This paper incorporates a low-complexity precoding scheme, termed partial zero-forcing (PZF), for an orthogonal frequency time space (OTFS) modulated cell-free massive multiple input multiple output (mMIMO) integrated sensing and communication (ISAC) system. This scheme assigns ZF precoding and maximal ratio transmission (MRT) to communication users based on their channel gain. This approach addresses the computational complexity of global ZF operations and the inability of MRT precoding to mitigate inter-user interference. In addition, we introduce the formulation of the mean-to-average-sidelobe-ratio (MASR) metric for sensing within this system. We derive novel closed-form spectral efficiency (SE) expressions and compute the MASR metric to evaluate sensing performance. The results demonstrate that PZF outperforms MRT and full-pilot ZF (FZF) in terms of SE and computational complexity, providing a scalable solution for high mobility beyond 5G applications. We also evaluate how downlink power allocation at the AP influences communication and sensing performance, using closed-form SE per user and MASR as metrics.

The physical layer in a cellular network base station typically runs on a field programmable gate array (FPGA) to enable reconfigurability and adaptability to meet future demands of the network with sufficient computational power. This work proposes a novel architecture and algorithms for the FPGA implementation of the fifth-generation (5G) new radio (NR) physical downlink control channel (PDCCH) using the 3GPP procedures for downlink control information (DCI) processing, including sub-block interleaver, bit selection, scrambling, golden sequence generation, and modulation. The proposed algorithms are optimized to meet 5G-NR frame timings for DCI processing with minimum power consumption and hardware resource utilization. We perform rigorous testing including all corner cases to benchmark our designs in i) standalone mode, and ii) integrated fashion for the end-to-end PDCCH processing. The PDCCH implementation, with maximum DCI payload, using the proposed architecture and algorithms achieves$1.5 \mu s$latency,$1.98\%$hardware resource utilization,$1.2$Gbps throughput and$10$Gbps/W power efficiency. With a subcarrier spacing$\Delta f=30$KHz, it can multiplex DCI to schedule up to$20$and$40$users for one orthogonal frequency division multiplexing (OFDM) and two OFDM symbols long PDCCH respectively.

Orthogonal time-frequency space (OTFS) transmission is a promising multi-carrier modulation scheme for high-mobility communication scenarios. We consider a novel multiple frame-based superimposed pilot (SP)-OTFS system, which exhibits two-dimensional (2-D) delay-Doppler domain channel sparsity. We design a multiple-frame-based coupled prior sparse Bayesian learning (M-CPSBL) algorithm to exploit the 2-D channel sparsity while estimating it. This algorithm, however, inverts a large-dimensional matrix in each iteration with a high complexity. We next design its low-complexity version, which works on a smaller dimensional problem. The two proposed algorithms are used for SP-OTFS systems, which superpose low-powered pilots on to data signals. The combination of M-CPSBL and SP-OTFS frameworks is shown to outperform existing OTFS systems in terms of channel estimation normalized mean squared error, and spectral efficiency (SE). We derive a closed-form expression to optimize pilot and data powers to maximize the signal-to-interference-plus-noise ratio of SP-OTFS systems. We numerically show that optimal power allocation reduces the bit error rate, and increases the SE.

Dynamic Spectrum Sharing (DSS) enables fourth-generation long-term evolution (4G-LTE) and fifth-generation new radio ($5 \mathrm{G}-\mathrm{NR}$) technologies to coexist within the same frequency bands, facilitating efficient spectrum utilization. This paper presents a novel approach leveraging cell-specific reference signals (CRS) 4G-LTE for 5G-NR channel measurements, which allows 5G-NR channel state information reference signals (CSIRS) for data transmission. By repurposing LTE CRS for NR measurements and utilizing NR CSI-RS resource elements (REs) for data transmission, the proposed method enhances the throughput of the NR network without compromising LTE operations. i.e., it does not demand any extra REs from LTE for NR data transmission. The simulation results demonstrate improvements in data throughput, validating the feasibility and benefits of the proposed approach over the traditional methods in which LTE and NR use separate reference signals for their measurements.

This study focuses on designing a 3GPP-compliant channel deinterleaver for the 5G new radio (NR) physical uplink control channel (PUCCH) for field programmable gate array (FPGA). The PUCCH transmit chain employs a complex triangular structure for channel interleaving which typically requires nested ‘for’ loops, while the channel deinterleaver in the receive chain has to perform the reverse operation using LLRs, which requires handling multiple bits, thereby increasing complexity. We present a novel algorithm for FPGA implementation of the channel deinterleaver with reduced complexity by replacing the nested ‘for’ loop with a single ‘for’ loop and conditional variable increment. Our design is benchmarked using a 2-tier testing strategy with the Matlab 5G NR toolbox, with test vectors covering all configurations. Our FPGA implementation demonstrates that the designed channel deinterleaver completes its operation within a slot duration of 5G NR with minimal resource utilization.

Orthogonal time frequency space (OTFS) waveform provides resilience against doubly dispersive wireless channel resulting in high mobility application. This paper amalgamates OTFS waveform with full-duplex (FD) distributed massive multi-ple input multiple output (mMIMO) communications to achieve high throughput gain in high mobility applications. In particular, we consider an OTFS-aided FD cell-free mMIMO system, where FD access points (APs) simultaneously serve half-duplex (HD) uplink (UL) and downlink (DL) users. We derive novel closed-form spectral efficiency (SE) expressions for both UL and DL in the existence of channel estimation errors. Our simulations validate the correctness of the derived closed-form SE expressions and highlight the impact of velocity on sum SE for two different vehicular models. The simulation results also demonstrate the SE improvement of the FD cell-free mMIMO-OTFS system over its HD counterpart.

The integration of orthogonal time frequency and space modulation technique (OTFS) and reconfigurable intel-ligent surfaces (RIS) provides a promising solution for better connectivity in high mobility scenarios. Physical layer security (PLS) by exploiting channel characteristics has recently emerged as a promising security solution. We propose a two-fold PLS scheme for communication via RIS using OTFS waveform. As a first fold of security, we design a channel-based precoder for rotating delay-Doppler (DD) information symbols of a legitimate user. Subsequently, an artificial noise (AN) is added with the precoded DD symbols to enable another layer of security. To project the AN onto the channel subspace of a legitimate user and to mitigate its effect on the legitimate user's receiver, we derive a projection matrix by using the superposition of direct and cascaded channels. The strongest channel maximization based phase optimization scheme is also designed. Our results show that, unlike the legitimate user, the eavesdropper is not able to decode the information at all. The simulation results demonstrate the improved bit-error-rate (BER) of the proposed phase optimization technique over the random phase selection and OTFS system without RIS.

Modulation recognition is an integral part of many communication systems. For example, it is widely used in cooperative communication, and stealthy decoding a signal in non-cooperative communication. This work proposes a modulation recognition method using unsupervised learning aided clustering analysis of constellations in single-user (SU) and multi-user (MU) scenarios. The idea is to pose modulation classification as a pattern recognition problem in the I/Q plane, and then design modulation classifiers by exploiting patterns formed by the constellations. The proposed algorithm classifies SU versus MU mode of communication in the first step, followed by recognising modulation used by the users. Simulation results show that the designed method achieves more than 90% average accuracy of correct classification above 8 dB SNR, and significantly outperforms the existing methods.

We propose a novel coupled prior-based sparse Bayesian framework for superimposed pilot (SP)-aided OTFS systems. The SP-OTFS system, wherein low-powered pilot signals are superimposed on to data signals, reduces the large pilot overhead, which is common in the existing OTFS systems. In the proposed coupled prior, sparsity of each channel gain is controlled not only by its own sparsity, but by also that of all other channel gains. OTFS channel has an inherent two-dimensional sparsity which can be exploited to increase its estimation accuracy. Using the proposed prior, we design a multiple received frame-based coupled prior sparse Bayesian learning (M-CPSBL) OTFS channel estimation algorithm, which is shown to outperform its state-of-the-art counterparts.

5G new radio (NR) distinguishes itself by offering remarkable features such as significantly higher throughput and lower latency compared to 4G. In 5G, low density parity check (LDPC) encoding is employed as the channel coding scheme for the transmission of data bits. Subsequent to the LDPC encoding stage, bit selection and bit interleaving operations are performed. Rate matching plays an important role in selecting specific encoded bits for transmission, utilizing techniques like shortening and repetition to support hybrid automatic repeat request functionality. To address the issue of latency encountered in processing large transport blocks, we propose parallel algorithms for the rate matching. These algorithms are implemented on field programmable gate arrays, and then the performance of the optimized algorithms is compared with the existing algorithms specified in the 3rd generation partnership project standards.

This work focuses on the FPGA implementation of the 3GPP complaint polar encoder for 5G-new radio (NR). We use Xilinx's polar intellectual property (IP) aka polar IP for the polar encoding functionality. We design a polar configuration IP by generating the bit allocation (BA) table, configuring register values and passing the right control parameters. Next, a parameter handler is developed which is a memory mapped module and provides an advanced extensible interface (AXI)-lite interface to the polar IP. To test the functionality of the polar IP, a Verilog test bench is developed. It provides configuration parameters, payload and BA table in compliance with 3GPP standards to the polar configuration IP. We verify the output of our implementation with the Matlab 5G-NR built-in library on the Xilinx evaluation board. Our results show the efficiency of the implementation in terms of latency and resource utilization on the evaluation board.

In this paper, single-input single-output filter bank multicarrier (FBMC) waveform based on offset quadrature amplitude modulation (OQAM) is investigated in conjunction with intelligent reflecting surface (IRS). A frame structure for IRS assisted FBMC waveform is designed for channel frequency response (CFR) estimation, followed by IRS reflecting coefficients optimization. In particular, an ON/OFF channel estimation technique is proposed by inserting guard symbols between the adjacent training symbols. The guard symbols are utilized to mitigate the inter-symbol-interference between the adjacent training symbols, and to help in calculating the inherent intrinsic interference in FBMC waveform. We next investigate a random phase initialization based successive convex approximation technique to jointly optimize the IRS reflecting coefficients and sub-carriers transmit power allocation using both perfect and imperfect CFRs. Our simulation results demonstrate the accuracy of proposed CFR estimation and reflecting coefficient optimization schemes, and the effect of the guard symbols on their performances.

Orthogonal time frequency space (OTFS) modulation establishes reliable communication over highly time-varying wireless channels. This work designs a low-complexity linear minimum mean square error (LMMSE) receiver for practical pulse-shaped multiple-input multiple-output (MIMO)-OTFS systems. The proposed design reduces complexity by exploiting inherent channel sparsity and channel-agnostic structure of matrices involved in the LMMSE receiver. The proposed design, with log-linear complexity order, does not make any approximation, and provides exactly the same solution, and consequently the same bit error rate, as that of the conventional LMMSE receiver, which has a cubic complexity order.

Sparse Bayesian learning (SBL)-based channel state information (CSI) estimation schemes are developed for filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). Initially, an SBL-based channel estimation scheme is designed for a frequency-selective quasi-static single-input single-output (SISO)-FBMC system, relying on the interference approximation method (IAM). The IAM technique, although has low complexity, is only suitable for channels exhibiting mild frequency-selectivity. Hence, an alternative time-domain (TD) model based sparse channel estimation framework is developed for highly frequency-selective channels. Subsequently, the Kalman filtering (KF)-based IAM and its TD counterpart are developed for sparse doubly-selective CSI estimation in SISO-FBMC systems. These schemes are also extended to FBMC-based multiple-input multiple-output (MIMO) systems, for both quasi-static and doubly-selective channels, after demonstrating the special block and group-sparse structures of the IAM and TD-based models respectively, which are the characteristic features of such channels. The Bayesian Cramér-Rao lower bounds (BCRLBs) and the time-recursive BCRLBs are derived for the proposed quasi-static as well as doubly-selective sparse CSI estimation models, respectively. Our numerical results closely match the analytical findings, demonstrating the enhanced performance of the proposed schemes over the existing techniques.

Orthogonal time frequency space modulation (OTFS) scheme establishes reliable communication in a rapidly time-varying wireless channel with a high Doppler spread. We design a low-complexity linear minimum mean squared error (LMMSE) receiver for practical-pulse-shaped multiple-input multiple-output (MIMO)-OTFS systems. The proposed receiver exploits the inherent channel sparsity and the channel-agnostic structure of matrices involved in the LMMSE receiver, and has only a log-linear complexity. It provides exactly the same solution, and hence the same bit error rate (BER), as that of the conventional LMMSE receiver with a cubic order of complexity. We also derive, by using the Taylor series expansion and the results from random matrix theory, a tight closed-form approximation for the post-processing signal-to-noise-plus-interference ratio (SINR) expression of the proposed receiver. This expression is derived by assuming imperfect receive channel state information. We show using extensive numerical investigations that the derived SINR expression, when averaged over multiple channel realizations, accurately characterizes the BER of a MIMO-OTFS system.

We derive closed form bit error rate (BER) expression for orthogonal time frequency space (OTFS) zero-forcing (ZF) receiver with perfect and imperfect receive channel information. Depending on the delay-Doppler locations of the propagation paths of the OTFS channel H, the expression$\mathrm {H}^{H}\mathrm {H}$is shown to have either distinct or repetitive eigenvalues. When$\mathrm {H}^{H}\mathrm {H}$has two or less distinct eigenvalues and the remaining ones are repetitive, we derive the probability distribution function (pdf) of the signal-to-noise-plus-interference-ratio (SINR) of the ZF receiver. A closed form BER expression is then derived by averaging the conditional BER over the SINR pdf. When$\mathrm {H}^{H}\mathrm {H}$has$n$distinct eigenvalues, we use numerical integration to derive a generalized expression for the SINR pdf. We show that this pdf can be tightly approximated by the Gamma pdf, and then use it to derive the BER expression. The derived OTFS ZF BER expression therefore, unlike the existing ones in the literature, does not require averaging over multiple channel realizations. We show, for different modulation schemes and OTFS system parameters, that the BER calculated using the derived expressions closely matches the one calculated numerically.

We propose a superimposed pilot (SP)-based channel estimation and data detection framework for orthogonal time-frequency space (OTFS) systems, which superimposes low-powered pilots on to data symbols in the delay-Doppler domain. We propose two channel estimation and data detection designs for SP-OTFS systems which, unlike the existing OTFS designs, do not designate any slots for pilots, and consequently have higher spectral efficiency (SE). The first SP design estimates channel by treating data as interference, which degrades its performance at high signal to noise ratio. The second SP design alleviates this problem by iterating between channel estimation and data detection. Both these designs detect data using message passing algorithm which exploits OTFS channel sparsity, and consequently has low computational complexity. We also derive a lower bound on the signal-to-interference-plus-noise ratio of the proposed designs and maximize it by optimally allocating power between data and pilot symbols. We numerically validate the derived analytical results, and show that the proposed designs have superior SE than the existing OTFS channel estimation and data detection designs.

This paper investigates uplink rate performance of filter bank multicarrier (FBMC)-offset quadrature amplitude modulation (OQAM) based multi-user (MU) multicell massive multiple-input multiple-output (MIMO) systems. Contrary to existing works in the FBMC literature, channel between users and the base station (BS) within a cell is modelled to be Ricean fading with a deterministic fast fading component and a random Rayleigh distributed component. Closed-form expressions for the ergodic achievable uplink sum rate and corresponding approximation are derived for the maximum ratio combining (MRC) at the BS in the presence of imperfect channel state information (CSI). This is achieved by considering the effect of pilot contamination, and the statistical characteristics of the intrinsic interference in FBMC systems. Uplink power scaling laws with imperfect CSI at the BS are investigated. Analytical results are also developed for analysing the effect of the Ricean-K factor on the achievable uplink sum rate. Numerical examples are presented to demonstrate the tightness between the analysis and simulations, and to compare the performance of FBMC-OQAM and traditional OFDM-based MU multi-cell massive MIMO systems.

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramér-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

This study investigates the uplink performance of offset quadrature amplitude modulation (OQAM)‐based orthogonal frequency division multiplexing (OFDM) waveform in single‐cell multi‐user massive multiple‐input multiple‐output (MIMO) systems over Ricean fading. An uplink training‐based minimum mean square error (MMSE) channel estimation by exploiting the inherent characteristics of OFDM/OQAM waveform is briefly presented. By leveraging the second‐order statistical characteristics of the intrinsic interference, the achievable uplink sum rates and corresponding closed‐form approximations are derived in the presence of MMSE channel estimate at the base station with zero‐forcing (ZF) and maximum‐ratio combining (MRC) receivers. Based on the above analytical results for the uplink rates, power scaling laws are also investigated. The effect of Ricean‐ K factor on the achievable uplink sum rate is also examined analytically for both the MRC and ZF receivers. Numerical results compare the performance of OFDM/OQAM and classic cyclic‐prefix OFDM‐based massive MIMO systems and also validate the various analytical expressions derived in this work.

This paper analyses the performance of filter bank multicarrier (FBMC) signaling in conjunction with offset quadrature amplitude modulation (OQAM) in multi-user (MU) massive multiple-input multiple-output (MIMO) systems. Initially, closed form expressions are derived for tight lower bounds corresponding to the achievable uplink sum-rates for FBMC-based single-cell MU massive MIMO systems relying on maximum ratio combining (MRC), zero forcing (ZF) and minimum mean square error (MMSE) receiver processing with/without perfect channel state information (CSI) at the base station (BS). This is achieved by exploiting the statistical properties of the intrinsic interference that is characteristic of FBMC systems. Analytical results are also developed for power scaling in the uplink of MU massive MIMO-FBMC systems. The above analysis of the achievable sum-rates and corresponding power scaling laws is subsequently extended to multi-cell scenarios considering both perfect as well as imperfect CSI, and the effect of pilot contamination. The delay-spread-induced performance erosion imposed on the linear processing aided BS receiver is numerically quantified by simulations. Numerical results are presented to demonstrate the close match between our analysis and simulations, and to illustrate and compare the performance of FBMC and traditional orthogonal frequency division multiplexing (OFDM)-based MU massive MIMO systems.

Semi-blind (SB) channel estimation is conceived for millimeter wave (mmWave) analog-beamforming (AB) and hybrid-beamforming (HB)-based multiple-input multiple-output (MIMO) systems, which also exploits the data symbols for improving the estimation accuracy. A novel aspect of the proposed framework is that it directly estimates the analog beamformer/ combiner weights without necessitating the estimation of the entire mmWave MIMO channel matrix. By involving powerful matrix perturbation theoretic techniques, a closed-form expression is derived for the mean-squared-error (MSE) of the mmWave-AB-SB algorithm. As a further novelty, our mmWave-HB-SB technique relies on the decomposition of the channel matrix as the product of a decorrelating and a unitary matrix. Subsequently, the former is estimated purely relying on the unknown data symbols, whereas the latter is estimated exclusively from the training vectors. A lower bound on the MSE of the proposed mmWave-HB-SB technique is derived using the constrained Cramér-Rao lower bound (CRLB) framework. Furthermore, the performance gain of our mmWave-HB-SB technique over the conventional purely training-based scheme is also quantified analytically. Our simulation results demonstrate the superiority of the techniques advocated over the existing solutions and also verify the accuracy of our analytical findings.

This paper analyses peak to average power ratio (PAPR) of superimposed training sequence (STS) based multiple input multiple output filter bank multi carrier (MIMO-FBMC) offset quadrature amplitude modulation (OQAM) systems using complementary cumulative distribution function (CCDF). The results of this method are compared with complex training sequence decomposition (CTSD) based MIMO-FBMC OQAM systems.

This work addresses frequency synchronisation and channel estimation for highly frequency selective orthogonal frequency division multiplexed systems with offset quadrature amplitude modulation. A two‐step approach is proposed, wherein a coarse carrier frequency offset (CFO) estimator, which does not require channel knowledge, is designed in the first step by exploiting the correlation between the received and training signals. A time‐domain model is derived for the joint estimation of fine CFO (FCFO) and highly frequency selective channel in the second step. In contrast to the frequency domain model in the literature, the derived time‐domain model does not require the channel to be frequency flat. Based on the time‐domain model, the weighted least squares and minimum mean square error estimators are investigated to jointly estimate the FCFO and frequency selective channel. The Cramer‐Rao lower bounds (CRLBs) are derived for the proposed estimators, and are used to evaluate the performance of the joint FCFO and channel estimation in the presence of inter‐symbol‐interference due to the overlapping of adjacent time‐domain orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) symbols. Simulation results demonstrate achievability of the CRLB bounds and performance gain of the proposed estimators over the state‐of‐the‐art existing estimators.

This paper investigates the zero forcing (ZF) based vertical Bell laboratories layered space-time (V-BLAST) detection in multiple input multiple output (MIMO) filter bank multicarrier (FBMC) systems based on the offset quadrature amplitude modulation (OQAM). Initially, a QR decomposition based convectional ZF-successive interference cancellation (C-ZF-SIC) technique is examined for the detection in MIMO-FBMC-OQAM systems. In order to tackle intense error propagation in the C-ZF-SIC scheme due to the intrinsic interference in FBMC systems, a neighbourhood detection based ZF-SIC (ND-ZF-SIC) technique is proposed that exploits the fact that each symbol in the FBMC-OQAM system interferes only with the symbols within a small neighbourhood. Further, the ND-ZF-SIC scheme based on the sorted QR decomposition (ND-ZF-SQRD) is also investigated for the detection in MIMO-FBMC-OQAM systems. Simulation results demonstrate that the proposed schemes significantly outperform the conventional scheme, and attain a performance similar to that of MIMO-OFDM systems.

A training-based two-step approach which addresses carrier frequency offset (CFO) and channel frequency response (CFR) estimation in single-input multiple-output (SIMO)-filter bank multicarrier (FBMC) systems based on offset quadrature amplitude modulation (OQAM), is presented. A coarse CFO estimator which does not require the channel knowledge, is investigated in first step. For the combined estimation of fine CFO and CFR in second step, a least squares (LS) estimator based on the conventional interference approximation method (IAM), is proposed. The Cramer-Rao lower bound (CRLB) is derived for the proposed estimator. Simulation results show that the proposed estimator in second step achieves CRLB in the lower signal to noise ratio (SNR) regime. The bit error rate (BER) performance of the proposed approach is also numerically evaluated.