Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods

Advanced DSP Algorithms For Modern Wireless Communication Transceivers

A higher network throughput, a minimized delay and reliable communications are some of many goals that wireless communication standards, such as the fifthgeneration (5G) standard and beyond, intend to guarantee for its customers. Hence, many key innovations are currently being proposed and investigated by researchers in the academic and industry circles to fulfill these goals. This dissertation investigates some of the proposed techniques that aim at increasing the spectral efficiency, enhancing the energy efficiency, and enabling low latency wireless communications systems. The contributions lay in the evaluation of the performance of several proposed receiver architectures as well as proposing novel digital signal processing (DSP) algorithms to enhance the performance of radio transceivers. Particularly, the effects of several radio frequency (RF) impairments on the functionality of a new class of wireless transceivers, the full-duplex transceivers, are thoroughly investigated. These transceivers are then designed to operate in a relaying scenario, where relay selection and beamforming are applied in a relaying network to increase its spectral efficiency. The dissertation then investigates the use of greedy algorithms in recovering orthogonal frequency division multiplexing (OFDM) signals by using sparse equalizers, which carry out the equalization in a more efficient manner when the low-complexity single tap OFDM equalizer can no longer recover the received signal due to severe interferences. The proposed sparse equalizers are shown to perform close to conventional optimal and dense equalizers when the OFDM signals are impaired by interferences caused by the insertion of an insufficient cyclic prefix and RF impairments

Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various...Comment: double column, 58 page

Fast channel estimation in the transformed spatial domain for analog millimeter wave systems

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typically involves an initial beam training process where a subset of the possible beam directions at the transmitter and receiver is scanned along a predefined codebook. Unfortunately, the high number of transmit and receive antennas deployed in mmWave systems increase the complexity of the beam selection and channel estimation tasks. In this work, we tackle the channel estimation problem in analog systems from a different perspective than used by previous works. In particular, we propose to move the channel estimation problem from the angular domain into the transformed spatial domain, in which estimating the angles of arrivals and departures corresponds to estimating the angular frequencies of paths constituting the mmWave channel. The proposed approach, referred to as transformed spatial domain channel estimation (TSDCE) algorithm, exhibits robustness to additive white Gaussian noise by combining low-rank approximations and sample autocorrelation functions for each path in the transformed spatial domain. Numerical results evaluate the mean square error of the channel estimation and the direction of arrival estimation capability. TSDCE significantly reduces the first, while exhibiting a remarkably low computational complexity compared with well-known benchmarking schemes

An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing

Joint communication and radar sensing (JCR) represents an emerging research field aiming to integrate the above two functionalities into a single system, by sharing the majority of hardware, signal processing modules and, in a typical case, the transmitted signal. The close cooperation of the communication and sensing functions can enable significant improvement of spectrum efficiency, reduction of device size, cost and power consumption, and improvement of performance of both functions. Advanced signal processing techniques are critical for making the integration efficient, from transmission signal design to receiver processing. This paper provides a comprehensive overview of the state-of-the-art on JCR systems from the signal processing perspective. A balanced coverage on both transmitter and receiver is provided for three types of JCR systems, namely, communication-centric, radar-centric, and joint design and optimization

User Grouping and Resource Allocation for Joint Communication and Positioning in mmWave Multi-cell Networks

B5G/6G expands a new application scenario of joint communication and positioning which can simultaneously provide high-quality communication and positioning services. Millimeter wave (mmWave) and massive Multiple Input Multiple Output (MIMO) can help systems to achieve high-quality communication and generate high-directional beams to assist positioning. In this paper, the proposed Structured Perturbed Orthogonal Matching Pursuit (SPOMP) could alleviate the pilot pollution in massive MIMO systems and break the resolution of angular estimation. Based on the above super-resolution estimation, we develop a dynamic two-stage multi-cell user grouping scheme to reduce interference and improve resource utilization. Combined with the grouping results and our derived performance metric, a joint optimization problem for power and bandwidth allocation is proposed to maximize the comprehensive performance of joint communication and positioning while guaranteeing performance bounds. An effective cyclic iteration algorithm based on Alternating Direction Method of Multipliers (ADMM) is present to solve the proposed optimization problem. Numerical results show that the proposed joint user grouping and resource allocation scheme achieves a larger rate-accuracy region and a better multi-cell service performance balance compared to conventional schemes

Massive MIMO 시스템을 위한 채널 추정 및 피드백 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 이정우.To meet the demand of high throughput in next generation wireless systems, various directions for physical layer evolution are being explored. Massive multiple-input multiple-output (MIMO) systems, characterized by a large number of antennas at the transmitter, are expected to become a key enabler for spectral efficiency improvement. In massive MIMO systems, thanks to the orthogonality between different users' channels, high spectral and energy efficiency can be achieved through simple signal processing techniques. However, to get such advantages, accurate channel state information (CSI) needs to be available, and acquiring CSI in massive MIMO systems is challenging due to the increased channel dimension. In frequency division duplexing (FDD) systems, where CSI at the transmitter is achieved through downlink training and uplink feedback, the overhead for the training and feedback increases proportionally to the number of antennas, and the resource for data transmission becomes scarce in massive MIMO systems. In time division duplexing (TDD) systems, where the channel reciprocity holds and the downlink CSI can be obtained through uplink training, pilot contamination due to correlated pilots becomes a performance bottleneck when the number of antennas increases. In this dissertation, I propose efficient CSI acquisition techniques for various massive MIMO systems. First, I develop a downlink training technique for FDD massive MIMO systems, which estimates the downlink channel with small overhead. To this end, compressed sensing tools are utilized, and the training overhead can be highly reduced by exploiting the previous channel information. Next, a limited feedback scheme is developed for FDD massive MIMO systems. The proposed scheme reduces the feedback overhead using a dimension reduction technique that exploits spatial and temporal correlation of the channel. Lastly, I analyze the effect of pilot contamination, which has been regarded as a performance bottleneck in multi-cell massive MIMO systems, and propose two uplink training strategies. An iterative pilot design scheme is developed for small networks, and a scalable training framework is also proposed for networks with many cells.1 Introduction 1 1.1 Massive MIMO 1 1.2 CSI Acquisition in Massive MIMO Systems 3 1.3 Contributions and Organization 6 1.4 Notations 7 2 Compressed Sensing-Aided Downlink Training 9 2.1 Introduction 10 2.2 System Model 13 2.2.1 Channel Model 13 2.2.2 Downlink Channel Estimation 16 2.3 CS-Aided Channel Training 19 2.3.1 Training Sequence Design 20 2.3.2 Channel Estimation 21 2.3.3 Estimation Error 23 2.4 Discussions 26 2.4.1 Design of Measurement Matrix 26 2.4.2 Extension to MIMO Systems 27 2.4.3 Comparison to CS with Partial Support Information 28 2.5 Simulation Results 29 2.6 Conclusion 37 3 Projection-Based Differential Feedback 39 3.1 Introduction 40 3.2 System Model 44 3.2.1 Multi-User Beamforming with Limited Feedback 45 3.2.2 Massive MIMO Channel 47 3.3 Projection-Based Differential Feedback 48 3.3.1 Projection-Based Differential Feedback Framework 48 3.3.2 Projection for PBDF Framework 51 3.3.3 Efficient Algorithm 57 3.4 Discussions 58 3.4.1 Projection with Imperfect CSIR 58 3.4.2 Acquisition of Channel Statistics 61 3.5 Simulation Results 62 3.6 Conclusion 69 4 Mitigating Pilot Contamination via Pilot Design 71 4.1 Introduction 72 4.2 System Model 73 4.2.1 Multi-cell Massive MIMO Systems 74 4.2.2 Uplink Channel Training 75 4.2.3 Data Transmission 77 4.3 Iterative Pilot Design Algorithm 78 4.3.1 Algorithm 79 4.3.2 Proof of Convergence 81 4.4 Generalized Pilot Reuse 81 4.4.1 Concept of Pilot Reuse Schemes 81 4.4.2 Pilot Design based on Grassmannian Subspace Packing 82 4.5 Simulation Results 85 4.5.1 Iterative Pilot Design 85 4.5.2 Generalized Pilot Reuse 87 4.6 Conclusion 89 5 Conclusion 91 5.1 Summary 91 5.2 Future Directions 93 Bibliography 96 Abstract (In Korean) 109Docto

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques

Millimeter wave link configuration in practical scenarios

Acquiring channel state information (CSI) for link configuration in wideband millimeter wave (mmWave) massive multiple-input-multiple-output (MIMO) systems with hybrid architectures is challenging, due to the high dimensions of the channel matrices, the low signal-to-noise ratio (SNR) before beamforming, the various hardware constraints and the high mobility in the vehicular context. Previous work in this area exploits channel sparsity, statistical priors or side information to reduce the overhead associated to initial channel estimation or channel tracking. These works consider, however, a system model that neglects hardware imperfections. In addition, many of the proposed solutions are unable to operate in some realistic scenarios, such as vehicle-to-everything (V2X) communications. In this dissertation, we develop new signal processing solutions that can enable low-overhead mmWave link configuration under various disturbances and practical limitations, e.g., hardware impairments, calibration errors, beam squint effect, channel blockage, high mobility, to name a few. In the first part of this dissertation, we focus on the problem of wideband channel estimation for mmWave MIMO systems with different hardware imperfections. We first design a dictionary learning aided channel estimation strategy for wideband mmWave MIMO systems by explicitly considering the hardware uncertainties and calibration errors, and then derive algorithms that learn the optimal sparsifying dictionaries for channel representation and estimation. In a second contribution of this part, we further develop a dictionary learning aided compressive channel estimation scheme for mmWave MIMO systems by incorporating beam squint into the model of array responses. Numerical results show the proposed solutions can adapt to the practical scenarios and help reduce the overhead associated with channel estimation significantly. In the second part of this dissertation, we deal with the problem of wideband channel tracking for mmWave MIMO systems with or without the impact of blockage. We first introduce statistical channel models that include the evolution models for channel gains and angles of arrival/departure, as well as the statistics of blockage events. Then, we design novel blockage detection schemes and efficient Bayesian channel tracking algorithms to facilitate the low-overhead tracking with or without blockage. Numerical results corroborate that the proposed solutions achieve better channel tracking performance even in mobile scenarios that suffer from highly dynamic blockage events.Electrical and Computer Engineerin