Reactive jammer detection in OFDM with index modulation

Detection of jamming attacks is an important tool to improve the resource efficiency of jammer resilient communication networks. Detecting reactive jammers is especially difficult since the attacker is cognitive and focuses only on the used channels. Orthogonal frequency division multiplexing with index modulation (OFDM-IM) consists of active and passive subcarriers. Only active subcarriers carry modulated signals while passive subcarriers are left unused. In OFDM-IM systems, information bits are also dynamically embedded in the indices of these active subcarriers. As a result, remaining passive subcarriers cause instantaneously changing and unused holes in the spectrum that a reactive jammer cannot escape from attacking. In this paper, we propose an OFDM-IM-based detection scheme to improve the detection performance against reactive jammers. The proposed method exploits the dynamically changing empty OFDM-IM subcarriers to improve detection performance. A detection mechanism that is based on the variance of received signals is considered to identify the jammed subcarriers reliably and with low complexity. We assumed a destructive and elusive reactive jammer model that applies a zero-mean Gaussian jamming signal to the occupied channels. The performance of the variance detector is investigated analytically for OFDM-IM and OFDM-based systems under the given jammer model. The results showed that passive subcarriers of OFDM-IM inherently provide a better detection performance compared to the classical OFDM. Lastly, the analytical results are verified via simulations against both full-band and partial-band reactive jammers. Also, the effect of noise and the jamming power on the detection performance is investigated via extensive simulations.     

Outage capacity analysis of the massive MIMO diversity channel

We consider the massive Multiple Input Multiple Output (MIMO) channel affected by independent and identically distributed Rayleigh fading, with linear processing at both transmitter and receiver sides to pursue full diversity, and analyze its outage capacity for large number of antennas. We first discuss the classical Single Input Multiple Output (SIMO) diversity channel that encompasses Maximal Ratio Combining (MRC) or Selection Combining (SC). For MRC, a numerical computation and a Gaussian Approximation (GA) are considered, whereas for SC an exact evaluation is presented. The analysis is then straightforwardly extended to the Multiple Input Single Output (MISO) diversity channel that encompasses Maximal Ratio Transmission (MRT) or transmit antenna selection. The general full diversity MIMO channel is finally considered, with optimal linear processing or simple antenna selection at both transmitter and receiver. If the number of antennas is sufficiently large on at least one side, the outage capacity of each considered diversity channel approaches that of a reference Additive White Gaussian Noise (AWGN) channel with properly defined Signal-to-Noise Ratio (SNR), which provides a performance benchmark. This conclusion is valid for large but realistic number of antennas compatible with the assumption of independent fading.     

基于FRFT域分形特征的压制干扰存在性检测（英）

基于分数阶傅里叶变换（FRFT）域的分形特征研究压制干扰的存在性检测问题。首先,分析了典型的三种压制干扰的分形特征,说明了典型压制干扰信号具有分形特性,并采用盒维数和信息维数定量描述它们的分形特征。然后,发现了压制干扰和高斯白噪声在FRFT域具有不同的分形特征,进而提出了一种检测压制干扰存在性的方法。最后,仿真验证了该方法的有效性和优越性。     

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

In massive multiple-input multiple-output (MIMO), it is much challenging to obtain accurate channel state information (CSI) after radio frequency (RF) chain reduction due to the high dimensions. With the fast development of machine learning(ML), it is widely acknowledged that ML is an effective method to deal with channel models which are typically unknown and hard to approximate. In this paper, we use the low complexity vector approximate messaging passing (VAMP) algorithm for channel estimation, combined with a deep learning framework for soft threshold shrinkage function training. Furthermore, in order to improve the estimation accuracy of the algorithm for massive MIMO channels, an optimized threshold function is proposed. This function is based on Gaussian mixture (GM) distribution modeling, and the expectation maximum Algorithm (EM Algorithm) is used to recover the channel information in beamspace. This contraction function and deep neural network are improved on the vector approximate messaging algorithm to form a high-precision channel estimation algorithm. Simulation results validate the effectiveness of the proposed network.     

Spectral and energy efficiency for uplink massive MIMO systems with mixed-ADC architecture

In this paper, we introduce a mixed- analog-to-digital converter (ADC) architecture for massive multiple-input multiple-output (MIMO) systems and study the system’s performance mainly includes the achievable spectral efficiency and energy efficiency. In principle, the mixed-ADC architecture permits the one part of antennas at the base station (BS) are connected to speed and expensive full-resolution ADCs and the remaining part of the antennas are connected to the cheap low-resolution ADCs. By applying the general maximum-ratio combining detector, a tractable approximate expression for the achievable SE is obtained. Leveraging on the derived results, the effects of the number of BS antennas and the percent of the full-resolution ADCs on the achievable SE are investigated. Results show that the achievable SE increases with the percent of the full-resolution ADCs and the number of BS antennas. Based on a realistic power consumption model, we evaluate the energy efficiency for the considered mixed-ADC architecture. Moreover, under the certain achievable SE constraint, we maximize the energy efficiency by adjusting the number of low-resolution ADCs and the resolution bits of the corresponding ADC device. Numerical results showcase that the energy efficiency can be improved by enhancing the average transmitted power, and there exists an optimal number of resolution bits and the number of antennas to maximize the energy efficiency, which indicates that the application of mixed-ADC architecture has a great potential in future mobile communication system.     

激光干扰红外成像系统的噪声等效温差及信噪比分析

噪声等效温差(NETD)和信噪比(SNR)是衡量红外成像系统性能的重要指标之一,它与探测器的多种性能参数有关。分析了激光辐照干扰红外探测系统及不同材料类型红外探测器性能参数变化。通过理论定量计算分析,比对YAG激光(波长1.06 μm)干扰前后系统噪声等效温差、信噪比曲线,得到了激光损伤后的NETD比损伤前NETD大2至3个数量级,同时,激光干扰后的信噪比相对激光干扰前信噪比小2个多数量级,进一步推导出激光干扰对红外探测系统的影响。     

Digi-FH-OFDM: An all-digital wideband frequency-hopped OFDM system

Frequency-hopped spread-spectrum systems (FHSS) traditionally employ a super-heterodyne receiver architecture to perform frequency hopping in the passband. Such an architecture consists of analog blocks such as the mixer and the local oscillator that contribute greatly to the overall cost and hardware complexity of the system. The recent development of direct radio-frequency (RF) data converters has led to the possibility of having an all-digital receiver architecture where an RF signal is digitized directly to baseband, without the need to translate it to an intermediate frequency. Motivated by this, we propose an all-digital wideband frequency-hopped orthogonal frequency division multiplexing system which is abbreviated as Digi-FH-OFDM in this paper. The system performs two-stage frequency hopping — one in the wideband and the other in the baseband. The system architecture and the implementation details are presented. Real-time power spectra of the hopped signals in the wideband are obtained after transmitting them over the air via the RF data converters of the reconfigurable Xilinx Ultrascale ZCU111 RFSoC board and the Qorvo RF front end card. The bit error rate performance of the system is studied against eavesdropping and jamming attacks under a slow-fading channel and pilot-based channel estimation. The proposed Digi-FH-OFDM system outperforms the existing analog and partially digital FH-OFDM systems in terms of hardware complexity, robustness to eavesdropping and jamming, and the overall latency.     

Data detection based on matrix decomposition for massive MIMO systems in realistic channel scenarios

Massive multiple-input multiple-output (MIMO) is a key technology for modern wireless communication systems. In massive MIMO receivers, data detection is a computationally expensive task. In this paper, we explore the performance and the computational complexity of matrix decomposition based detectors in realistic channel scenarios for different massive MIMO configurations. In addition, data detectors based on decomposition algorithms are compared to the approximate-inversion detection (AID) methods. It is shown that the alternating-direction-method-of-multipliers-based-Infinity-Norm (ADMIN) detection is promising in realistic channel environment and the performance is stable even when the ratio of the base-station (BS) antenna elements to the number of users is small. In addition, this paper studies the performance of several detectors in imperfect channel state information (CSI) and correlated channels. Our work provides valuable insights for massive MIMO systems and very large-scale integration (VLSI) designers to select the appropriate massive MIMO detector based on their specifications.     

水声多输入多输出信道的分布式压缩感知估计

多输入多输出技术通过采用多个阵元进行多发多收空间复用信道可在极其有限的通信带宽下实现高速水声通信,但由于同时存在通道间干扰和多径干扰,水声MIMO信道估计变得困难。提出利用MIMO水声信道多径稀疏结构存在的相关性,在经典联合稀疏模型的基础上对MIMO观测矩阵进行重组,从而建立基于分布式压缩感知的单载波水声MIMO通信信道联合稀疏模型;同时,针对信道响应中具有相同多径位置的稀疏部分和特有稀疏部分设计区分性正交匹配追踪算法进行联合重构,进一步抑制通道间干扰的影响。最后通过仿真和海上实验进行本方法有效性的验证,实现16 kbps的MIMO水声通信。通过算法推导、仿真和实验可得到结论:利用MIMO水声信道多径相关性进行分布式压缩感知估计可提高估计性能。      

Full-Duplex Relay with Delayed CSI Elevates the SDoF of the MIMO X Channel

In this article, the sum secure degrees-of-freedom (SDoF) of the multiple-input multiple-output (MIMO) X channel with confidential messages (XCCM) and arbitrary antenna configurations is studied, where there is no channel state information (CSI) at two transmitters and only delayed CSI at a multiple-antenna, full-duplex, and decode-and-forward relay. We aim at establishing the sum-SDoF lower and upper bounds. For the sum-SDoF lower bound, we design three relay-aided transmission schemes, namely, the relay-aided jamming scheme, the relay-aided jamming and one-receiver interference alignment scheme, and the relay-aided jamming and two-receiver interference alignment scheme, each corresponding to one case of antenna configurations. Moreover, the security and decoding of each scheme are analyzed. The sum-SDoF upper bound is proposed by means of the existing SDoF region of two-user MIMO broadcast channel with confidential messages (BCCM) and delayed channel state information at the transmitter (CSIT). As a result, the sum-SDoF lower and upper bounds are derived, and the sum-SDoF is characterized when the relay has sufficiently large antennas. Furthermore, even assuming no CSI at two transmitters, our results show that a multiple-antenna full-duplex relay with delayed CSI can elevate the sum-SDoF of the MIMO XCCM. This is corroborated by the fact that the derived sum-SDoF lower bound can be greater than the sum-SDoF of the MIMO XCCM with output feedback and delayed CSIT.     

Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems

Hybrid analog/digital multiple input multiple output (MIMO) system is proposed to mitigate the challenges of millimeter wave (mmWave) communication. This architecture enables utilizing the large array gain with reasonable power consumption. However, new methods are required for the channel estimation problem of hybrid architecture-based systems due to the fewer number of radio frequency (RF) chains than antenna elements. Leveraging the sparse nature of the mmWave channels, compressed sensing (CS)-based channel estimation methods are proposed. Recently, machine learning (ML)-aided methods have been investigated to improve the channel estimation performance. Additionally, the Doppler effect should be considered for the high mobility scenarios, and we deal with the time-varying channel model. Therefore, in this article, we consider the scenario of time-varying channels for a multi-user mmWave hybrid MIMO system. By proposing a Deep Neural Network (DNN) and defining the inputs and outputs, we introduce a novel algorithm called Deep Learning Assisted Angle Estimation (DLA-AE) for improving the estimation of the Angles of Departure/Arrival (AoDs/AoAs) of the channel paths. In addition, we suggest Linear Phase Interpolation (LPI) to acquire the path gains for the data transmission instants. Simulation results show that utilizing the proposed DLA-AE and LPI methods enhance the time-varying channel estimation accuracy with low computational complexity.     

Performance analysis of dual-cell HSDPA plus MIMO and LTE along with adaptive MIMO switching at the cellular level

The main target of this research work is to present the performance analysis of Dual-Cell High Speed Downlink Packet Access (DC-HSDPA) plus Multiple Input Multiple Output (MIMO) supported by sophisticated Power Control (PC) with Long Term Evolution (LTE) integrated with Adaptive MIMO Switching (AMS). A simple approach of power allocation for DC-HSDPA in the downlink direction is presented in this paper, in which power resources are dynamically allocated to the users, irrespective of the number of code utilizations. This paper also highlights the impact of efficient power allocation in DC-HSDPA compared to conventional DC-HSDPA without any PC. In addition to different Intersite Distance (ISD), the impact of serving variable numbers of users per Transmission Time Interval (TTI) was also analyzed in terms of average cell throughput, relative throughput gain, and user’s probability of no data transfer in a macrocellular environment.Simulation results revealed that at 500 m ISD LTE exhibits better performance with AMS compared to spatial multiplexing, and it offered an average cell throughput of around 47 Mbps with nearly 5.5% user’s probability of no data transfer. It was learned that DC-HSDPA performance improves by adopting a PC scheme in the Downlink (DL) direction. At small ISD, DC-HSDPA with MIMO provides an average cell throughput of around 19.5 and 13.7 Mbps with and without PC, respectively.     

一种基于自相关函数的幂律检测器

针对水下高斯噪声中非高斯瞬态信号的检测问题,在研究Nuttall提出的power-law(幂律)检测器的基础上,为了提高其检测性能,依据信号和噪声的自相关函数的差异,提出了基于自相关函数的power-law检测器,并对其参数的最佳选取及检测性能进行了仿真研究。仿真结果表明,基于自相关函数的检测器较基于DFT的power-law检测器性能有所提高,有一定的可行性。     

Performance of GPRML-AR system in perpendicular magnetic recording channel with thermal decay

The bit-error rate (BER) performance of the generalized partial response maximum likelihood with autoregressive (GPRML-AR) channel model system in perpendicular magnetic recording (PMR) channel with thermal decay is obtained. The 128/130(0,16/8) run-length-limited (RLL) code is used as a recording code. The GPR channel consists of the PR1 channel followed by the reduction circuit of predicted noise. The BER performance is evaluated by computer simulation using a thermal decay model. The model has been obtained by using an approximate equation that represents amplitude degradation of the reproducing waveform with elapsed time based on the experimental data for CoPtCr-SiO₂ PMR media. The Viterbi detector with an AR channel model is employed. Furthermore, long-term degradation of the required SNR to achieve a BER of 10⁻⁴ with elapsed time is obtained and the performance is compared with that of PR1ML system. The results show that the poorer the thermal stability of the medium becomes, the larger the SNR gain of the GPR1ML-AR system over the PR1ML system becomes. The SNR gain also increases with elapsed time.     

探测器噪声对波前编码成像系统图像复原过程影响的理论研究

波前编码光学系统其成像过程由光学成像和图像复原两部分组成,光学成像过程探测器上所成的编码图像含有噪声,该噪声在图像复原过程将被放大,为了分析探测器噪声对波前编码系统在图像复原过程中产生的影响,通过添加高斯随机噪声来模拟探测器噪声,使用直接逆滤波、维纳滤波和Lucy—Richardson滤波算法,对波前编码成像系统焦点位...     

Soft decision decoding of the orthogonal complex MIMO codes for three and four transmit antennas

Orthogonality is a much desired property for MIMO coding. It enables symbol-wise decoding, where the errors in other symbol estimates do not affect the result, thus providing an optimality that is worth pursuing. It also paves the way for low complexity soft decision decoding, which for orthogonal complex MIMO codes is known for two transmit (Tx) antennas, i.e. for the Alamouti code. We propose novel soft decision decoders for the orthogonal complex MIMO codes on three and four Tx antennas and extend the old result of maximal ratio combining (MRC) to cover all orthogonal codes up to four Tx antennas.As a rule, a sophisticated transmission scheme encompasses forward error correction (FEC) coding, and its performance is measured at the FEC decoder instead of at the MIMO decoder. We introduce the receiver structure that delivers the MIMO decoder’s soft decisions to the demodulator, which in turn cranks out the logarithm of likelihood ratio (LLR) of each bit and delivers them to the FEC decoder. This significantly improves the receiver, where a maximum likelihood (ML) MIMO decoder makes hard decisions at a too early stage. Further, the additional gain is achieved with stunningly low complexity.     

Low-complexity soft-output signal detector based on AI-SSOR preconditioned conjugate gradient method over massive MIMO correlated channel

Massive multiple-input multiple-output (MIMO) techniques with a large number of antenna elements at base station (BS) have been proved as an alternative to provide potential opportunity to increase the spectrum and energy efficiency. However, in the system, there generally exists a spatial correlation effect due to insufficient antenna elements spacing and/or the lack of rich scattering at BS. The minimum mean square error (MMSE) method performs signal detection at the expense of large-scale matrix inversion operation. Thus, the conjugate gradient (CG) method has received a lot of attentions to realize the MMSE detection efficiently. Unfortunately, this efficiency can be compromised due to the ill-conditioned equalization matrix of MMSE method over the correlated channel environments. Moreover, the hard output signal detection exhibits a sharply degradation in performance for higher-order quadrature amplitude modulation (QAM). Therefore, the modern communication systems use the soft-output information, i.e., log-likelihood ratio (LLR) along with the forward error-correcting code (FEC) to achieve satisfactory performance. The LLR computation along with a higher-order QAM remains challenging due to the exhaustive search of symbol in the modulation constellation. In this paper, a low-complexity soft-output signal detector based on approximate inverse symmetric successive over-relaxation preconditioned conjugate gradient (AI-SSOR-CG-SOD) method is proposed to realize MMSE method detection for uplink multiuser massive MIMO correlated channel. In the proposed method, a new preconditioner, an AI-SSOR, which is based on the Neumann series approximation of the inverse of the conventional SSOR preconditioner is firstly developed to handle ill-conditioned matrix, and then incorporated with CG method to improve the convergence rate and performance. According to the characteristic of the Gray-coding that adjacent symbols in the constellation set have only one different bit, the constellation set is divided multiple times based on the bits of the inphase and the quadrature components of the symbol, which reduces the complexity of the LLR computation of the transmitted bits by avoiding the exhaustive search process. Simulation results show that the AI-SSOR preconditioner is robust against spatial correlation effect, and the proposed detector converges at 3 iterations. Simulation results also show that the proposed detector achieves a better trade-off between the complexity and the performance compared to other existing detectors.     

Design of bitstream-based adaptive-connected array architecture for mmWave Multiuser MIMO systems

We investigate a bitstream-based adaptive-connected massive multiple-input multiple-output (MIMO) architecture that trades off between high-power full-connected and low-performance sub-connected hybrid precoding architectures. The proposed adaptive-connected architecture which enables each data stream to be computed independently and in parallel, consists of fewer phase shifters (PS) and switches than the other adaptive-connected architectures. With smaller array groups, the proposed architecture uses fewer PS and switches, so that its power consumption gradually decreases in millimeter wave (mmWave) Multiuser MIMO (MU-MIMO) system. To fully demonstrate the performance of the proposed architecture in mmWave MU-MIMO system with practical constraints, we combine the connection-state matrix with the hybrid precoders and combiners to maximize energy efficiency (EE) of the system equipped with the proposed architecture. We then propose the hybrid precoding and combining (HPC) scheme suitable for multi-user and multi-data streams which utilizes the SCF algorithm to obtain the constant modulus of the analog precoder at convergence. In the digital precoding and combining stage, the digital precoder and combiner are designed to reduce the amount of computation by utilizing the singular value decomposition (SVD) of corresponding equivalent channel. In the mmWave MU-MIMO-OFDM system equipped with the proposed architecture, with the increase of the total number of data streams, simulation results demonstrate that we can exploit the proposed HPC scheme to achieve better EE than the traditional hybrid full-connected architecture exploiting some existing schemes.     

Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack.     

Proof-of-principle experimental demonstration of quantum secure imaging based on quantum key distribution

We present a quantum secure imaging(QSI) scheme based on the phase encoding and weak+vacuum decoy-state BB84 protocol of quantum key distribution(QKD). It allows us to implement a computational ghost imaging(CGI) system with more simplified equipment and reconstructed algorithm by using a digital micro-mirror device(DMD) to preset the specific spatial distribution of the light intensity. What is more, the quantum bit error rate(QBER) and the secure key rate analytical functions of QKD are used to see through the intercept-resend jamming attacks and ensure the authenticity of the imaging information. In the experiment, we obtained the image of the object quickly and efficiently by measuring the signal photon counts with a single-photon detector(SPD), and achieved a secure key rate of 571.0 bps and a secure QBER of 3.99%, which is well below the lower bound of QBER of 14.51%. Besides, our imaging system uses a laser with invisible wavelength of 1550 nm, whose intensity is as low as single-photon, that can realize weak-light imaging and is immune to the stray light or air turbulence, thus it will become a better choice for quantum security radar against intercept-resend jamming attacks.