Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.

In this paper, a comprehensive and detailed uplink performance analysis is provided for cell-free massive multipleinput multiple-output orthogonal frequency division multiplexing (CF m-MIMO OFDM) systems, which consider the impact of multiple user equipment (UE) heterogeneous factors. This is the first performance analysis work on CF m-MIMO OFDM systems that simultaneously accounts for the heterogeneous mobility speed, activation probability and serving priority. Considering that UE's serving priority determines the amount of its allocated time-frequency resources, a novel closed-form expression of uplink spectral efficiency (SE) is derived by weighting each UE's SE based on its allocated time-frequency resources. The derived SE expression can quantify the impact of multiple UE heterogeneous factors on the uplink performance. Additionally, the SE performance analysis of local processing and fully centralized processing is also included for comparison. Simulation results show that CF m-MIMO OFDM systems under multiple UE heterogeneous factors outperform existing CF m-MIMO systems in terms of the 90 %-likely uplink SE, and allow a trade-off among fronthaul overhead, complexity and SE performance.

In this paper, a new waveform called discrete Fourier transform spread orthogonal frequency division multiplexing with chirp modulation (DFT-s-OFDM-CM) is proposed for the next generation of wireless communications. The information bits are conveyed by not only Q-ary constellation symbols but also the starting frequency of chirp signal. It could maintain the benefits provided by the chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), e.g., low peak-to-average power ratio (PAPR), full frequency diversity exploitation, etc. Simulation results confirm that the proposed DFT-s-OFDM-CM could achieve higher spectral efficiency while keeping the similar bit error rate (BER) to that of chirped DFT-s-OFDM. In addition, when maintaining the same spectral efficiency, the proposed DFT-s-OFDM-CM with the splitting of information bits into two streams enables the use of lower-order constellation modulation and offers greater resilience to noise, resulting in a lower BER than the chirped DFT-s-OFDM.

In this paper, a novel three-stage data-aided dual-space (DADS) (i.e., beamspace and signal subspace) channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems subject to pilot contamination. By exploiting the orthogonality of signal subspace, the non-overlapping interference caused by pilot contamination is identified and mitigated in the coarse channel estimate via subspace projection. Thanks to the independence of transmitted data between users, the overlapping interference is suppressed through alternating iterative refinement of the channel estimate and detected data. Additionally, to initially address the under-determined estimation problem arisen from hybrid beamforming (HBF) structures, an improved matching pursuit algorithm is proposed for coarse sparse beamspace channel estimation by appropriately selecting the scaling factor and adjusting the step size in a piecewise manner, followed by enhancement via subspace projection. Furthermore, by accurately detecting the overlap level of interference in the beamspace, the proposed channel estimation scheme selects an appropriate channel enhancement or refinement strategy to address both non-overlapping and overlapping interference subject to several typical channels without significantly increasing computational complexity. Simulation results demonstrate that the proposed channel estimation scheme achieves higher channel estimation accuracy and exhibits stronger resilience to both interference intensity and the number of interference compared to existing channel estimation schemes.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels.

In this paper, a novel dual-space (DS) two-stage channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems with pilot contamination. This is the first work in m-MIMO which takes into account both HBF and pilot contamination. The proposed channel estimation scheme consists of two stages. In Stage I, a coarse sparse channel estimation is conducted in the beamspace based on the proposed variable-stepsize adaptive matching pursuit (VS-AMP) algorithm, which enhances the channel recovery accuracy by carefully selecting and setting the scaling factor and step size. In Stage II, the impact of pilot contamination from interfering users is initially mitigated through the beamspace separability, while coarse channel estimate is further refined into a quasi-sparse format by subspace projection. Simulation results show that the proposed channel estimation scheme outperforms the state-of-the-art schemes in terms of normalized mean square error (NMSE) of channel estimation and bit error rate. The NMSE of the proposed channel estimation scheme also exhibits higher resilience to interference intensity while maintaining comparable computational complexity.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication networks. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the transmitted information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through quadrature amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block matching pursuit algorithm is proposed for accurate decoding by leveraging block-structured sparse prior information. Simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of packet error rate and spectral efficiency.

In this paper, a Turbo basis expansion modeling (BEM) orthogonal time frequency space (OTFS) scheme is proposed for high-mobility communications subject to either Doppler-shift or Doppler-spread channel. It consists of superimposed pilot power and BEM order optimization and Turbo BEM OTFS receiver. The signal-to-interference-and-noise ratio (SINR) is derived as functions of OTFS system parameters and several channel information (i.e., channel correlation matrix, noise variance, etc.). The optimal superimposed pilot power ratio is then derived with a closed-form solution by calculating the first derivative of derived SINR. BEM order is specially optimized for each channel, instead of simply increasing or reducing it. The Turbo BEM OTFS receiver is proposed with an exchange of soft information between BEM channel estimation, signal detection, and data decoding, leading to high reliability. By using superimposed pilots for initial BEM channel estimation, the proposed Turbo BEM OTFS scheme has zero dedicated pilot overhead, resulting in high spectral efficiency. Simulation results confirm that the proposed OTFS scheme outperforms the existing OTFS schemes in terms of bit error rate (BER) and spectral efficiency. Extrinsic information exchange transfer (EXIT) chart analysis and simulation results also exhibit the fast convergence speed of proposed OTFS scheme.

In this paper, a novel dual-rate orthogonal time frequency space (OTFS) system resilient to oscillator frequency offset (OFO) and Doppler spread is presented for high-mobility communications. To the best of the authors' knowledge, this is the first work in OTFS which introduces dual-rate frame and addresses both OFO and Doppler spread. By multiplexing low-rate data blocks at the front and back of OTFS frame, OFO is estimated without requiring extra pilots. Thanks to subspace-based algorithm, OFO is estimated with high accuracy, and the residual small OFO can be jointly estimated with Doppler-spread channel by utilizing a few pilots, without deteriorating performance. Simulation results verify the superior performance of the proposed dual-rate system over existing single-rate systems, in terms of bit error rate (BER), mean-square-error (MSE) of OFO estimation, and MSE of equivalent channel estimation, while featuring lower pilot overhead and power. The BER of the proposed system also approaches its lower bound, which assumes perfect estimation of OFO and Doppler-spread channel.

Orthogonal time frequency space (OTFS) modulation has become an effective technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) multi-input multi-output (MIMO) OTFS systems. Specifically, first, an iterative data-aided channel estimation scheme is proposed to accurately acquire UL channels with the aid of specifically designed frame pattern. Then the discrete prolate spheroidal basis expansion model (DPS-BEM) is used to model the time-varying UL channel estimates, and the dynamic DPS-BEM coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels for all antenna pairs and paths by iteratively extrapolating the fitting coefficients. Simulation results verify that the proposed scheme outperforms the existing schemes in terms of normalized mean square error of channel prediction and DL sum-rate.

In this paper, a near-optimal Karhunen-Loeve basis expansion modeling (KL-BEM) orthogonal time frequency space (OTFS) receiver with superimposed pilots has been proposed for high-mobility communications over time-varying channels with Doppler and delay spread. First, an initial KL-BEM channel estimation is conducted using superimposed pilots, followed by the removal of superimposed pilots from the received OTFS signal and equalization based on message passing (MP) algorithm. After that, the detected data symbols are utilized as pseudo pilots together with the superimposed pilots to refine both KL-BEM channel estimation and equalization in an iterative manner. Simulation results confirm the superior performance of the proposed KL-BEM OTFS receiver over prior art in terms of bit error rate (BER). The resulting BER performance is close to the lower bound obtained by assuming perfect channel estimation. Besides, the proposed scheme provides high spectral efficiency, while featuring fast convergence and affordable complexity.

In this paper, by utilizing the temporal correlation of wireless channels, a hierarchical basis expansion model (HBEM) based estimation scheme is proposed for orthogonal frequency division multiplexing systems over doubly selective channel, where the complex exponential basis expansion model (CE-BEM) is used to extract the channel impulse response and the discrete Legendre polynomials BEM is used to refine the CE-BEM coefficients to improve the performance of channel estimation. We design a non-periodic sparse pilot pattern, and hence only scarce subcarriers of a small number of pilots are required for channel estimation, resulting a training overhead reduction of around 50% over the previous CE-BEM based schemes. A block-based signal space matching pursuit algorithm is proposed to enhance the estimation accuracy of CE-BEM coefficients. Furthermore, the proposed HBEM scheme enables a reduction in the number of estimated CE-BEM coefficients by more than 50%, compared to the previous work. A lower bound on the mean square error (MSE) of the proposed HBEM scheme is derived. Simulation results show that the proposed HBEM scheme significantly outperforms the previous CE-BEM based schemes in terms of MSE of channel estimation and bit error rate.

In this paper, a new receiver design based on basis expansion model (BEM) orthogonal time frequency space (OTFS) is presented for high-mobility communications with Doppler-spread channel. By deriving an analytical BEM OTFS system model, a low-order generalized complex exponential BEM (GCE-BEM) aided rough channel estimation is proposed at the initial stage with low pilot overhead, followed by equalization. Then, the refinement of channel estimation and equalization is conducted iteratively, in which a high-resolution GCE-BEM model with a large BEM order is adopted and the detected data symbols are exploited as pseudo-pilots, leading to higher estimation accuracy. Simulation results show that the proposed BEM OTFS receiver significantly outperforms the existing OTFS receivers in terms of the mean square error (MSE) of channel estimation and bit error rate (BER), while featuring low pilot overhead. Results also show the near-optimal performance of the novel solution,i.e., achieved BER is very close to the case of perfect channel estimation. The theoretical lower bound on MSE of channel estimation is derived to verify the effectiveness of the proposed BEM OTFS receiver, which is shown to be close to simulation results.

We investigate a multi-device ultra-reliable low-latency communication system with heterogeneous traffic and finite block length over temporally-correlated fading channels. In light of the challenging demand for accurate channel estimation with limited pilot in a short frame, two frame structures, which respectively adopt independent pilots and shared pilot, are investigated. Block lengths and pilot lengths are jointly optimized for the two frame structures, through instantaneous channel state information (CSI) based dynamic optimization and statistical CSI based static optimization, to strike the tradeoffs among performance, complexity and signaling overhead. The proposed joint optimization algorithms significantly outperform the existing approaches that solely optimize block lengths or pilot lengths. The dynamic optimization algorithms achieve near-optimal performance at dramatic complexity reduction over exhaustive search, and maintain robustness against traffic heterogeneity. Also, the static optimization algorithms are conducted offline, while still outperforming the previous instantaneous CSI based dynamic optimization approaches. It is demonstrated that the independent-pilot frame structure with dynamic optimization is preferable in the scenario with high traffic heterogeneity or high mobility, and that the shared-pilot frame structure with static optimization presents a comparable performance to the former in the case of low mobility, incurring negligible complexity and signaling overhead.

In this paper, we investigate an open topic of a multiuser single-input-multiple-output (SIMO) generalized frequency division multiplexing (GFDM) system in the presence of carrier frequency offsets (CFOs) and in-phase/quadrature-phase (IQ) imbalances. A low-complexity semi-blind joint estimation scheme of multiple channels, CFOs and IQ imbalances is proposed. By utilizing the subspace approach, CFOs and channels corresponding to$U$users are first separated into$U$groups. For each individual user, CFO is extracted by minimizing the smallest eigenvalue whose corresponding eigenvector is utilized to estimate channel blindly. The IQ imbalance parameters are estimated jointly with channel ambiguities by very few subcarriers. The proposed scheme is feasible for a wider range of receive antennas number and has no constraints on the assignment scheme of subsymbols and subcarriers, modulation type, cyclic prefix length and the number of subsymbols per GFDM symbol. Simulation results show that the proposed scheme significantly outperforms the existing methods in terms of bit error rate, outage probability, mean-square-errors of CFO estimation, channel and IQ imbalance estimation, while at much higher spectral efficiency and lower computational complexity. The Cramér-Rao lower bound is derived to verify the effectiveness of the proposed scheme, which is shown to be close to simulation results.

Timely delivery of information plays an important role in time-sensitive applications like factory automation and monitoring. In this paper, we consider a dual-server short-block wireless communication system to ensure fresh information generated at relatively high update rate to be delivered to destination in real time, where the information is generated at a relatively high update rate, encoded into two short-block queues and delivered in two parallel paths in real time. The age of information (AoI) performance is investigated for the dual-queue system in the presence of block delivery errors. This is the first work to consider both multiple queues and block errors in AoI analysis. An expression for average AoI is derived, based on the Markov-chain process to enable low-complexity system design and optimization, and its correctness is verified by simulations. It is shown that the dual-queue system investigated significantly outperforms the single-queue system in terms of average AoI, peak AoI violation probability and throughput at a relatively high status update rate. The impacts of update rate and blocklength on average AoI are also investigated.

In this paper, we investigate the short frame structure optimization in terms of shared-pilot length and finite block length (FBL) for an industrial Internet-of-Things (IIoT) system with heterogeneous traffic requirements on latency, reliability and information size. Both dynamic and static optimization approaches are investigated to allow trade-offs between performance, complexity and signaling overhead. Effective throughput maximization problems are formulated based on statistical and instantaneous channel state information (CSI), respectively, and their monotonicities are proved. A statistical CSI based static joint block length and shared-pilot length (S-JBSPO) algorithm is proposed, which is conducted offline. With no spectral overhead and very low complexity, S-JBSPO outperforms the existing instantaneous CSI based approaches from medium to high SNR. An instantaneous CSI based dynamic JBSPO (D-JBSPO) algorithm is proposed, which maintains a near-optimal and robust throughput performance against a wide range of traffic requirements, and significantly outperforms the previous approaches, thanks to a much higher degree of freedom. D-JBSPO also demonstrates a significant performance gain over S-JBSPO, regardless of the Doppler frequency and the number of devices.

We propose an efficient enhanced K-means clustering (E-KMC) algorithm for semi-blind channel estimation of uplink cell-free massive multiple-input multiple-output (MIMO) systems in factory automation, an important application of the internet of things (IoT). The proposed E-KMC algorithm operates with significantly less clusters and complexity than the KMC algorithm while achieving enhanced bit error rate (BER) performance, as the latter converges extremely slowly even with just medium modulation order and a medium number of transmit antennas. A near-optimal short pilot is designed to assist clustering of the E-KMC based channel estimation scheme. The semi-blind receiver structure achieves a BER performance that is very close to the case with perfect channel state information (CSI), as well as a mean square error (MSE) of channel estimation that is very close to the theoretical lower bound derived in the paper. The proposed E-KMC based channel estimation scheme also significantly outperforms other types of semi-blind channel estimation approaches including second-and higher-order statistics based and machine learning based approaches, while at a much lower complexity. In addition, the E-KMC based channel estimation, is conducted at central processing unit (CPU) and avoids excessive fronthaul overhead due to exchange of the estimated CSI between access points (APs) and CPU.

Conventional request-grant based non-orthogonal multiple access (NOMA) incurs tremendous overhead and high latency. To enable grant-free access in NOMA systems, user activity detection (UAD) is essential. In this paper, we investigate compressive sensing (CS) aided UAD, by utilizing the property of quasi-time-invariant channel tap delays as the prior information. This does not require any prior knowledge of the number of active users like the previous approaches, and therefore is more practical. Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. They achieve much higher UAD accuracy than the previous work at low signal-to-noise ratio (SNR). Based on the UAD results, we also propose a low-complexity CS based channel estimation scheme, which achieves higher accuracy than the previous channel estimation approaches.

An interference alignment (IA) and independent component analysis (ICA)-based semiblind scheme, referred to as IA-ICA, is proposed for downlink dual-user power-domain nonorthogonal multiple access (NOMA) systems in high-reliability low-latency (HRLL) Internet of Things (IoT). At the base station (BS), one user is converted to constructive interference to the other user via phase alignment of each symbol. At both user ends, ICA is used for semiblind signal detection. The phase rotation via nonredundant precoding at the BS does not introduce any spectral overhead, while only 1-2 pilot symbols are required for elimination of ICA incurred ambiguity. Closed-form expressions are derived for the users' symbol error rate (SER) performance in Rayleigh fading with 4-quadrature amplitude modulation (4-QAM), which matches the simulation results very well. Based on the analytical results, we propose an efficient power allocation algorithm that is based on statistical channel state information (CSI) only, and therefore, the signaling overhead involved is negligible. In particular, a near-optimal SER performance can be achieved with equal power allocation between the two users. The proposed IA-ICA-based semiblind NOMA system demonstrates a much better SER performance than the existing approaches even though they are under perfect CSI. Hence, it is a feasible solution for HRLL IoT, with high reliability and very low spectral and signaling overheads.

In this paper, we maximize the system throughput of a point-to-point ultra-reliable low-latency communications (URLLC) system by jointly optimizing its block length and pilot length under the constraints of latency and block error probability. A finite block length (FBL) is adopted to enable low transmission latency. We prove that the throughput is approximately concave with respect to pilot length, given a block length, and that there exists a unique optimal block length in terms of throughput, with a given pilot length. Closed-form expressions are derived for the near-optimal pilot length with a given block length, as well as the asymptotic block error probability with respect to both block length and pilot length. A low-complexity iterative algorithm is proposed for joint optimization of block length and pilot length, which converges within only 1-3 iterations. Simulation results show that the proposed joint optimization scheme achieves a near-optimal throughput performance of an FBL URLLC system, with a much lower complexity than exhaustive search. It also significantly outperforms the previous approaches that considered either block length optimization or pilot length optimization only.

We propose a low-complexity semi-blind joint multi- carrier frequency offset (CFO) and multi-channel estimation scheme for uplink generalized frequency division multiple access (GFDMA) systems. To the best of our knowledge, this is the first work to investigate the estimation of both CFOs and channels for a wide range of GFDMA systems, allowing arbitrary carrier assignment, modulation type and cyclic prefix length, and a wide range of the number of receive antennas. Thanks to the orthogonality between noise subspace and each signal subspace of U users, a complex U-CFO and U-channel estimation problem is decomposed into 2U one-dimensional problems, and solved in a semi-blind manner. Also, the multi-CFO compensation is performed at receiver rather than transmitter, avoiding spectral overhead due to feedback of multiple CFOs. Simulation results show that the proposed scheme significantly outperforms the existing methods in terms of bit error rate (BER) and root-mean-square-errors (RMSEs) of CFO and channel estimation, at much lower computational complexity than the existing methods.

We propose a robust semi-blind estimation scheme of channel and carrier frequency offset (CFO) for generalized frequency division multiplexing (GFDM) systems. This, to the best of our knowledge, is the first work to propose an integral solution to channel and full-range CFO for a wide range of GFDM systems. Based on the derived equivalent system model with CFO included implicitly, a subspace based method is proposed to perform initial channel estimation blindly, which requires only a small number of received symbols to achieve the second order statistics of the received signal. Then, CFO estimation and channel ambiguity elimination are undertaken in series by utilizing a small number of nulls and pilots in a single sub-symbol. Both channel and CFO estimations are more robust against inter-carrier interference (ICI) and inter-symbol interference (ISI) caused by the nonorthogonal filter of GFDM, compared to the existing methods. The proposed scheme achieves a bit error rate (BER) performance close to the ideal case with perfect CFO and channel estimations especially at medium and high signal-to-noise-ratios (SNRs).

We investigate an amplify-and-forward (AF) full-duplex (FD) relay system, where the FD incurred self-interference (SI), through partial cancellation at relay, is treated as a useful source at destination to enhance degree of freedom in signal detection, while reducing the signal processing cost of SI cancellation. An independent component analysis (ICA) based equalization structure is employed at destination to separate and detect the desired signal from the residual SI in a semi-blind way. The mode of SI cancellation at relay is chosen adaptively based on the threshold of signal-to-interference ratio (SIR) at relay. The proposed FD relay system not only features reduced signal processing cost of SI cancellation, but also achieves much higher energy efficiency (EE) than conventional FD relay systems where SI is canceled as much as possible. Also, the proposed system enables full resource utilization via consecutive data transmission at all time and the same frequency, leading to much higher throughput and EE than the conventional time-splitting and power-splitting based SI recycling approaches that occupy partial resources. Last but not least, the proposed system demonstrates a bit error rate (BER) performance that is robust against a wide range of SI and close to the ideal case with perfect channel state information (CSI) and perfect SI cancellation, while requiring no training sequence for estimation of any channel involved.

Filtered-orthogonal frequency division multiplexing (F-OFDM) is a promising candidate waveform for the fifth generation (5G) wireless communications because of its high flexibility and low out-of-band emission (OOBE). However, it suffers from dramatic peak-to-average-power ratio (PAPR), which is higher than that of OFDM and results in the power amplifier (PA) not working in the high-efficiency region. We propose a hybrid PAPR reduction scheme including precoding, time-domain selected mapping (TSLM) and companding techniques, for F-OFDM systems with independent component analysis (ICA) based blind channel equalization, which can achieve significant PAPR reduction over the previous work. Also, this is the first work to reduce PAPR while enabling the PA to work with the highest possible efficiency. The reciprocal of the hybrid PAPR reduction is embedded in the ambiguity elimination process of ICA, and therefore does not require any dedicated side information from the transmitter or any exclusive signal processing at the receiver, leading to a much higher spectral efficiency (SE) and lower computational complexity than the previous work. The bit error rate (BER) performance of the system with the proposed hybrid PAPR reduction scheme is shown to be close to the ideal case with perfect channel state information (CSI), while no side information and training sequence are required for PAPR reduction and channel estimation, thanks to the effectiveness of the ICA based blind channel equalization.

We propose an iterative semi-blind (ISB) receiver structure to enable ultra-reliable low-latency communications in short-frame full-duplex (FD) systems with carrier frequency offset (CFO). To the best of our knowledge, this is the first paper to propose an integral solution to channel estimation and CFO estimation for short-frame FD systems by utilizing a single pilot. By deriving an equivalent system model with the CFO included implicitly, a subspace-based blind channel estimation is proposed at the initial stage, followed by CFO estimation and channel ambiguities elimination. Then, the refinement of channel and the CFO estimates is conducted iteratively. The integer and fractional parts of CFO in the full range are estimated as a whole and in closed-form at each iteration. The proposed ISB receiver significantly outperforms the previous methods in terms of frame error rate, mean square errors of channel estimation and CFO estimation and output signal-to-interference-and-noise ratio, while at a halved spectral overhead. Cramér-Rao lower bounds are derived to verify the effectiveness of the proposed ISB receiver structure. It also demonstrates high-computational efficiency as well as the fast convergence speed.

We propose an iterative semi-blind carrier frequency offset (CFO) estimation, self-interference (SI) cancelation and signal detection scheme for full-duplex (FD) orthogonal frequency division multiplexing (OFDM) systems. To the best of our knowledge, this is the first work to consider signal detection of FD systems in the presence of both CFO and SI. The CFO estimation, SI cancelation and signal detection are performed initially by a subspace based semi-blind method, which are then enhanced significantly by performing iterations among them. Its CFO compensation is performed on the desired signal estimate, avoiding the introduction of CFO to the SI. The pilots for the desired signal and SI are carefully designed to enable simultaneous transmission of them to achieve FD training mode. Simulation results show that, the proposed iterative scheme, with much lower training overhead, demonstrates a significant performance enhancement over the existing methods. By utilizing the second order statistics of the received signal, a much superior bit error rate (BER) performance can be achieved compared to the case with perfect SI cancelation and CFO compensation. Its output signal-to- interference-and-noise-ratio (SINR) is close to that with perfect SI cancelation, and robust against the input signal-to-interference ratio (SIR).

A joint multi-carrier frequency offset (CFO) and multi- time of arrival (TOA) estimation algorithm for multiuser single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. With carefully designed pilots, multiple CFOs and TOAs of K users are separated jointly, dividing a complex 2K-dimensional estimation problem into 2K low-complexity mono-dimensional estimation problems. Two CFO estimation approaches, including a low-complexity closed-form solution and a high-accuracy null-subcarrier assisted accurate estimation approach, are proposed, where the integer and fractional parts of each CFO are estimated as a whole rather separately. Each TOA is estimated regardless of CFO by exploring the features of the inter-carrier interference matrix. The Cramer-Rao lower bounds (CRLBs) of multi-CFO and mutli-TOA estimation are derived for the first time for SIMO OFDM systems. Simulation results show that the proposed CFO and TOA estimators provide higher estimation accuracy than the existing approaches. They also achieve performances close to the CRLBs especially at high signal-to-noise-ratios (SNRs).