User-Centric Cell-Free (UCCF) Wireless Systems: Principles and Optimization

User-centric cell-free (UCCF) wireless networks have a range of distinguished characteristics, which can be exploited for meeting some challenges that the conventional cellular systems are hard to. This chapter is devoted to delivering the fundamentals of wireless communications in UCCF systems, including channel modeling and estimation, uplink (UL) detection, downlink (DL) transmission, and resource optimization. Specifically, the advantages of cell-free networking are examined in contrast to the conventional celluar systems. The global and location-aware distributed UL detection are explored in the principles of minimum mean-square error (MMSE) and brief propagation. Correspondingly, the global and distributed DL transmission schemes are designed based on the MMSE precoding. The optimization of both UL and DL is analyzed with respect to system design and resource-allocation. Furthermore, some challenges for the implementation of UCCF systems in practice are identified and analyzed.

Optimization for MIMO Integrated Sensing and Communications

The fundamentals of MIMO communications and MIMO sensing are firstly analyzed with regard to channel and sensing capacities. It is shown that the different objectives of communications and sensing lead to different signaling waveforms required for achieving their capacities. Hence, the optimization of integrated sensing and communications (ISAC) is relied on a trade-off expected between the performance of communications and that of sensing. Following this observation, the design and resource optimization in general MIMO ISAC systems are discussed along with the analysis of some existing ISAC schemes. Furthermore, the design of ISAC in mmWave communications is addressed. Specifically, the principle of sensing in mmWave systems is established, and a range of optimization alternatives for ISAC design in mmWave systems are reviewed.

Principles and Optimization of Reflective Intelligent Surface Assisted mmWave Systems

A conceptual example is first analyzed to show that efficient wireless communications is possible, when user equipment (UE) receiver, BS transmitter or/and the scatter (reflector) in wireless channels employ the required channel state information (CSI) to remove the randomness of signal phase. Then, the principles and optimization of three reflective intelligent surface (RIS) assisted mmWave (RIS-mmWave) models are introduced. The first model assumes one BS, one RIS and one UE; the second one assumes one BS, one RIS and multiple UEs; while the third RIS-mmWave model assumes one BS, multiple RISs and multiple UEs. Furthermore, the optimization of BS precoder and RIS phase-shifts is addressed in the context of the massive RIS-mmWave scenarios, where the number of BS antennas and that of RIS reflection elements are significantly larger than the number of supported UEs. The analyses demonstrate that, while the deployment of RISs with mmWave is capable of solving the blockage problem and has the potential to significantly improve efficiency, finding the near-optimum solutions for RIS phase-shifts is highly challenging in practice.

Localization in Reconfigurable Intelligent Surface Aided mmWave Systems: A Multiple Measurement Vector Based Channel Estimation Method

The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS-assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.

Molecular Code-Division Multiple-Access: Signaling, Detection, and Performance

To accomplish relatively complex tasks, in Internet of Bio-Nano Things (IoBNT), information collected by different nano-machines (NMs) is usually sent via multiple-access channels to fusion centers (FCs) for further processing. Relying on two types of molecules, in this paper, a molecular code-division multiple-access (MoCDMA) scheme is designed for multiple NMs to simultaneously send information to an access-point (AP) in a diffusive molecular communications (DMC) environment. We assume that different NMs may have different distances from AP, which generates `near-far' effect. Correspondingly, the uniform and channel-inverse based molecular emission schemes are proposed for NMs to emit information molecules. To facilitate the design of different signal detection schemes, the received signals by AP are represented in different forms. Specifically, by considering the limited computational power of nano-machines, three low-complexity detectors are designed in the principles of matched-filtering (MF), zero-forcing (ZF), and minimum mean-square error (MMSE). The noise characteristics in MoCDMA systems and the complexity of various detection schemes are analyzed. The error performance of the MoCDMA systems with various molecular emission and detection schemes is demonstrated and compared. Our studies and performance results demonstrate that MoCDMA constitutes a promising scheme for supporting multiple-access transmission in DMC, while the channel-inverse based transmission can ensure the fairness of communication qualities (FoCQ) among different NMs. Furthermore, different detection schemes may be implemented to attain a good trade-off between implementation complexity and communication reliability.

Orthogonal Time-Frequency-Space (OTFS) and Related Signaling

The principle of orthogonal time-frequency-space (OTFS) signaling is firstly analyzed, followed by explaining that OTFS embeds another signaling scheme referred to as orthogonal short-time Fourier (OSTF). Then, the relationship among OTFS, OSTF, orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-division multiple-access (SC-FDMA) is explored, demonstrating that OSTF/OTFS are fundamentally the extensions of OFDM/SC-FDMA from one-dimensional (1D) signaling to two-dimensional (2D) signaling. Hence, the characteristics and performance of OSTF/OTFS schemes can be perceived from the well-understood OFDM/SC-FDMA schemes. Accordingly, the advantages and disadvantages of OSTF/OTFS are discussed. Furthermore, from the principles of OFDM/SC-FDMA, the multiuser multiplexing in OSTF/OTFS systems is analyzed with respect to uplink and downlink, respectively. Added on this, a range of generalized multiplexing schemes are presented, whose characteristics are briefly analyzed.

Intelligent Reflective Surface Assist Integrated Sensing and Wireless Power Transfer

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems.

Space-Time Shift Keying Aided OTFS Modulation for Orthogonal Multiple Access

Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) {and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.

Performance Analysis and Approximate Message Passing Detection of Orthogonal Time Sequency Multiplexing Modulation

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.