Search

Abstract Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

For the requirements of real-time services in autonomous driving systems, such as high-definition (HD) maps, based on the integration of sensing, communication, and computing, a multi-node cooperative sensing mechanism was proposed with the age of information (AoI) as the real-time indicator.Considering the constraints on communication and computing resources and vehicle energy consumption, the information collection, transmission and processing of sensing nodes were optimally scheduled to minimize the AoI averaged over time.A Lyapunov-based online scheduling algorithm was proposed to transform the long-term stochastic optimization problem into an online optimization problem, which could be solved with low complexity.Compared with the existing mechanism considering integrated communication and computing, the proposed mechanism can improve real-time performance by 9%～50%.

With the commercialization of the fifth generation mobile networks, researchers are now focusing on discovering the potential key techniques of the next generation mobile networks, which are expected to provide more accurate perception, lower latency, and higher network capacity. As the communication equipments increase exponentially, wireless transmission environment becomes more complex, leveraging wireless security challenges more and more severe, such as computationally powerful interception, intelligent malicious jamming, communication behavior monitoring. The physical layer security (PLS) techniques have been widely explored as a complement to traditional encryption schemes by exploiting the randomness characteristics of wireless channels to achieve the security from the physical layer. Additionally, reconfigurable intelligent surface (RIS) is considered as a key enabling technology for the six generation (6G) mobile networks, due to its ability to achieve the reconstruction of wireless channel, which is also regarded as a good match with PLS techniques for improving communication security. This paper presents a comprehensive review of the latest research on the integration of RIS and PLS. First, we introduce the principles of PLS from the development of secure communications and the basics of RIS based on the generalized Snell’s law. Then, we categorize RIS according to different hardware architectures, of which the corresponding scenarios are also presented. Subsequently, we review recent works on RIS-assisted PLS in different communication networks, and classify the security scenarios in which RIS is integrated with various advanced communication technologies. Finally, we discuss the potential future research directions and challenges of RIS-aided PLS communications.

Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scalable clustered cell-free network architecture has been proposed recently, under which the large-scale network is flexibly partitioned into a set of independent subnetworks operating parallelly. In this paper, we study the energy-efficient clustered cell-free networking problem with AP selection. Specifically, we propose a user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm to form subnetworks dynamically. Following this, we analyze the average energy efficiency achieved with the proposed UCR-ApSel scheme theoretically and derive an effective closed-form upper-bound. Based on the analytical upper-bound expression, the optimal AP-selection ratio that maximizes the average energy efficiency is further derived as a simple explicit function of the total number of APs and the number of subnetworks. Simulation results demonstrate the effectiveness of the derived optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with the optimal AP-selection ratio achieves around 40% higher energy efficiency than the baselines. The analysis provides important insights to the design and optimization of future ultra-dense wireless communication systems.

Abstract As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has $$6^{\circ }$$ 6 ∘ advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at $$\textrm{SNR}=-8$$ SNR = - 8 dB and the number of snapshots $$N=100$$ N = 100 , this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.

This paper aims to provide a reduced-complexity decoding for satellite communication systems to enhance system performance and transmission efficiency. By addressing the complexity, error rate, and latency issues for satellite communications, a novel 3-dimensional product decoding scheme is proposed. The density evolution algorithm is also introduced to analyze and optimize for the binary message passing decoding, which further reduces the computational complexity and latency. The simulation results show that the proposed method can obtain about 0.3- and 0.15-dB performance gains compared to the similar decodings of the conventional 2-dimensional BCH product codes and staircase codes, respectively. Our proposed coding scheme offers a broad prospect to the critical issues of transmission latency and data throughput in satellite networks.

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) $\gamma _{0}$ at IRS, is studied. Making use of the weak law of large numbers, the simple asymptotic expressions for the received SNR at user and the average SNR at IRS are derived as the number $N$ of IRS elements goes to medium-scale and large-scale. When $N$ tends to large-scale, the asymptotic received SNR at user is proved to be a linear increasing function of a product of $\gamma _{0}$ and $N$ . Subsequently, when the base station (BS) transmit power is fixed, there exists an optimal limited reflective power at IRS. At this point, more IRS reflect power will degrade the SNR performance. Additionally, under the total power sum constraint of the BS transmit power and the power reflected by the IRS, an optimal power allocation (PA) strategy is derived and shown to achieve 0.83 bit rate gain over equal PA. Finally, an IRS with finite phase shifters being taken into account, generates phase quantization errors, and further leads to a degradation of receive performance. The corresponding closed-form performance loss expressions for user’s asymptotic SNR, achievable rate (AR), and bit error rate (BER) are derived for active IRS. Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multiple-input multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing low-resolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choices with acceptable performance loss. In this paper, the combination of the mixed-ADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closed-form expression of the Cramér-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixed-ADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2–4 bits of resolution may be a good choice in practical massive MIMO systems.

Abstract This paper focuses on a reconfigurable intelligent surface (RIS)‐aided full‐duplex multi‐user massive multiple‐input multiple‐output system with transceiver hardware impairments (THWIs). Different from the existing works, the two‐timescale design scheme is considered. The phase shift at the RIS is designed only based on the statistical channel state information. Firstly, the closed‐form expression of the uplink and downlink achievable rate is derived. Then, the power scaling laws are revealed. Finally, the impact of THWIs on the system performance is analysed and genetic algorithm to maximize the achievable rate is used.

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing optimal sensor placement strategies mainly work on the cases without any constraints on the sensor locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., maximizing the determinant of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed-form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.

Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively.

Intelligent reflecting surface (IRS) is a revolutionary and low-cost technology for boosting the spectrum and energy efficiencies in future wireless communication network. In order to create controllable multipath transmission in the conventional line-of-sight (LOS) wireless communication environment, an IRS-aided directional modulation (DM) network is considered. In this paper, to improve the transmission security of the system and maximize the receive power sum (Max-RPS), two alternately optimizing schemes of jointly designing receive beamforming (RBF) vectors and IRS phase shift matrix (PSM) are proposed: Max-RPS using general alternating optimization (Max-RPS-GAO) algorithm and Max-RPS using zero-forcing (Max-RPS-ZF) algorithm. Simulation results show that, compared with the no-IRSassisted scheme and no-PSM optimization scheme, the proposed IRS-assisted Max-RPS-GAO method and Max-RPS-ZF method can significantly improve the secrecy rate (SR) performance of the DM system. Moreover, compared with the Max-RPS-GAO method, the proposed Max-RPS-ZF method has a faster convergence speed and a certain lower computational complexity.

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We focus on the phase optimization of IRS to improve the system sum rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable system sum rate enhancement, which is called Max-RPS-EVD. To further achieve a higher system sum rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20% system sum rate gain over random phase. Additionally, it is also proved the optimal system sum rate can be achieved when TW-DFR and IRS are located in the middle of S and D.

Massive multiple-input multiple-output (mMIMO) enables higher spectrum efficiency by deploying a large number of antenna elements.However, mMIMO will bring a substantial increase in the number of radio frequency (RF) channel, which will result in a sharp increase in cost.Therefore, it is necessary to design mMIMO with comprehensive consideration of the trade-off between the performance and the cost.Sparse array technology can approach the performance of the uniform array with less number of antenna elements by optimizing the position distribution and excitation amplitudes of the antenna elements, thereby reducing the number of RF channels and controlling the cost, and achieving a better cost-effective balance.Both link-level and system-level evaluations and analysis of sparse array based mMIMO that targets for 6G were performed under realistic simulation settings.The results show that with little performance loss, the sparse array based mMIMO can significantly reduce the number of antenna elements and the cost of base station infrastructure.

As excellent tools for aiding communication, an intelligent reflecting surface (IRS) and an unmanned aerial vehicle (UAV) can extend the coverage area, remove the blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance of directional modulation (DM) networks using an IRS and UAV in combination. To fully explore the benefits of the IRS and UAV, two efficient methods are proposed to enhance the SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix (PSM), which is called Max-SR-SLNR. To reduce the computational complexity, the CM, artificial noise (AN) beamforming, and IRS phase shift design are independently designed in the following method. The CM beamforming vector is constructed based on the maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, the AN beamforming vector is designed by null-space projection (NSP) on the remaining two channels, and the PSM of the IRS is directly given by the phase alignment (PA) method. This method is called the MRT-NSP-PA. The simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as the IRS tends to a larger scale.

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss.

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

In this paper, three-dimensional (3D) beamforming characteristics and applications in fifth generation (5G) mobile communications have been studied by considering the physical structure of array antennas, and the properties of the 3D beam pattern formed by planar, rectangular array antennas. Array beam gains are formulated according to rectangular array antennas. The effect of array antenna configuration on 3D beamforming is studied especially according to the building height. The field trial and measurement results have been presented for single and multiple mobile users. The field trial results show that (1) The total sum rate from all users can be increased multiple times (i.e., 3 to 4 times) as large as that of a single user. When the number of users is larger than 8, the sum rate becomes saturated; (2) Users with uniform angular distribution can achieve larger sum rate than users with centralized distribution due to space separation; (3) The performance of the multi-antenna system is best under static-user conditions, with it dropping considerably for mobile conditions, even by more than 50% due to poor channel state information estimation; (4) In case of 3D beamforming, good coverage performance can be achieved in medium or high buildings.

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method.

Switched-beam systems offer a promising solution for realizing multi-user communications at millimeter wave (mmWave) frequencies. A low-complexity beam allocation (LBA) algorithm has been proposed to solve the challenging problem of maximizing sum data-rates. However, there are practical limitations in mmWave systems, such as restrictions in the number of available radio frequency transceiver chains at the base station, sensitivity to sidelobe interference and the beam generation techniques. In this paper, using generalized beam-patterns, we present the maximum sum data-rates achievable in switched-beam mmWave systems compared with fixed-beam systems by applying LBA. Then, the impact on maximum sum data rates of actual beam-patterns, obtained from a practical mmWave lens antenna, which have higher and non-uniform sidelobes compared with the theoretical beams, is assessed. Finally, as a guide for practical wireless system design, benchmarks are established for relative sidelobe levels that provide acceptable sum data-rate performance when considering generalized beam patterns.

This paper investigates the uplink and downlink achievable rates of full-duplex (FD) massive multi-input-multi-output (MIMO) systems in which low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) are employed and maximum ratio combining/maximum ratio transmission processing are adopted. Then, employing an additive quantization noise model, we derive approximate expressions of the uplink and downlink achievable rates, in which the effect of the quantization error, the loop interference, and the inter-user interference is considered. The theoretical results show that using proper power scaling law and more antennas can eliminate the interference and the noise. Furthermore, under the fixed number of antennas, the uplink and downlink approximate achievable rates will become a constant, as the number of quantization bits tends to infinity. Increasing the resolution of ADCs/DACs will limitedly improve the system performance but cause excessive overhead and power consumption, so adopting low-resolution ADCs/DACs in FD massive MIMO systems is sensible.

Security has become an extremely important research topic in wireless networks over the last decade, as it is intimately related to both individual privacy and national security. Directional modulation, as a conventional type of secure modulations, transmits confidential information along the desired directions of legitimate receivers, and artificial noise in other directions, to deliberately confuse eavesdroppers in line-of-sight channels. Recently, artificial noise is also introduced into spatial modulation, leading to a secure spatial modulation strategy. In this Special Section in IEEE A CCESS, secure modulation is defined broadly as any secure modulation method, which includes, but is not limited to, secure directional modulation, secure spatial modulation, and secure index modulation.

Millimeter wave (mmWave) communication has been regarded as one of the most promising technologies for the future generation wireless networks because of its advantages of providing a ultra-wide new spectrum and ultra-high data transmission rate. To reduce the power consumption and circuit cost for mmWave systems, hybrid digital and analog (HDA) architecture is preferred in such a scenario. In this paper, an artificial noise (AN) aided secure HDA beamforming scheme is proposed for mmWave multiple input single output (MISO) system with low resolution digital-to-analog converters (DACs) and finite-quantized phase shifters on radio frequency. The additive quantization noise model for AN aided HDA system is established to make an analysis of the secrecy performance of such systems. With the partial channel knowledge of eavesdropper available, an approximate expression of secrecy rate (SR) is derived. Then using this approximation formula, we propose a two-layer alternatively iterative structure (TLAIS) for optimizing digital precoder (DP) of confidential message (CM), digital AN projection matrix (DANPM) and analog precoder (AP). The inner-layer iteration loop is to design the DP of CMs and DANPM alternatively given a fixed matrix of AP. The outer-layer iteration loop is between digital baseband part and analog part, where the former refers to DP and DANPM, and the latter is AP. Then for a given digital part, we propose a gradient ascent algorithm to find the AP vector. Given a matrix of AP, we make use of general power iteration (GPI) method to compute DP and DANPM. This process is repeated until the terminal condition is reached. Simulation results show that the proposed TLAIS can achieve a better SR performance than existing methods, especially in the high signal-to-noise ratio region.

The radio frequency (RF) phase shifter with finite quantization bits in analog beamforming (AB) structure forms quantization error (QE) and causes a performance loss of received signal to interference plus noise ratio (SINR) at the receiver (called Bob). By using the law of large numbers in probability theory, the closed-form expression of the SINR performance loss is derived to be inversely proportional to the square of Sinc (or sin(x)/x) function. Here, a phase alignment method is applied in the directional modulation transmitter with the AB structure. Also, the secrecy rate (SR) expression is derived with the QE. From the numerical simulation results, we find that the SINR performance loss gradually decreases as the number L of quantization bits increases. This loss is less than 0.3 dB when L is larger than or equal to three. As L exceeds five, the SINR performance loss at Bob can be approximately trivial. Similarly, the SR performance loss gradually reduces as L increases. In particular, the SR performance loss is about 0.1 bits/s/Hz for L = 3 at signal-to-noise ratio of 15 dB.

Abstract We investigate the uplink and downlink achievable rates of full-duplex (FD) massive multi-input multi-output (MIMO) systems with low-resolution analog-digital converters/digital-to-analog converters (ADCs/DACs), where maximum ratio combining/maximum ratio transmission (MRC/MRT) processing are adopted and imperfect channel state information (CSI) is assumed. In this paper, the quantization noise is encapsulated as an additive quantization noise model (AQNM). Then, employing the minimum mean-square error (MMSE) channel estimator, approximate expressions of the uplink and downlink achievable rates are derived, based on the analysis of the quantization error, loop interference (LI), and the inter-user interference (IUI). It is shown that the interference and noise can be eliminated by applying power scaling law properly and increasing the number of antennas. Moreover, given the number of antennas, it is found that the uplink and downlink approximate achievable rates will converge to a constant when the number of quantization bit tends to infinity. Therefore, the system performance that can be improved by increasing ADC/DAC resolution is limited, implying that it is reasonable to adopt low-resolution ADCs/DACs in FD massive MIMO systems.

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.

In this paper, given the beamforming vector of confidential messages and the artificial noise (AN) projection matrix and total power constraint, a power allocation(PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation networks. By the method of Lagrange multiplier, the analytical expression of the proposed PA strategy is derived. To confirm the benefit from the Max SR-based PA strategy, we take all the null-space projection beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noise ratio (SNR) regions. compared with three typical PA parameters such β = 0.1, 0.5, and 0.9, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than 60%. In addition, as the PA factor increases from 0 to 1, the achievable SR increase accordingly in the low SNR region, whereas it first increases and the decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor become large and tends to one in the medium and high SNR regions. In other words, the contribution of AN to SR can be trivial in such a situation.

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases.

In this paper, a random frequency diverse array-based directional modulation with artificial noise (RFDA-DM-AN) scheme is proposed to enhance physical layer security of wireless communications. Specifically, we first design the RFDA-DM-AN scheme by randomly allocating frequencies to transmit antennas, thereby achieving 2-D (i.e., angle and range) secure transmissions, and outperforming the state-of-the-art 1-D (i.e., angle) phase array (PA)-based DM scheme. Then we derive the closed-form expression of a lower bound on the ergodic secrecy capacity (ESC) of our RFDA-DM-AN scheme. Based on the theoretical lower bound derived, we further optimize the transmission power allocation between the useful signal and artificial noise (AN) in order to improve the ESC. Simulation results show that: (1) our RFDA-DM-AN scheme achieves a higher secrecy capacity than that of the PA-based DM scheme; (2) the lower bound derived is shown to approach the ESC as the number of transmit antennas N increases and precisely matches the ESC when N is sufficiently large; and (3) the proposed optimum power allocation achieves the highest ESC of all power allocations schemes in the RFDA-DM-AN.

In orthogonal frequency division multiplexing relay networks with in-phase and quadrature imbalances and full-duplex relay station (RS), how to optimize pilot pattern and power allocation using the criterion of minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain least-squares channel estimator has a heavy impact on self-interference (SI) cancellation. First, the design problem of pilot pattern is casted as a convex optimization. From the Karush-Kuhn-Tucker conditions, the optimal analytical expression is derived when source and RS powers are given or fixed. Second, under the total transmit power sum constraint of source node and RS, an optimal power allocation (OPA) strategy is proposed to further alleviate the effect of Sum-MSE. Simulation results show that the proposed OPA performs better than equal power allocation (EPA) in terms of Sum-MSE, and the Sum-MSE performance gain increases with deviating ρ from the value of ρo minimizing the Sum-MSE, where ρ is defined as the average ratio of the residual SI channel gain at RS to the intended channel gain from source to RS. For example, the OPA achieves approximately 5-dB signal-to-noise ratio gain over EPA by shrinking or stretching ρ with a factor 4. More importantly, the more ρ decreases or increases, the more the performance gain becomes significant.

In this paper, we propose joint angle-delay subspace based-channel estimation in single cell for broadband massive multiple-input and multiple-output systems employing orthogonal frequency division multiplexing modulation. Based on a parametric channel model, we present a new concept of the joint angle-delay subspace, which can be tracked by the low-complexity low-rank adaptive filtering algorithm. Then, we investigate an interference-free transmission condition that the joint angle-delay subspaces of the users reusing the same pilots are non-overlapping. Since the channel statistics are usually unknown, we develop a robust minimum mean square error (MMSE) estimator under the worst precondition of pilot decontamination, considering that the joint angle-delay subspaces of the interfering users fully overlap. Furthermore, motivated by the interference-free transmission criteria, we present a novel low-complexity greedy pilot scheduling algorithm to avoid the problem of initial value sensitivity. Simulation results show that the joint angle-delay subspace can be estimated effectively, and the proposed pilot reuse scheme combined with robust MMSE channel estimation offers significant performance gains.

Communities are often associated with important structural characteristics of a complex network system, therefore detecting communities is considered to be a fundamental problem in network analysis. With the development of data collection technology and platform, more and more sources of network data are acquired, which makes the form of network as well as the related data more complex. To achieve integrative community detection of a multi-layer attributed network that involves multiple network layers together with their attribute data, effectively utilizing the information from the multiple networks and the attributes may greatly enhance the accuracy of community detection. To this end, in this article, we study the integrative community detection problem of a multi-layer attributed network from the perspective of matrix factorization, and propose a penalized alternative factorization (PAF) algorithm to resolve the corresponding optimization problem, followed by the convergence analysis of the PAF algorithm. Results of the numerical study, as well as an empirical analysis, demonstrate the advantages of the PAF algorithm in community discovery accuracy and compatibility with multiple types of network-related data.