Terahertz (THz) integrated sensing and communication (ISAC) enables simultaneous data transmission with Terabit-per-second (Tbps) rate and millimeter-level accurate sensing. To realize such a blueprint, ultra-massive antenna arrays with directional beamforming are used to compensate for severe path loss in the THz band. In this paper, the time-frequency-space transmit design is investigated for THz ISAC to generate time-varying scanning sensing beams and stable communication beams. Specifically, with the dynamic array-of-subarray (DAoSA) hybrid beamforming architecture and multi-carrier modulation, two ISAC hybrid precoding algorithms are proposed, namely, a vectorization (VEC) based algorithm that outperforms existing ISAC hybrid precoding methods and a low-complexity sensing codebook assisted (SCA) approach. Meanwhile, coupled with the transmit design, sensing algorithms are proposed to realize high-accuracy sensing, including a target discovery method, a wideband DAoSA MUSIC method for angle estimation and a sum-DFT-GSS approach for range and velocity estimation. Furthermore, to overcome the cyclic prefix limitation and Doppler effects, an inter-symbol interference- and inter-carrier interference-tackled sensing algorithm is developed. Numerical results indicate that the proposed sensing algorithms can realize centi-degree-level angle estimation accuracy and millimeter-level range estimation accuracy, which are one or two orders of magnitudes higher than existing methods in the millimeter-wave band.

Terahertz integrated sensing and communication (ISAC) is viewed as a game-changing technology to realize Terabit-per-second data rate and millimeter-level sensing in sixth-generation (6G) and beyond systems. To characterize the theo-retical performance of communication and sensing, an analytical framework for THz ISAC networks is developed by using the tool of stochastic geometry. In the presence of the blockage effects, channel fading and directional antenna radiation, the moment generating functions (MGFs) of the aggregated interference are derived. By utilizing the MGF of interference, the analytical expressions for coverage and capacity of THz ISAC networks are derived. Numerical results indicate that the coverage probabilities for THz ISAC networks are maximized at the access point (AP) density of 0.06 m-2, Moreover, it is verified that the fully-unified ISAC waveform is the most efficient scheme in contrast with frequency-division and time-division ISAC.

Providing links of rate over 100 Gbps and high energy density at the receiver, Terahertz (THz) simultaneous wireless information and power transfer (SWIPT) has huge potential to support the functionality of Internet of things (IoT) including distributed sensor networks and mobile robot systems. In this paper, a THz SWIPT system with ultra-massive MIMO (UM-MIMO) is proposed based on power splitting structure. By utilizing electromagnetic (EM) analysis, approximated closed-form expressions for energy distribution are derived to characterize the achievable rate and harvested energy of THz SWIPT system. Furthermore, the impact of finite-resolution phase shifter (FRPS) is analyzed, which cause the decline of energy density at the receiver and thus received power. Simulation results are provided to validate the derivations and demonstrate that decline of received power caused by FRPS can be locally compensated by increasing the inter-element spacing of antenna arrays. Moreover, compared with SWIPT at 1 THz and SWIPT at 28 GHz, SWIPT at 300 GHz balances the achievable rate and the harvested power, which allows the user to achieve rate of 200 Gbps and harvest power over 0.01 mW simultaneously.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the 10°3 BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise and multi-target estimation.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects, which are especially severe in the THz band. Moreover, peak-to-average power ratio (PAPR) degrades the THz power amplifier (PA) efficiency. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system with superimposed pilots is proposed to improve the robustness to Doppler effects and reduce PAPR for THz ISAC. Then, a two-phase sensing parameter estimation algorithm is developed to integrate sensing functionality into the DFT-s-OTFS waveform. Meanwhile, a low-complexity iterative channel estimation and data detection method with a conjugate gradient based equalizer is proposed to recover the data symbols of DFT-s-OTFS. The proposed DFT-s-OTFS waveform can improve the PA efficiency by 10% on average compared to OTFS. Simulation results demonstrate that the proposed two-phase sensing estimation algorithm for THz DFT-s-OTFS systems is able to realize millimeter-level range estimation accuracy and decimeter-per-second-level velocity estimation accuracy. Moreover, the effectiveness of the iterative method for data detection aided by superimposed pilots in DFT-s-OTFS systems is validated by the simulations and the bit error rate performance is not degraded by the Doppler effects.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising interdisciplinary technology that realizes simultaneously transmitting Terabit-per-second (Tbps) and millimeter-level accurate environment or human activity sensing. However, both communication performance and sensing accuracy are influenced by the Doppler effects and peak-to-average power ratio (PAPR), which are especially severe in the THz band. In this paper, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) system for THz ISAC is proposed with a two-stage sensing parameter estimation algorithm. The proposed sensing algorithm can realize millimeter-level range estimation accuracy and decimeter-per-second velocity estimation accuracy. Moreover, the proposed DFT-s-OTFS can improve the power amplifier efficiency by 10% on average compared to OTFS and enhance the sensing accuracy by one order of magnitude and the bit error rate performance by two orders of magnitude in high-mobility scenarios in contrast with orthogonal frequency division multiplexing (OFDM) and discrete Fourier transform spread OFDM (DFT-s-OFDM).

Terahertz (THz) band communication is a promising pillar technology to satisfy the demands of intelligent information society. The ultra-broad bandwidth in the THz band provides a great potential of a plethora of applications and services. However, THz wireless communication systems encounter stringent challenges, including more severe Doppler effects and more strict peak-to-average power ratio (PAPR) requirements. In this work, a discrete Fourier transform spread orthogonal time frequency space (DFT-s-OTFS) modulation scheme is proposed to address these issues of THz communications. The proposed DFT-s-OTFS can improve the bit error rate (BER) performance by two orders of magnitude compared to orthogonal frequency division multiplexing (OFDM) in presence of high Doppler spread, and reduce the PAPR by approximately 3 dB in contrast with OTFS.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. By integrating communication and sensing, the the THz joint communication and sensing (JCS) system can realize both unprecedented data rates and ubiquitously accurate sensing. However, THz JCS meets stringent challenges due to the peculiarities of THz channel and devices. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz JCS. The proposed system is able to provide lower peak-to-average power ratio than OFDM. Without compromising communication capabilities, SI-DFT-s-OFDM can realize millimeter-level range estimation accuracy and improve the velocity estimation accuracy by 10 times than OFDM. Finally, SI-DFT-s-OFDM achieves 18% improvement of data rate over DFT-s-OFDM.

Recently researchers have been attracted by the joint communication and sensing (JCS) techniques, due to their diverse benefits in communication-sensing applications. By sharing the spectrum and hardware components, the advantages of JCS systems include enhanced spectrum efficiency and reduced device costs. Following the trend of scaling up the carrier frequencies for 5G and beyond, Terahertz (THz) band is envisioned as a promising chunk of spectrum featuring multi-GHz bandwidth windows. However, despite the great promise, the waveform design for THz JCS is still not explored enough. In this work, the design guidelines for THz JCS waveform are presented. With the help of these guidelines, a non-uniform multi-wideband orthogonal frequency division multiplexing (NMW-OFDM) THz system is proposed to overcome the limitations of OFDM based JCS. The proposed NMW-OFDM system is able to realize sub-millimeter-level accuracy of the range estimation by using a multi-stage sensing algorithm, which is three orders of magnitude improvement compared to the OFDM-based JCS system. In addition, the accuracy of velocity estimation can be enhanced by 10 times within a short frame time. Finally, the system can achieve unprecedented communication data rate of 100 Gbps without sacrificing the maximum detectable distance.

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to multi-path fading and blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides multi-GHz ultra-broad bandwidth. Applying the THz spectrum to indoor localization, the channel state information (CSI) of THz signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Intra-Attention Bidirectional Recurrent (SIABR) deep learning method is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path rays are first analyzed in the recurrent neural network with the attention mechanism at the lower level. Furthermore, the upper-level residual network (ResNet) of the constructed SIABR network extracts hidden information to output the geometric coordinates. Simulation results demonstrate that the 3D localization accuracy in the metric of mean distance error is within 0.25m. The developed SIABR network has very fast convergence and is robust against THz indoor line-of-sight blockage, multi-path fading, channel sparsity and CSI estimation error.

Providing high-bandwidth and fast-speed links, wireless local area networks (WLANs) in the Terahertz (THz) band have huge potential for various bandwidth-intensive indoor applications. However, due to the specific phenomena in the THz band, including severe reflection loss, indoor blockage effects, multi-path fading, the analysis on the interference and coverage probability at a downlink is challenging. In this paper, indoor blockage effects caused by the walls and human bodies are analyzed. Next, a statistical THz channel model is proposed to characterize the THz indoor propagation. In light of these, the moment generating functions of the aggregated interference and theoretical expressions for the mean interference power are derived. As a result, the approximated coverage probability and average network throughput are derived. Extensive numerical results show that for the nearest access point (nearest-AP) user association scheme, the optimal AP density is 0.15/m2, which results in the coverage probability reaches 93% and the average network throughput is 30 Gbps/m2. In addition, by adopting a novel line-of-sight access point (LoS-AP) user association mechanism, the coverage probability and the average network throughput can be further improved by 3 percent and 2 Gbps/m2, respectively.

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to the shadowing, multi-path fading, blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides ultra-broad bandwidth for indoor applications. Applying to indoor localization, the channel state information (CSI) of THz wireless signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Bidirectional Long Short-term Memory (SBi-LSTM) recurrent neural network (RNN) architecture is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path ray are first analyzed in the Bi-LSTM network at the base level. Furthermore, the upper level residual network (ResNet) of the constructed SBi-LSTM network extracts for the geometric coordinates. Simulation results validate the convergence of our SBi-LSTM method and the robustness against indoor non-line-of-sight (NLoS) blockage. Specifically, the localization accuracy in the metric of mean distance error is within 0.27m under the NLoS environment, which demonstrates 60% enhancement over the state-of-the-art techniques.