) is fundamental to many functionalities for mobile communication systems. With the advance of machine learning (ML) and digital maps, i.e., digital twins, we have a big opportunity to learn the propagation environment and design novel methods to derive and report CSI. In this work, we propose to combine untrained neural networks (UNNs) and conditional generative adversarial networks (cGANs) for MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI for some locations which are used to build the input to a cGAN. Based on the prior-CSI estimates, their locations and the location of the desired channel, the cGAN is trained to output the channel expected at the desired location. This combined approach can be used for low overhead CSI reporting as, after training, we only need to report the desired location. Our results show that our CSI recreation method is successful in modelling the wireless channel under different configurations of prior-CSI spatial sampling. In addition, the results consider a real world measurement campaign for indoor line of sight and non-line of sight channels. The signal to noise ratio (SNR) achieved by our CSI recreation is better than the SNR reported by the measured campaign providers. Moreover, our CSI recreation provides means for low overhead CSI reporting as the UNN structure is underparameterized compared to the full explicit CSI, and only the desired location is needed for the cGAN to recreate the desired CSI.

Efficient Performance Bound for Channel Estimation in Massive MIMO Receiver

In this paper, we present a new performance bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power calculation after the CE unit in a multi-antenna receiver. We decompose a non-line of sight (NLOS) channel into separate taps and calculate the cross-covariance matrix between them. Then a linear minimum mean squared error (MMSE) method is applied with these taps to estimate residual CE error value for each unique scenario, assuming Gaussian distribution of tap amplitudes and antenna noise. An artificial CE is calculated as a sum of the ideal noiseless channel (pre-defined Quadriga model) and the leftover noise after the optimal estimate. The artificial CE is then utilized in the MIMO detector and decoder units to calculate performance bound. Our method outperforms the accuracy of a well-known Cramer-Rao lower bound (CRLB) due to considering more statistics (number of taps and correlation between them) since performance strongly depends on several channel taps and their power ratio. Additionally, we show that our bound can be obtained from the generalized Bayesian CRLB. Simulation results are presented for the 5G QuaDRiGa 2.0 NLOS channel.

Diamond communication network design for the non-line-of-sight ultraviolet channel model

Ultraviolet (UV) communication can work effectively in non-line-of-sight (NLOS) mode due to its special scattering property. An NLOS channel model suitable for UV communication network is established. The relationship between path loss and emission beam angle, emission elevation angle, and transmission distance is analyzed, and the effectiveness of the model is verified by experiment. Based on the established NLOS channel model, a diamond communication network design is proposed for the UV communication network, the effective coverage area expression of the diamond network and the maximum effective coverage rate of 44.23% are obtained, and the minimum number of nodes required for the complete seamless coverage of the communication network is analyzed. By comparing the diamond network and the square network under the certain connectivity, the effective coverage area loss rates of the diamond network and the square network are 1.504% and 21.083%, respectively.