Site-Specific Online Compressive Beam Codebook Learning in mmWave Vehicular Communication

Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matrix with a sparse representation of the channel matrix. Due to the constraints imposed by analog processing, designing CS matrices that efficiently exploit the channel structure is, however, challenging. In this paper, we propose an end-to-end deep learning technique to design a structured CS matrix that is well suited to the underlying channel distribution, leveraging both sparsity and the particular spatial structure that appears in vehicular channels. The channel measurements acquired with the designed CS matrix are then used to predict the best beam for link configuration. Simulation results for vehicular communication channels indicate that our deep learning-based approach achieves better beam alignment than standard CS techniques that use the random phase shift-based design.

In millimeter wave (mmWave) vehicular communications, beam scanning is usually used for beam alignment. However, beam scanning takes a long time, which makes it unsuitable in the mmWave vehicular communications with high-speed nodes. Moreover, the existing research on mmWave vehicular communications is usually based on one-hop transmission. However, due to a limited transmission range of mmWave, in many cases, multi-hop transmissions are required to transmit information from a source node to a destination node. To solve these problems, we propose an opportunistic routing protocol based on position information for beam alignment in mmWave vehicular communications. Beam alignment is performed based on the location information of neighbor nodes instead of beam scanning, which avoids the increase of network load and delay caused by beam scanning. In addition, the interference in concurrent transmissions is considered in order to optimize beamwidth and forwarding priorities. When the opportunistic routing is used, even if one candidate forwarder (CF) fails to forward a packet, the other CFs receiving the packet can still forward it, avoiding an increase in the network load and unicast delay caused by a large number of retransmissions. In an experiment, we extend the existing one-hop communication protocols into multi-hop communication protocols. Compared with these protocols, the proposed protocol yields lower end-to-end delay, higher packet delivery ratio and network throughput.

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

Beam alignment (BA), a core component of beamforming, is essential for achieving optimal directional gain in high-frequency communications. However, in single base station (BS) scenarios, BA may fail when the line-of-sight (LoS) path is obstructed. Multi-BS cooperation can effectively address this issue by leveraging the LoS paths from other BSs. However, applying traditional single-BS BA schemes to multi-BS cooperation scenarios leads to a substantial training overhead. To address this, we propose a sensing-aided simultaneous multi-BS BA (SSMBA) strategy in this paper. In the first phase, compressed sensing (CS) is utilized to enable simultaneous multi-beam training across multiple BSs, significantly reducing training overhead. In the second phase, leveraging integrated sensing and communication (ISAC) capabilities, a sensing-aided beam refinement technique is introduced to further increase BA accuracy and beamforming gain. The simulation results demonstrate that the proposed SSMBA strategy has advantages in both reducing the training overhead and increasing beamforming gain.

Compressed sensing (CS) is a sparse signal sampling methodology for efficiently acquiring and reconstructing a signal from relatively few measurements. Recent work shows that CS is well-suited to be applied to problems in genomics, including probe design in microarrays, RNA interference (RNAi), and taxonomic assignment in metagenomics. The principle of using different CS recovery methods in these applications has thus been established, but a comprehensive study of using a wide range of CS methods has not been done. For each of these applications, we apply three hitherto unused CS methods, namely, l1-magic, CoSaMP, and l1-homotopy, in conjunction with CS measurement matrices such as randomly generated CS m matrix, Hamming matrix, and projective geometry-based matrix. We find that, in RNAi, the l1-magic (the standard package for l1 minimization) and l1-homotopy methods show significant reduction in reconstruction error compared to the baseline. In metagenomics, we find that l1-homotopy as well as CoSaMP estimate concentration with significantly reduced time when compared to the GPSR and WGSQuikr methods.

Millimeter-Wave (mmWave) communication has come into the spotlight as an enabling approach for next generation wireless networks. Communication at mmWave is however challenging due to the large path loss and limited power. This implies that antenna arrays with large directional gain are required both at the Base Station (BS) and the user sides. Finding the strongest narrow beam pair connecting the BS and the user is referred to as Beam Alignment (BA). In this paper, we propose an efficient BA scheme for multi-user systems via estimating the second order statistics of the channel. In the proposed scheme, the BS probes the channel in the downlink letting each user estimate its own channel, where all the users within the BS coverage are trained simultaneously. We formulate the channel estimation at the user side as a Compressed Sensing (CS) of a non- negative sparse vector and use the recently developed Non- Negative Least Squares (NNLS) technique to solve it efficiently. We evaluate our method via numerical simulations and compare it with other competitive algorithms. It has been verified that the proposed approach incurs less training overhead, exhibits higher efficiency in multi-user scenarios, and is highly robust to fast time- varying channels.

Compressive sensing is a simultaneous data acquisition and compression technique, which can significantly reduce data bandwidth, data storage volume, and power. We apply this technique for transient photometric events. In this work, we analyze the effect of noise on the detection of these events using compressive sensing (CS). We show numerical results on the impact of source and measurement noise on the reconstruction of transient photometric curves, generated due to gravitational microlensing events. In our work, we define source noise as background noise, or any inherent noise present in the sampling region of interest. For our models, measurement noise is defined as the noise present during data acquisition. These results can be generalized for any transient photometric CS measurements with source noise and CS data acquisition measurement noise. Our results show that the CS measurement matrix properties have an effect on CS reconstruction in the presence of source noise and measurement noise. We provide potential solutions for improving the performance by tuning some of the properties of the measurement matrices. For source noise applications, we show that choosing a measurement matrix with low mutual coherence can lower the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we show that by choosing a lower expected value of the binomial measurement matrix, we can lower the amount of error due to CS reconstruction.

Intelligent reflecting surface (IRS) has emerged as a competitive solution to address blockage issues in millimeter wave (mmWave) and Terahertz (THz) communications due to its capability of reshaping wireless transmission environments. Nevertheless, obtaining the channel state information of IRS-assisted systems is quite challenging because of the passive characteristics of the IRS. In this paper, we consider the problem of beam training/alignment for IRS-assisted downlink mmWave/THz systems, where a multi-antenna base station (BS) with a hybrid structure serves a single-antenna user aided by IRS. By exploiting the inherent sparse structure of the BS-IRS-user cascade channel, the beam training problem is formulated as a joint sparse sensing and phaseless estimation problem, which involves devising a sparse sensing matrix and developing an efficient estimation algorithm to identify the best beam alignment from compressive phaseless measurements. Theoretical analysis reveals that the proposed method can identify the best alignment with only a modest amount of training overhead. Simulation results show that, for both line-of-sight (LOS) and NLOS scenarios, the proposed method obtains a significant performance improvement over existing state-of-art methods. Notably, it can achieve performance close to that of the exhaustive beam search scheme, while reducing the training overhead by 95%.

We demonstrate an optical mixing system for measuring properties of sparse radio frequency (RF) signals using compressive sensing (CS). Two types of sparse RF signals are investigated: (1) a signal that consists of a few 0.4 ns pulses in a 26.8 ns window and (2) a signal that consists of a few sinusoids at different frequencies. The RF is modulated onto the intensity of a repetitively pulsed, wavelength-chirped optical field, and time-wavelength-space mapping is used to map the optical field onto a 118-pixel, one-dimensional spatial light modulator (SLM). The SLM pixels are programmed with a pseudo-random bit sequence (PRBS) to form one row of the CS measurement matrix, and the optical throughput is integrated with a photodiode to obtain one value of the CS measurement vector. Then the PRBS is changed to form the second row of the mixing matrix and a second value of the measurement vector is obtained. This process is performed 118 times so that we can vary the dimensions of the CS measurement matrix from 1×118 to 118×118 (square). We use the penalized ℓ(1) norm method with stopping parameter λ (also called basis pursuit denoising) to recover pulsed or sinusoidal RF signals as a function of the small dimension of the measurement matrix and stopping parameter. For a square matrix, we also find that penalized ℓ(1) norm recovery performs better than conventional recovery using matrix inversion.

An improved random equivalent sampling (RES) approach based on compressed sensing (CS) for repetitive ultrasonic signal detection is presented. The proposed system considers recovering ultrasonic signal with high equivalent sampling frequency from samples captured using analogue-to-digital converter (ADC) clocked at a rate much lower than Nyquist rate. A basis function is constructed to realise the ultrasonic signal sparse representation, which paves the way for applying CS theory to ultrasonic signal sub-Nyquist sampling. A sampling architecture is developed, which is applicable for ultrasonic compressive detection. The CS measurement matrix is constructed in the context of RES. Experimental results indicate that, with this sampling system, an ultrasonic signal sampled at ultra-low rate, but still can be recovered with overwhelming probability.

In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.

Non-orthogonal multiple access (NOMA) and mmWave are considered to be promising techniques for 5G and beyond cellular network communication systems. While the mmWave band provides a very wide under-utilized bandwidth, NOMA enhances the spectral efficiency compared to orthogonal multiple access (OMA). The combination of these two techniques could be considered as the key solution to the high required data rates in next-generation communication systems. The implementation of NOMA is studied and verified under an ideal condition with perfect knowledge of the channel state information (CSI) at the base station (BS). However, under the practical conditions, the fluctuation of the wireless channel makes perfect CSI unachievable at the BS. Hence, we proposed to use the angle of departure (AoD) as feedback information from UEs to the BS. We assume that, mobile users (UEs) perfectly estimate the channel by detecting the pilot signals. Then, UEs quantize the AoD and feed back to the BS. Finally, the BS uses the AoD to perform user clustering, power allocation and beamforming. To reduce the feedback overhead further, we proposed a user clustering algorithm which uses one-bit feedback to determine the change of the AoD. Numerical results demonstrate that the proposed NOMA system outperforms the conventional orthogonal multiple-access (OMA) system with the same amount of feedback information.

With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.

The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.

Optimizing spectral efficiency based on poisson queuing model for procuring cognitive intelligence in vehicular communication