IRS-Enabled Spectrum Sharing:Interference Modeling,Channel Estimation and Robust Passive Beamforming

Intelligent reflecting surface(IRS),with its unique capability of smartly reconfiguring wireless channels,provides a new solution to improving spectrum efficiency,reducing energy consumption and saving deployment/hardware cost for future wireless networks.In this paper,IRS-enabled spectrum sharing is investigated,from the perspectives of interference modeling,efficient channel estimation and robust passive beamforming design.Specifically,we first characterize the interference in a spectrum sharing system consisting of a single primary user(PU)pair and a single secondary user(SU)pair,and extend it to the large-scale network by leveraging the Poisson point process(PPP).Then,we propose an efficient channel estimation framework based on decoupling the cascaded IRS channels.Moreover,the tradeoff between spectrum efficiency and energy efficiency is derived from the view of channel estimation accuracy.Finally,we discuss the robust passive beamforming design in presence of imperfect channel estimation and nonideal/discrete phase shifts.It is hoped that this paper provides useful guidance for unlocking the full potential of IRS for achieving efficient spectrum sharing for future wireless networks.

A MATCHED FILTER BANK BASED DOA ESTIMATION FOR ASYNCHRONOUS MULTIPATH CDMA CHANNELS

In this paper, a Direction Of Arrival (DOA) estimation algorithm is proposed for multiuser signals through uplink asynchronous multipath Code Division Multiple Access (CDMA) channels. The algorithm is based directly on the correlation matrices of matched filter bank outputs of desired user’s multipath signals and it does not require that the elements of base station antenna array outnumber the multipath signals, which is necessary for the conventional sub-space based direction-finding algorithm. Simulation results show that the proposed algorithm estimates the DOA of multipath signals effectively and acceptably. The proposed algorithm has the prominent advantages of low complexity, simpleness and practicality, which make it much more suitable for practical application.

Partially Overlapping Channel Assignment Using Bonded and Non-Bonded Channels in IEEE 802.11n WLAN

Nowadays,wireless local area network(WLAN)has become prevalent Internet access due to its low-cost gadgets,flexible coverage and hasslefree simple wireless installation.WLAN facilitates wireless Internet services to users with mobile devices like smart phones,tablets,and laptops through deployment of multiple access points(APs)in a network field.Every AP operates on a frequency band called channel.Popular wireless standard such as IEEE 802.11n has a limited number of channels where frequency spectrum of adjacent channels overlaps partially with each other.In a crowded environment,users may experience poor Internet services due to channel collision i.e.,interference from surrounding APs that affects the performance of the WLAN system.Therefore,it becomes a challenge to maintain expected performance in a crowded environment.A mathematical model of throughput considering interferences from surrounding APs can play an important role to set up a WLAN system properly.While set up,assignment of channels considering interference can maximize network performance.In this paper,we investigate the signal propagation of APs under interference of partially overlapping channels for both bonded and non-bonded channels.Then,a throughput estimation model is proposed using difference of operating channels and received signal strength indicator(RSSI).Then,a channel assignment algorithm is introduced using proposed throughput estimation model.Finally,the efficiency of the proposal is verified by numerical experiments using simulator.The results show that the proposal selects the best channel combination of bonded and non-bonded channels that maximize the performance.

Novel Channel Estimation Method Based on Decision-Directed in OFDM

Based on the analysis of decision-directed （DD） channel estimation by using training symbols, a novel DD channel estimation method is proposed for orthogonal frequency division multiplexing （OFDM） system. The proposed algorithm takes the impact of decision error into account, and calculates the impact to next symbol duration channel state information. Analysis shows that the error propagation can be effectively restrained and the channel variation is tracked well. Simulation results demonstrate that both the signal error rate （SER） and the normalized mean square error （NMSE） performance of the proposed method are better than the traditional DD （DD＋ LS） and the maximum likelihood estimate （DD＋ MLE） method.

Single-Channel Compressive Sensing for DOA Estimation via Sensing Model Optimization

The performance of multi-channel Compressive Sensing (CS)-based Direction-of-Arrival (DOA) estimation algorithm degrades when the gains between Radio Frequency (RF) channels are inconsistent, and when target angle information mismatches with system sensing model. To solve these problems, a novel single-channel CS-based DOA estimation algorithm via sensing model optimization is proposed. Firstly, a DOA sparse sensing model using single-channel array considering the sensing model mismatch is established. Secondly, a new single-channel CS-based DOA estimation algorithm is presented. The basic idea behind the proposed algorithm is to iteratively solve two CS optimizations with respect to target angle information vector and sensing model quantization error vector, respectively. In addition, it avoids the loss of DOA estimation performance caused by the inconsistent gain between RF channels. Finally, simulation results are presented to verify the efficacy of the proposed algorithm.

SIMPLE AND EFFICIENT SPACE-TIME CHANNEL AND DOA ESTIMATION TECHNIQUES IN TD-SCDMA SYSTEMS

In this paper, a simple method is presented for multi-user space-time channel estimation in Time Division-Synchronized Code Division Multiple Access (TD-SCDMA) systems. The method is based on a spe- cific midamble assignment strategy, which results in a cyclic Toeplitz midamble-matrix in the linear equation of the received data vectors. A Fast Fourier Transform (FFT)-based algorithm is used to obtain the estimate of the uplink multi-user space-time channels. Furthermore, the estimated space-time channel is applied to the identification of multi-paths for each user, and Direction Of Arrival (DOA) estimation for each path is carried out by using the extracted spatial signature vector. Aside from the simplicity in computation, the proposed di- rection of arrival estimation method can effectively resolve multi-paths regardless of the correlation and angle separations of the multi-paths.

Intelligent reflecting surfaces-assisted millimeter wave communication:Channel estimation based on deep learning

In response to the challenge posed by the complexity of the system and the difficulty in obtaining accurate channel state information(CSI)for millimeter wave communication assisted by intelligent reflecting surfaces(IRS),we propose a deep learning-based channel estimation scheme.The proposed scheme employs a hybrid active/passive IRS architecture,wherein the least square(LS)algorithm is initially utilized to acquire the channel estimate from the active elements.Subsequently,this estimation is interpolated to obtain a preliminary channel estimation and ultimately refined into an accurate estimate of the channel using the channel super-resolution convolutional neural network(Chan-SRCNN)deep learning network.The simulation results demonstrate that the proposed scheme surpasses LS,orthogonal matching pursuit(OMP),synchronous OMP(SOMP),and deep neural network(DNN)channel estimation algorithms in terms of normalized mean squared error(NMSE)performance,thereby validating the feasibility of the proposed approach.

Resource Allocation for Channel Estimation in Reconfigurable Intelligent Surface-Aided Multi-Cell Networks

Reconfigurable intelligent surface(RIS)is a promising solution to deal with the blockage-sensitivity of millimeter wave band and reduce the high energy consumption caused by network densification. However, deploying large scale RISs may not bring expected performance gain due to significant channel estimation overhead and non-negligible reflected interference.In this paper,we derive the analytical expressions of the coverage probability, area spectrum efficiency(ASE)and energy efficiency (EE)of a downlink RIS-aided multi-cell network.In order to optimize the network performance, we investigate the conditions for the optimal number of training symbols of each antenna-to-antenna and antenna-to-element path (referred to as the optimal unit training overhead) in channel estimation.Our study shows that:1)RIS deployment is not“the more, the better”, only when blockage objects are dense should one deploy more RISs;2) the coverage probability is maximized when the unit training overhead is designed as large as possible;3)however,the ASE-and-EE-optimal unit training overhead exists. It is a monotonically increasing function of the frame length and a monotonically decreasing function of the average signal-to-noise-ratio (in the high signal-to-noise-ratio region). Additionally,the optimal unit training overhead is smaller when communication nodes deploy particularly few or many antennas.

Active User and Data Detection for Uplink Grant-free NOMA Systems

This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.

A Novel Algorithm for DOA Estimation in Asynchronous DS-CDMA Systems

A novel algorithm for DOA estimation using linear antenna array in the asynchronous DS-CDMA systems is presented in the presence of channel mismatches or signal fading between the sensors. Code-matched filters and parallel MUSIC algorithms are proposed to overcome the restriction that the number of array elements must exceeds the number of the sources for subspace-based direction of arrival. It can be shown that multiple access interference throughout the code-matched filter is asymptotically Gaussian in the asynchronous DS-CDMA systems. A relevant performance analysis is derived. Computer simulation proved the efficiency of the proposed algorithm.

PERFORMANCE ANALYSIS OF THE MUSIC ALGORITHM IN THE PRESENCE OF AMPLITUDE AND PHASE ERROR

This paper presents the performance analysis of the MUSIC algorithm in the presence of channel amplitude and phase Error. Theoretical expression for the error of DOA estimating with MUSIC algorithm and Cramer-Rao bound are derived. It is compared with simulations performed for some representative cases. The results of theoretical expression and simulation show that existence of these errors will increase the error of DOA estimating and degrade its performance.

AN IMPROVED DOA ESTIMATION ALGORITHM FOR ASYNCHRONOUS MULTIPATH CDMA SYSTEM

This paper proposes an improved Direction Of Arrival(DOA) estimation algorithm for asynchronous multipath Code Division Multiple Access(CDMA) system. The algorithm is based on the correlation matrices of outputs of decorrelator, which is a Multi-User Detection(MUD) approach, one of the key techniques for CDMA system. Through decorrelating processing, the desired user's mulipath signals can be resolved and all the other resolved multipath signal interference is eliminated. So the proposed algorithm is expected to perform much better than algorithm such as that based directly on the Matched Filter(MF) bank outputs. Simulation results confirm this. While the improved algorithm performs better and better as Signal-to-Noise Ratio(SNR) increases, the performance of algorithm based directly on the MF bank outputs can not be improved.

双IRS辅助大规模MIMO系统中基于双时间尺度的信道估计方案

在双智能反射面(IRS)辅助的大规模多入多出(MIMO)通信系统中,为获取信道状态信息(CSI)而进行的信道估计需要较大的导频开销。考虑到基站(BS)-IRS信道与IRS-IRS信道具有高维且准静态的特性,而IRS-用户(UE)信道具有低维且时变的特性,本文提出一种基于双时间尺度的信道估计方案。首先,对于准静态信道,由BS发送导频并基于坐标下降算法和最小二乘法分别估计BS-IRS信道和IRS-IRS信道。然后根据准静态信道的估计结果进一步基于最小二乘法估计IRS-UE信道。仿真结果表明,本文所提出的方案可以采用低于对比方案50%的导频开销来获取低于对比方案近10 dB的估计归一化均方误差(NMSE),实现用更少的导频开销获取更高的信道估计精度。

IRS辅助MU-MISO系统中基于深度残差学习的信道估计

针对智能反射表面辅助多用户通信系统中传统信道估计方法性能下降的问题,将信道估计问题转化为信道去噪问题,利用深度残差学习方法学习残差噪声,从含噪导频信号中恢复信道系数。同时为提升信道估计精度,设计信道估计网络进一步提升去噪性能。网络主体包含两个模块:拼接信息保留模块将每一层卷积输出相融合,防止信道特征丢失,有效提取信道噪声的主体特征;扩张卷积稀疏模块通过扩大感受野范围获得信道的重要结构和细节特征,恢复信道噪声的边缘细节特征。仿真结果表明,归一化均方误差约等于0.45时,所提方法在不明显增加复杂度情况下,相比于线性最小均方误差算法获得3.7 dB的信噪比增益,更为接近最小均方误差信道估计器的性能,表现出了更好的性能和可用性。

基于压缩感知的智能反射面信道估计

针对智能反射面(intelligent reflecting surface,IRS)辅助的通信系统中稀疏度未知信道的估计问题,提出了一种基于压缩感知的稀疏自适应信道估计算法。首先,研究了正交匹配追踪(orthogonal matching pursuit,OMP)算法下信道的残差l2范数与输入的信道稀疏度之间的关系,得出了OMP算法恢复稀疏度未知信道的迭代终止条件;然后,提出了一种二阶段稀疏自适应信道估计算法,在第一阶段估计信道稀疏度,在第二阶段增加或删减支撑集原子,最终使得恢复的信道向量误差最小。仿真结果表明,与经典的最小二乘法、已知稀疏度的OMP算法、稀疏自适应匹配追踪(sparsity adaptive matching pursuit,SAMP)算法相比,提出的算法性能良好,鲁棒性强。

利用元学习算法的IRS-OTFS通信系统信道估计

针对高多普勒场景下智能反射表面(IRS)辅助多用户通信系统存在的信道估计传输开销大的问题,该文结合正交时频空间(OTFS)调制特点构造一种IRS-OTFS通信系统,充分发挥IRS和OTFS的性能优势,并在此基础上提出一种学习率自适应的模型无关元学习(MAML)算法。对IRS-OTFS多用户信道估计任务做离线训练,根据各任务的收敛速度自适应地调整学习率,防止训练失衡,并利用信道之间的相关性和MAML算法的少样本、泛化特性得到全局模型和适应性模型,快速学习新用户信道的传输特性,降低传输开销,提高信道估计准确性。理论分析和仿真结果表明,该算法在信道传输条件相同的情况下,将传输开销降低了大约50%,并相对于基准算法有4.8 dB左右的性能提升。

智能反射面辅助的OFDM系统稀疏信道估计研究

针对智能反射面(IRS)辅助的宽带正交频分复用(OFDM)系统的信道估计,当前大多数研究都是基于单符号全导频设置,且级联信道系数过多会导致导频开销较大。为此,提出了一种基于时域-角域块稀疏的两阶段信道估计方案。首先,通过分析信道在时域和角域上存在的共同稀疏特性,将级联信道矩阵转换为时域-角域上的块稀疏表示,并将信道估计问题转换为块稀疏矩阵恢复问题。其次,考虑传输导频限制和时域-角域块稀疏特性,提出了一种两阶段稀疏信道估计方案对级联信道进行稀疏恢复。仿真结果表明,相比另外三种基准方案,该方案可以使用较少的导频获得更优的信道估计性能,有效减少了导频开销,且估计准确度更高。

基于不动点深度学习的IRS辅助毫米波移动通信系统全信道估计

将智能反射面(IRS)与大规模MIMO结合能够保证和提高毫米波通信系统性能。针对基站(BS)-用户直连信道与用户-IRS-BS反射信道混叠场景,该文提出一种自适应的全信道估计方法。首先,引入辅助变量,采用原子范数将直连信道与反射信道的稀疏角度域子空间进行关联;然后,利用原子范数最小化将全信道估计问题建模为连续角度域稀疏矩阵重建规划;最后,基于不动点深度学习网络设计低复杂度的问题求解算法。该算法不仅能够克服传统基于模型解法中非线性估计算子对先验知识的依赖还可根据移动场景变化自适应调节算法复杂度。仿真结果表明,所提算法能够避免传统时分估计策略引起的差错传播效应,具有更高的估计精度和更低的复杂度。

基于压缩感知的STAR-RIS辅助毫米波系统信道估计

一种可同时透射和反射的可重构智能表面(simultaneously transmitting and reflecting reconfigurable intelligent surface,STAR-RIS)可以同时辅助位于其两侧用户的无线通信。针对STAR-RIS辅助下的双用户毫米波通信系统的上行链路信道估计问题进行了研究,并对两用户在角度域的联合稀疏特性进行了探究。基于级联信道的联合稀疏特性和压缩感知理论,将级联信道估计问题转化为稀疏信号恢复问题,并提出了一种位置判决正交匹配追踪(position-judgment orthogonal matching pursuit,PJ-OMP)算法以减少导频开销。仿真结果显示,与传统的正交匹配追踪(orthogonal matching pursuit,OMP)算法相比,所提出的算法能够减少信道估计的导频开销,即使在某一侧用户所分配的能量较少时也能够准确地估计信道。

一种深海多途相干信道下的高分辨DOA估计方法

针对基于平面波声场模型的深海DOA(Direction of Arrival,DOA)估计存在误差的问题,从射线理论出发,建立了多途信道下深海DOA估计的阵列信号模型,推导出了深海多径声线传播时间和相邻阵元声线传播时延差的表征方法,运用解相干DOA估计算法,提高深海DOA估计性能,通过仿真验证了算法的有效性。研究表明,深海DOA估计问题实为多维参数优化问题;充分考虑海洋声场的声传播特性,可以从根本上解决深海DOA估计的误差问题。