人工噪声辅助的多用户安全传输方法

针对传统人工噪声辅助的安全传输方法无法适用于发射天线数等于接收天线数的多用户(Multiuser, MU)多输入多输出(Multiple-Input Multiple-Output, MIMO)系统的问题,提出了一种人工噪声辅助的多用户安全传输新方法。通过合理设计人工噪声与预编码矩阵,该方法可在同时满足所有用户传输需求的基础上,显著提升发射天线数等于接收天线数的MU-MIMO系统的安全性。同时,针对提出的方法进行了检测算法与平均保密速率的推导与分析。理论分析与仿真结果表明,所提方法可有效提升无线通信系统的安全性,在信噪比为20 dB时,该方法较传统方法的平均保密速率提升达到120%。     

基于有限反馈的非可信中继系统的物理层安全性能分析

在基于有限反馈获得部分信道状态信息的条件下,研究了放大转发非可信中继系统的物理层安全传输技术.通过目的节点发送人工噪声干扰信息,使系统获得了正安全容量.推导了安全中断概率和传输中断概率的闭合表达式,分析了反馈比特数对系统安全性和可靠性的影响,进而提出了能同时兼顾系统安全性和可靠性的最优反馈比特数的自适应选择方案.     

联合发端天线选择和收端人工噪声的物理层安全传输方法

该文在同频全双工技术快速发展的背景下,针对物理层安全研究中波束成形技术的高复杂度和发端天线选择(TAS)技术的低性能,提出一种联合发端天线选择和收端人工噪声(AN)的物理层安全传输方法TAS-rAN。首先,有多根天线的发端,利用天线选择技术,选取能使合法接收方接收信噪比最大的天线发送保密消息;其次,有同频全双工能力的收端,在接收到消息的同时,发送人工噪声来扰乱窃听方对保密消息的窃听。在Nakagami-m信道下,推导了安全中断概率的闭合表达式,并基于此,得到非零安全容量的概率表达式;通过渐进安全中断概率的推导,得到TAS-rAN方法的安全分集度。仿真结果表明,与已有的TAS-single和TAS-Alamouti方法相比,TAS-rAN安全方法具有较强的稳定性,且能提供更优的安全性能。     

利用人工噪声提高合法接收者性能的物理层安全方案

该文研究在采用波束赋形和人工噪声的物理层安全方案中利用人工噪声提高合法接收端性能。发送端根据发送符号和信道系数,判断人工噪声是否对合法接收端的信号检测有益,并针对有益噪声和无益噪声分别设计不同的噪声波束赋形矢量。通过利用有益噪声,在不改变窃听端接收信噪比的条件下,合法接收端的信噪比有较明显的提高。对误比特率和保密容量进行理论分析和仿真,结果显示,与传统的人工噪声方案相比,所提方案可提高合法接收端的性能,改善保密容量。     

基于接收机人工噪声的物理层安全技术及保密区域分析

提出了一种实现无线通信物理层安全的新方法,并从信息论的角度进行了性能分析。此方法通过合法接收者发送人工噪声来干扰窃听者信道,同时通过抵消技术使得自身不受人工噪声的影响。此方法无需信道信息的反馈,能够对抗多天线的窃听者,具有强的鲁棒性。此外基于地理位置信息提出了一种“保密区域”的新概念,可以作为物理层安全的评价标准和设计准则。分析和仿真结果表明所提算法对安全性能的提升较为明显,所提“保密区域”概念能够较好的从地理位置的角度评估物理层安全性能。      

物理层安全的随机波束成型传输算法

针对传统信息论物理层安全研究中,对合法用户信道质量要求较高的问题,文中提出随机波束成型的物理层安全传输方法。该方法通过在主信道特征向量的垂直方向构造人工随机波束成型信道,使得合法用户与窃听方信道传输特征的差异性转化为信道质量的差异,最终得到正的保密容量。为保证算法的完备性,文章解决了三方面问题:1）给出随机波束成型的空间构造方法;2）随机波束成型方法的保密容量和最佳信源分布;3）人工随机信道的构造方法。最后针对采用随机波束成型算法的发送分集系统,通过设计、仿真其噪声发生和功率分配方案,进一步说明即使在合法用户信道质量差的情况下,随机波束成型传输算法也可获得保密传输容量。      

基于LabVIEW-USRP的人工噪声辅助MIMO通信系统仿真实验硬件平台

人工噪声作为一种物理层安全技术，与基于强计算密码学的传统通信安全机制不同，不会被快速发展的算力破解。关于人工噪声辅助多输入多输出（Multi-Input Multi-Output,MIMO）通信系统的安全性的理论研究已相当完备，但由于人工噪声深度依赖即时、准确的信道反馈，其在实际系统中的应用效果仍需通过试验进一步验证。提出了一种人工噪声辅助下的MIMO通信系统仿真实验硬件平台，并通过实验数据证明了人工噪声对合法MIMO通信系统可靠性的影响较小，且能够有效提升系统安全性。     

基于携能通信的非信任双向中继网络安全传输方案

针对非信任双向中继网络的能量受限和信息安全问题,该文提出一种基于无线携能通信(SWIPT)与人工噪声辅助的物理层安全传输方案。该方案中的非信任中继采用功率分割(PS)策略辅助合法用户进行保密通信,而全双工干扰机在进行能量采集的同时发送人工噪声以确保系统安全。以最大化系统保密性能为目标,优化了中继的PS因子,推导了保密和速率的解析式及高信噪比条件下最佳PS因子的闭式解。特别针对非理想信道状态信息的情况,分析了信道估计误差对系统保密性能的影响。仿真结果验证了理论推导的正确性,并证明了所提的基于PS策略的干扰机协同传输方案相比采用时间切换(TS)策略或目的节点协同干扰的方案具有更优的保密性能。     

隐藏有限字符特性的跳空安全传输方法

针对无线数字通信系统,从信息论角度推导了一个保障物理层安全传输的充分条件,并提出了一种隐藏有限字符特性的跳空安全传输方法。首先,在有限字符输入系统中,多天线窃听者具有离散有噪无损信道（Discrete Noisy Lossless Channel ,DNLC ）结构,可以恢复保密信号,致使人工噪声方法失效,因此破坏窃听者的DNLC结构是保证系统传输安全的一个充分条件。然后,利用无线信道特征的多样性和独特性,提出一种基于多输入多输出（Multiple In-put Multiple Output ,MIMO）系统的跳空安全传输方法,在传输信息的过程中随机切换收发天线并发送空域干扰,可以隐藏保密信号的有限字符特性并破坏窃听者的DNLC结构。最后,理论分析和仿真结果表明了该安全方法的有效性。     

人工噪声辅助的物理层安全信号峰均功率比减低算法

人工噪声辅助的物理层安全通信系统采用人工噪声破坏窃听信道的方式提升系统安全信道容量是近年来物理层安全通信领域研究的经典系统模型之一。该文针对这一模型中发射信号存在高峰均功率比问题,利用噪声子空间提供的冗余度提出一种基于噪声子空间功率分配的峰均功率比降低算法。该算法通过分式规划、DC规划以及二次非凸等式约束松弛将非凸的峰均功率比优化问题转化为一系列的凸问题迭代求解。仿真结果表明在系统放大器存在一定线性范围的约束下,该文提出的算法能够有效降低人工噪声辅助的物理层安全通信系统发射信号的峰均功率比问题,达到提高系统中合法用户的通信性能的目的。     

Limited feedback-based block diagonalization for the MIMO broadcast channel

Block diagonalization is a linear preceding technique for the multiple antenna broadcast (downlink) channel that involves transmission of multiple data streams to each receiver such that no multi-user interference is experienced at any of the receivers. This low-complexity scheme operates only a few dB away from capacity but requires very accurate channel knowledge at the transmitter. We consider a limited feedback system where each receiver knows its channel perfectly, but the transmitter is only provided with a finite number of channel feedback bits from each receiver. Using a random quantization argument, we quantify the throughput loss due to imperfect channel knowledge as a function of the feedback level. The quality of channel knowledge must improve proportional to the SNR in order to prevent interference-limitations, and we show that scaling the number of feedback bits linearly with the system SNR is sufficient to maintain a bounded rate loss. Finally, we compare our quantization strategy to an analog feedback scheme and show the superiority of quantized feedback.     

Data Rate Loss Due to Quantized SNR Estimates in Adaptive Bit Loading Algorithms

Adaptive bit loading enables OFDM systems to optimize the transmit power levels and/or transmission data rates. These algorithms require the knowledge of ideal signal to noise ratio (SNR) for each data subcarrier at both the transmitter and the receiver. However, in practice the channel SNR can only be obtained and shared between the destination and the information source in a quantized fashion. In this paper, we analyze the effect of SNR quantization on the system data rate. We analytically obtain the average data rate loss due to quantization of SNR estimates. We show, via measurement results on powerline communication channels, that the effect of SNR quantization on the system data rate is not negligible if a midtread quantizer with less than 8 bits is used in the SNR estimation process for powerline communications. We also compare obtained results with theoretical capacity values in order to emphasize the data rate loss due to making use of quantized SNR estimates.     

Signature optimization for CDMA with limited feedback

We study the performance of joint signature-receiver optimization for direct-sequence code-division multiple access (DS-CDMA) with limited feedback. The receiver for a particular user selects the signature from a signature codebook, and relays the corresponding B index bits to the transmitter over a noiseless channel. We study the performance of a random vector quantization (RVQ) scheme in which the codebook entries are independent and isotropically distributed. Assuming the interfering signatures are independent, and have independent and identically distributed (i.i.d.) elements, we evaluate the received signal-to-interference plus noise ratio (SINR) in the large system limit as the number of users, processing gain, and feedback bits B all tend to infinity with fixed ratios. This SINR is evaluated for both the matched filter and linear minimum mean-squared error (MMSE) receivers. Furthermore, we show that this large system SINR is the maximum that can be achieved over any sequence of codebooks. Numerical results show that with the MMSE receiver, one feedback bit per signature coefficient achieves close to single-user performance. We also consider a less complex and suboptimal reduced-rank signature optimization scheme in which the user's signature is constrained to lie in a lower dimensional subspace. The optimal subspace coefficients are scalar-quantized and relayed to the transmitter. The large system performance of the quantized reduced-rank scheme can be approximated, and numerical results show that it performs in the vicinity of the RVQ bound. Finally, we extend our analysis to the scenario in which a subset of users optimize their signatures in the presence of random interference.     

Capacity of a Multiple-Antenna Fading Channel With a Quantized Precoding Matrix

Given a multiple-input multiple-output (MIMO) channel, feedback from the receiver can be used to specify a transmit precoding matrix, which selectively activates the strongest channel modes. Here we analyze the performance of random vector quantization (RVQ), in which the precoding matrix is selected from a random codebook containing independent, isotropically distributed entries. We assume that channel elements are independent and identically distributed (i.i.d.) and known to the receiver, which relays the optimal (rate-maximizing) precoder codebook index to the transmitter using $B$ bits. We first derive the large system capacity of beamforming (rank-one precoding matrix) as a function of $B$, where large system refers to the limit as $B$ and the number of transmit and receive antennas all go to infinity with fixed ratios. RVQ for beamforming is asymptotically optimal, i.e., no other quantization scheme can achieve a larger asymptotic rate. We subsequently consider a precoding matrix with arbitrary rank, and approximate the asymptotic RVQ performance with optimal and linear receivers (matched filter and minimum mean squared error (MMSE)). Numerical examples show that these approximations accurately predict the performance of finite-size systems of interest. Given a target spectral efficiency, numerical examples show that the amount of feedback required by the linear MMSE receiver is only slightly more than that required by the optimal receiver, whereas the matched filter can require significantly more feedback.      

Performance Analysis of Quantized Beamforming MIMO Systems

Multiple-input multiple-output (MIMO) wireless systems can achieve significant diversity and array gain by using single-stream transmit beamforming and receive combining. A MIMO beamforming system with feedback using a codebook based quantization of the beamforming vector allows practical implementation of such a strategy in a single-user scenario. The performance of this system in uncorrelated Rayleigh flat fading channels is studied from the point-of-view of signal-to-noise ratio (SNR) and outage probability. In this paper, lower bounds are derived on the expected SNR loss and the outage probability of systems that have a single receive antenna or two transmit antennas. For arbitrary transmit and receive antennas, approximations for the SNR loss and outage are derived. In particular, the SNR loss in a quantized MIMO beamforming system is characterized as a function of the number of quantization bits and the number of transmit and receive antennas. The analytical expressions are proved to be tight with asymptotically large feedback rate. Simulations show that the bounds and approximations are tight even at low feedback rates, thereby providing a benchmark for feedback system design     

On the performance of random vector quantization limited feedback beamforming in a MISO system

In multiple antenna wireless systems, beamforming is a simple technique for guarding against the negative effects of fading. Unfortunately, beamforming requires the transmitter to have knowledge of the forward-link channel which is often not available a priori. One way of overcoming this problem is to design the beamforming vector using a limited number of feedback bits sent from the receiver to the transmitter. In limited feedback beamforming, the beamforming vector is restricted to lie in a codebook that is known to both the transmitter and receiver. Random vector quantization (RVQ) is a simple approach to codebook design that generates the vectors independently from a uniform distribution on the complex unit sphere. This correspondence presents performance analysis results for RVQ limited feedback beamforming     

Distributed beamforming in wireless relay networks with quantized feedback

This paper is on quantized beamforming in wireless amplify-and-forward (AF) relay networks. We use the generalized Lloyd algorithm (GLA) to design the quantizer of the feedback information and specifically to optimize the bit error rate (BER) performance of the system. Achievable bounds for different performance measures are derived. First, we analytically show that a simple feedback scheme based on relay selection can achieve full diversity. Unlike the previous diversity analysis on the relay selection scheme, our analysis is not aided by any approximations or modified forwarding schemes. Then, for highrate feedback, we find an upper bound on the average signalto- noise ratio (SNR) loss. Using this result, we demonstrate that both the average SNR loss and the capacity loss decay at least exponentially with the number of feedback bits. In addition, we provide approximate upper and lower bounds on the BER, which can be calculated numerically.We observe that our designs can achieve both full diversity as well as high array gain with only a moderate number of feedback bits. Simulations also show that our approximate BER is a reliable estimation on the actual BER. We also generalize our analytical results to asynchronous networks, where perfect carrier level synchronization is not available among the relays.     

On the design of MIMO block-fading channels with feedback-link capacity constraint

In this paper, we propose a combined adaptive power control and beamforming framework for optimizing multiple-input/multiple-output (MIMO) link capacity in the presence of feedback-link capacity constraint. The feedback channel is used to carry channel state information only. It is assumed to be noiseless and causal with a feedback capacity constraint in terms of maximum number of feedback bits per fading block. We show that the hybrid design could achieve the optimal MIMO link capacity, and we derive a computationally efficient algorithm to search for the optimal design under a specific average power constraint. Finally, we shall illustrate that a minimum mean-square error spatial processor with a successive interference canceller at the receiver could be used to realize the optimal capacity. We found that feedback effectively enhances the forward channel capacity for all signal-to-noise ratio (SNR) values when the number of transmit antennas (n/sub T/) is larger than the number of receive antennas (n/sub R/). The SNR gain with feedback is contributed by focusing transmission power on active eigenchannel and temporal power waterfilling . The former factor contributed, at most, 10log/sub 10/(n/sub T//n/sub R/) dB SNR gain when n/sub T/>n/sub R/, while the latter factor's SNR gain is significant only for low SNR values.     

Quantization Methods for Equal Gain Transmission With Finite Rate Feedback

We consider the design and analysis of quantizers for equal gain transmission (EGT) systems with finite rate feedback-based communication in flat-fading multiple input single output (MISO) systems. EGT is a beamforming technique that maximizes the MISO channel capacity when there is an equal power-per-antenna constraint at the transmitter, and requires the feedback of t-1 phase angles, when there are t antennas at the transmitter. In this paper, we contrast two popular approaches for quantizing the phase angles: vector quantization (VQ) and scalar quantization (SQ). On the VQ side, using the capacity loss with respect to EGT with perfect channel information at transmitter as performance metric, we develop a criterion for designing the beamforming codebook for quantized EGT (Q-EGT). We also propose an iterative algorithm based on the well-known generalized Lloyd algorithm, for computing the beamforming vector codebook. On the analytical side, we study the performance of Q-EGT and derive closed-form expressions for the performance in terms of capacity loss and outage probability in the case of i.i.d. Rayleigh flat-fading channels. On the SQ side, assuming uniform scalar quantization and i.i.d. Rayleigh flat-fading channels, we derive the high-resolution performance of quantized EGT and contrast the performance with that of VQ. We find that although both VQ and SQ achieve the same rate of convergence (to the capacity with perfect feedback) as the number of feedback bits B increases, there exists a fixed gap between the two