Successive-cancellation (SC) decoding algorithm is the most commonly used method for decoding the polar codes. It can be viewed as a depth-first binary tree search, traversing from root to all the leaf nodes in the direction of left to right, where each leaf node corresponds to a codeword bit to be decoded. It decodes the bits sequentially one after the other, increasing the decoding latency with the increase in codeword length. In this paper, we propose an adaptive fast SC decoder that uses a link abstraction metric called received bit information rate (RBIR), in identifying the depth level, at which all the intermediate nodes and the attached information bits can be decoded directly without traversing till the leaf node by using the recursive structure of the SC decoder. We show that for a target block error rate (BLER) of 10%, the proposed RBIR-based adaptive fast SC (RBIR-AFSC) decoder has around 48% ~ 54% reduction in decoding latency compared to that of conventional SC decoder, without any loss in BLER performance, for 3GPP defined 5G NR TDL-A channel model.

In this paper, a novel modulation scheme called two-dimensional discrete cosine transform orthogonal frequency-division multiplexing (2D-DCT-OFDM), which extends the principles of OFDM modulation to a 2D domain is proposed. Unlike traditional OFDM scheme, which relies solely on time-frequency processing, 2D-DCT-OFDM exploits an alternate 2D transform domain for information mapping and employs 2D-DCT operations along these dimensions for signal processing. Here, the design principles and performance analysis of the proposed 2D-DCT-OFDM modulation scheme are presented. Finally, the BER performance of the proposed 2D-DCT-OFDM scheme for different user equipment velocities are obtained and compared with the prior 1D and 2D modulation schemes (such as DFT-s-OFDM and OTFS) to get pertinent candidate waveform suggestions for beyond 5thgeneration (B5G) and 6G cellular systems. Simulation results show that the 2D modulation schemes provide better BER performance in high-mobility scenarios. In addition, 2D-DCT-OFDM provides slightly better BER performance than other 2D modulation schemes under extreme mobility with significantly less computational complexity. Due to the simplicity, computational efficiency, ease of implementation, and power efficiency of 2D-DCT-OFDM, it is suitable for beyond 5G and 6G.

Co-channel interference, arising either from neighboring cells or due to the co-channel deployment of macro and low-power base stations (BS), limit the uplink rates in 5G and beyond systems. Although interference rejection combining (IRC) equalizer is widely adopted to mitigate this issue, it suffers from higher complexity (cubic in BS antennas) compared to traditional MMSE equalizer (cubic in users). This is particularly concerning given the large number of BS antennas used in 5G and beyond systems. Further, due to inaccurate estimation of interference statistics, IRC suffers from performance loss compared to traditional MMSE at low-to-moderate interference levels. Based on these observations, the present work proposes an opportunistic IRC receiver that utilizes a small neural network at the BS to dynamically select the optimal equalizer between MMSE and IRC. This approach significantly reduces the equalizer complexity while also achieving effective interference mitigation. Our 5G link level simulations show that, with 12 layers and 64 BS antennas, the proposed AI-based opportunistic receiver achieves a 68% reduction in complexity compared to IRC, while also meeting the desired performance target.

With sustainability as one of the key requirements and the design principles of the sixth generation (6 G) communications, implementation based power saving solutions that benefit both base station (BS) and user equipment (UE) sides have gained significant interest in the cellular industry research. Channel decoding is one of the receiver modules that can help reduce the power consumption of the receiver significantly, and is applicable to both BS and UE. In this paper, we propose a universal list size and iteration number predictor (ULIP), for reducing the power consumption, applicable for 4G, 5G and beyond-5G channel decoders. We propose to use a link abstraction abstraction metric called received bit information rate (RBIR), that captures the time-varying channel conditions to identify and choose the iteration and list sizes for these decoders, to reduce the power consumption. We evaluate the proposed ULIP for regulating the iteration number in turbo and low-density parity-check (LDPC) decoders, used in 4 G and 5 G data channels, respectively, and the list size in polar decoder, used in 5 G control channel. We also verify the proposed ULIP for regulating the list size in the recently introduced polarization-adjusted convolutional (PAC) decoder, a prospective scheme for 6 G. We show that the proposed solution has upto 54.7 % and 67.4 % reduction of iteration numbers in LDPC and turbo decoders, respectively, for a target block error rate (BLER) of 10 %, and upto$\sim 92 \%$reduction of list sizes in both polar and PAC decoders, for a target BLER of 0.1 %, compared to conventional decoding methods.

Decoding polar codes through the depth-first binary tree-search based successive-cancellation (SC) algorithm takes a lot of time. Multiple fast simplified successive-cancellation (FSSC) decoding algorithms have been proposed in the literature to reduce the decoding latency and thereby the power consumption, by finding special nodes in the decoding tree, at which instantaneous decoding can be done. We observed that the latency can also be reduced by applying instantaneous decoding at all the nodes of an identified depth level in the decoding tree, as early as the root level at higher signal to noise ratio (SNR) conditions, without the need of traversing till the leaf node. Received bit information rate (RBIR) is a link abstraction metric that can be used to capture the SNR and identify the depth-level for instantaneous decoding. Existing FSSC algorithms rely only on the special nodes, and doesn't opportunistically utilize the time-varying and frequency-selective channel conditions for early depth level decoding. Whereas, relying on only RBIR-based opportunistic early depth level SC decoding (RBIR-OSC) will miss out on the special nodes of FSSC, that can provide significant power saving even in lower SNR conditions. In this paper, we propose an RBIR-based opportunistic FSSC decoding (RBIR-OFSSC) for polar codes, which combines the instantaneous decoding at special nodes of FSSC with the RBIR-OSC. We show that the proposed RBIR-OFSSC decoding reduces latency by 98% compared to conventional SC algorithm, 81% compared to FSSC, and 79% compared to RBIR-OSC, without any performance loss, for the 3GPP defined 5G NR uplink control channel.

Modern base stations exploit distributed architectures defined by the O-RAN standard for efficient operation. In ORAN, the base station functionality is split between a radio unit (RU) and a distributed unit (DU). Further, based on 3GPP split option 7.2, O-RAN specifies two categories of RU: Cat-A (or 7.2A) wherein precoding is performed at the DU, and Cat-B (or 7.2B) wherein precoding operation is offloaded to the RU. Hence, in 7.2B, the precoding information is transferred from DU to RU, but, after application of beamspace compression (BSC). This involves conversion of the precoders to beamspace, which is followed by quantization of its real and imaginary coefficients. However, the BSC approach is suboptimal as it does not fully exploit the underlying correlation in the precoding vectors. To address this limitation, we propose a deep learning (DL) based beamspace precoder compression approach for O-RAN. Using 3GPP compliant simulations, we show that the proposed approach decreases the precoder transfer overhead by up to 85%, while also achieving a spectral efficiency gain of up to 25%. Furthermore, we show that by compressing the precoders in the beamspace, the operational complexity can be drastically reduced, specifically due to reduction in auto-encoder (AE) input dimensionality.

Beamforming enables millimeter-wave communications to achieve high data rates in 5G and beyond systems. However, accurate beam alignment entails a large training overhead. User device orientation changes and mobility can rapidly lead to beam misalignment and lower the data rate. They also make the beam gains a non-stationary random processes. We propose a comprehensive and novel approach called latent Thompson sampling-based beam selection (LTBS), which combines latent Thompson sampling to track the angle of arrival (AoA) as a latent state, receive beam subset selection based on the sampled AoA in a manner compliant with the 5G new radio standard, rate adaptation, and data beam selection based on predicted throughput. We propose two variants of LTBS that trade-off between complexity and accuracy in modeling millimeter-wave channels. The prior update and channel gain prediction in one of the variants are based on the realistic spatial channel model (SCM). We propose variations that employ windowing to also tackle lateral user mobility, which alters the AoA and the channel statistics. Our numerical results show that the proposed methods track the AoA in a manner robust to user orientation changes and provide higher average data rates compared to conventional and state-of-the-art learning-based beam selection methods.

Cyclic redundancy check (CRC)-aided successive cancellation list (SCL) decoder, also termed as CA-SCL decoder, is used for decoding the polar codes in practical 5G new radio (NR) systems. The list size used in CA-SCL decoder is directly proportional to the power consumption and the complexity of the decoding process. In practice, a fixed list size (L) is used for CA-SCL decoding. However, we have observed that the required list size varies with signal to noise ratio (SNR) for achieving a target block error rate (BLER) performance. In this paper, we propose an adaptive CA-SCL (CA-ASCL) decoding algorithm, that reduces the average list size and hence the complexity of the CA-SCL decoder by identifying the right list size to be used for each decoding process, using a link abstraction metric called received bit information rate (RBIR), that maps to received SNR. We show that for a target BLER of 10%, the proposed CA-ASCL decoder that chooses the list size adaptively among the set {2, 4, 8, 16, 32} has upto 61.51% reduction in list size compared to that of CA-SCL decoder with L = 32, without any loss in BLER performance, and upto 72.23% reduction with performance loss limited to 0.05 dB in SNR, respectively, for 3GPP defined 5G NR TDL-A channel model, with delay spread of 30 ns and maximum Doppler shift of 100 Hz.

QR decomposition based M-algorithm (QRDM) is a tree search method that achieves near-optimal signal detection in multiple-input multiple-output (MIMO) systems at a significantly lower complexity compared to maximum-likelihood detection (MLD). However, the complexity of QRDM still remains much higher than linear detectors typically employed in practical systems. While there exist some low-complexity variants of QRDM, they often sacrifice performance for complexity. The present work proposes a robust low-complexity variant of QRDM that leverages an initial linear solution to introduce two fundamental optimizations. First, it computes the costs for only k-bit neighbors of the linear solution in each layer, instead of all symbols of the modulation alphabet. Second, it dynamically changes the number of surviving nodes in each layer based on a threshold on the accumulated cost. These enhancements lead to a significant reduction in complexity while also achieving improved performance compared to conventional QRDM and its existing low-complexity variants. Notably, the proposed detector addresses the problem of premature pruning of reliable solutions in early layers, which is the key cause of sub-optimality in existing QRDM variants.

We consider a scheduling problem pertinent to a base station (BS) that serves heterogeneous users (UEs). We focus on a downlink model, with UEs that are either interested in large throughput, low latency (the time difference between the packet arrival at the BS, and reception at the UE), or low age-of-information (AoI, the difference between the current time and the arrival time of the latest packet at the BS that has been received at the UE). In each time step, the BS may serve a limited number of UEs. The objective is to find a scheduling algorithm that meets the service requirements of all the UEs. In this paper, we formulate this objective as a scheduling problem and develop a general framework to tradeoff between throughput, latency and AoI of different UEs. Finally, we analyze the derived results using numerical simulations.

Power amplifiers (PA) are the essential part of the wireless communication systems but generally exhibit non-linearity at the high voltage input. Non-linearity in the orthogonal frequency division multiplexing (OFDM) based transceiver can cause severe distortions for the both in-band and out-band signal because of the high peak to average power ratio (PAPR). As the system bandwidth goes higher, PA exhibit memory effect. Non-linearity and memory effect is usually handled efficiently with digital pre-distorter (DPD). DPD is modeled with Volterra series kind of polynomials specially generalized memory poly-nomial (GMP). Estimation of the coefficients of the GMP is non-trivial and involves lot of complexity when the order and the memory of the GMP are significantly high. In this paper, we propose AI-methods to select the top-P features of the GMP based DPD out of total$N$features (P$P$features during the training of the AI model. We also propose a perturbation based method, which perturbs the input and based on its effect on the output obtained from the trained model, finds the most suitable top-$P$features. At last, we show the performance of our methods by calculating the performance metrics such as error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR). We show that for the GMP with N = 602 features, AI based selected top-50 GMP features performs similar to the GMP with all 602 features, and thus the complexity is reduced by more than 99%.

Digital twin creates a digital representation of the physical world, which enables immersive technologies such as augmented reality (AR), virtual reality (VR), and holographic communication. In this paper, we take a significant step towards enabling digital twin at scale & low-cost by estimating depth map of the environment using existing communication infrastructure. Traditionally, camera or LiDAR (light detection and ranging) have been used for estimating the 3-D depth map. The proposed method RF2LiDAR is first of its kind algorithm that generates LiDAR-like high resolution representation of the environment from ambient communication signals. We first perform preprocessing on the multiple-input-multiple-output (MIMO) radio-frequency (RF) signal and then input the processed data to a deep learning model to target the LiDAR point cloud data. RF2LiDAR is able to generate LiDAR-like depth map of room of size 19m × 10m × 2m with 0.25m granularity from MIMO data. Further, we show that the predicted point clouds have an average Chamfer distance of 1.5m2and they capture the change in perception across various testing locations without any prior information of location and orientation of the receiver.

Estimating the direction-of-arrival (DOA) is a crucial problem in most array signal processing applications, including wireless communication, radar, sonar, astronomical observation, and acoustics. The traditional direction-of-arrival (DOA) estimate methods, which are based on subspace decomposition, need for the eigenvalue decomposition, resulting in greater computation complexity. Different adaptive algorithms, including fixed-step-size least mean square (FSS-LMS), variable step-size least mean square (VSS-LMS), and bias-compensated LMS (BC-LMS), have recently been developed for the DOA estimation by using the adaptive nulling antenna techniques in an effort to reduce the computational complexity. The aforementioned algorithms are designed upon the minimization of Mean Square Error (MSE), which proves to be effective in the presence of Gaussian noise. However, their performance will degrade and leads to inaccurate DOA estimation when non-Gaussian/impulsive noise is present. In order to improve the DOA estimation performance in the presence of non-Gaussian/impulsive noise environment, we propose a variable-step-size generalized modified Blake-Zisserman (VSS-GMBZ) algorithm in this letter. The VSS-GMBZ is evaluated for various non-Gaussian noise scenarios to determine DOA estimation accuracy in the Matlab environment. Numerical results demonstrate the superiority of VSS-GMBZ over existing methods.

Modern base station (BS) employ distributed architecture, wherein the overall processing is divided between a radio unit (RU) and a distributed unit (DU), that are connected by a fronthaul link. In open-RAN (O-RAN) 7.2 split architecture, RU performs only a few simple operations, while most of the BS processing happens at the DU, providing the benefits of centralized computing and control. A key challenge in 7.2 split is to limit the overhead on the fronthaul link within acceptable limits. To address this issue, a common solution is to perform combining operation on the received signals at RU antennas, and send low-dimensional signals to DU over fronthaul. The combining matrix used at the RU is typically based on older/aged CSI received from DU, resulting in performance degradation, which is referred as channel aging problem. An alternate approach to limit the fronthaul overhead is to quantize the frequency-domain (i.e., post-FFT) samples at RU to fewer bits (say, 3 or 4 bits) and send the low-resolution signals over fronthaul. Since this approach does not use aged channels, its performance is not affected by channel aging, but is mainly limited by quantization noise. It is recently shown that, under certain conditions, quantization approach achieves superior error performance compared to combining, while also maintaining lower overhead. Motivated by this observation, the present work proposes an AI-based solution to opportunistically switch from combining to quantization under favorable conditions, thereby mitigating channel aging while also reducing fronthaul overhead.

Modern base stations (BS) utilize distributed architectures, wherein the BS functionalities are split between a radio unit (RU) and a distributed unit (DU), that are connected via a fronthaul link. Managing the overhead on this link within tolerable limits is challenging due to ever increasing uplink data traffic and limited fronthaul capacity. In the widely accepted O-RAN 7.2 split architecture, the RU performs combining and sends lower dimensional signals over the fronthaul, thereby reducing the overhead. This approach suffers from performance loss due to channel aging, since RU combining is based on older/aged CSI received from DU. An alternate, less explored approach for reducing overhead is to transmit low-resolution signals over the fronthaul, obtained via quantization of post-FFT samples at the RU. While quantization noise results in some degradation, this approach does not suffer from channel aging. To date, a realistic performance comparison between combining and quantization is not available in the literature. The present work fills this gap and explores the regimes where quantization is favorable in both performance and overhead compared with combining. Our results suggest that opportunistically switching from combining to quantization under favorable conditions achieves superior performance at reduced overhead.

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the coverage and cover the areas under blockage. However, the efficient utilization of RIS resources during idle periods remain a challenge. The conventional RIS configuration inadvertently leads to signal reflection even during the idle periods, resulting in potential signal degradation and interference that can impede the direct and/or neighboring communication links. Due to hardware limitations, it is practically impossible for the RIS to completely absorb the unwanted signal, resulting in minimal reflection amplitude. Therefore, this paper presents the innovative operational mode called “scatter mode” for the RIS, a concept that intelligently controls the RIS behaviour during idle period by scattering incident signals in all spatial directions. By mitigating unnecessary signal reflections and reducing interference, the scatter mode enhances signal quality and network reliability. To realize the proposed scatter mode, two solutions are proposed, namely “random phase selection” and “sub-RIS”. With random phase selection and sub-RIS, the minimum interference is reduced by 12 dB and 25 dB compared to optimized phase to reflect. Finally, the signalling flow and procedure to seamlessly integrate the proposed configuration into the existing RIS deployment strategies and its practical implications are shown.

Minimum mean square error interference rejection combining (MMSE-IRC) equalizer is used at the base station (BS) receiver (Rx) in cellular systems to mitigate inter and intra cell interference. However, it has a complexity of the order$\mathcal{O}(N^{3})$, where$N$is the number of Rx antennas at the BS. As a result, it will become impractical to implement MMSE-IRC in the context of extreme massive MIMO (extreme-mMIMO) where the number of antennas at the BS may range from 256 to 4096. To this end, this paper proposes a low complexity equalizer called Diagonal-Interference Rejection combining (D-IRC). It uses diagonal loading of the noise and interference covariance matrix. It is shown that the proposed D-IRC equalizer has a computational complexity of order$\mathcal{O}(N^{2})$which is significantly less than the conventional MMSE-IRC equalizer with similar bit error rate (BER) performance. Further, the implementation of the proposed D-IRC in the centralized/cloud radio access networks is demonstrated in this paper.

The design and development of sub-Terahertz (sub-THz) cellular systems entail the need for new channel models that can precisely predict channel characteristics beyond 100GHz in outdoor and dynamic environments. This work proposes a novel multiple-input and multiple-output (MIMO) channel model for cellular communication, developed within the framework of a class of spatio-temporal stochastic processes called ambit-process. The modeling methodology effectively captures the typicalities of sub-THz propagation like molecular absorption and scattering of the evolving multipaths while accounting for the propagation delay of electromagnetic waves across large array apertures deployed at the transmitter and the receiver. This allows for an accurate characterization of the spatial-wideband effect along with other relevant spatio-temporal attributes of the channel. Numerical simulations indicate a good level of agreement between the spectral efficiency and spatio-temporal correlation of the proposed model against a state-of-the-art stochastic Terahertz (THz) channel model and measurements reported in the literature.

The design and development of Terahertz (THz) and sub-Terahertz (sub-THz) communication systems entail the need for new channel models that can precisely predict channel attributes at such frequencies (≥100 GHz) in outdoor and dynamic environments. This work proposes a novel hybrid-stochastic ultra-wideband channel model for sub-THz bands, developed within the framework of a class of spatio-temporal stochastic processes called the ambit-process. The proposed model is capable of supporting bandwidths of upto 1 GHz. The spatio-temporal evolution of the ambit framework allows for a spatially consistent, reasonably accurate and tractable characterization of the fading statistics and multipath propagation of the cellular channels. We leverage a recently proposed convolution-based low-complexity algorithm with necessary modifications to study key features of the microcellular sub-THz channel like associated diffused reflection and scattering, molecular absorption, spatio-temporal correlations, and consistency between the time-evolving delay and Doppler of the multipaths. Simulation results on path loss, shadowing, delay spread, and channel correlations indicate that the ambit model accurately captures the typicalities of an urban microcellular sub-THz channel and agrees well with the measurement results reported in the literature.

We consider a deep learning (DL) based approach to optimize channel state information (CSI) reporting in massive multiple-input and multiple-output (MIMO) systems. For CSI compression, existing methods use frequency-domain (FD) and spatial-domain (SD) correlation, whereas correlation also exists in the time-domain (TD) under fading conditions. Hence, we propose a DL-based three-dimensional compression (DL-3DC) approach to improve CSI reporting accuracy by using correlation in FD, SD and TD. In addition, to reduce the feedback overhead, we propose two DL-based CSI prediction methods: eigenvector based and MIMO channel based. We then integrate CSI prediction with DL-3DC at the UE and propose a joint CSI compression and prediction (JCCP) scheme to improve the CSI accuracy and reporting overhead trade-off. Although, UE-side JCCP is efficient when the future CSI application time instance is known in advance. To support prediction when this is unknown, we propose BS-side JCCP to predict the CSI at the BS after it is received. Through simulations, we show that DL-3DC improves the CSI reporting accuracy by up to 13.8% compared to the latest CSI reporting mechanism in New Radio (NR). Further, we show that JCCP significantly reduces the reporting overhead by up to 87.5% compared to NR CSI reporting.

In this paper, a downlink synchronization technique in an orthogonal time-frequency space (OTFS) cellular system at the physical layer is proposed. The primary synchronization signal (PSS) detection is the most computationally complex algorithm during synchronization and cell search. Therefore, a faster and low-complexity algorithm based on the overlap-add method is proposed. Here, PSS is a Zadoff-Chu sequence (ZCS) carrying a primary cell identity (PCID) mapped in the two dimensional (2D) delay-Doppler domain is broadcast by the base station (BS) using OTFS modulation, which is non-coherently detected at the user equipment (UE). Further, the effect of OTFS modulation on 2D delay-Doppler ZCS is investigated and analyzed to show that the duality property of ZCS is conserved between delay-Doppler to time-frequency domains and between time-frequency and time domains. Finally, the detection probabilities with multiple UE velocities are shown in multi-cell environment to show the robustness of the proposed technique. In addition, the OTFS performance is compared with OFDM for third generation partnership project (3GPP) defined tapped delay line (TDL) channel model. Simulation results show that when the UE speed is 540 km/h ($\equiv \mathbf{14}\mathbf{kHz}$Doppler frequency), a 99% detection probability is achieved at -13 dB SNR using the proposed technique, whereas the detection probability for OFDM drops to zero.

The need for higher uplink data rates in 5G and beyond systems is hindered by co-channel interference that can arise either from the neighboring cells or due to the co-channel deployment of macro and low-power base stations. To mitigate this issue, MMSE with interference rejection combining (IRC) has been widely adopted in practical systems. Unfortunately, MMSE-IRC suffers from higher complexity (cubic in number of BS antennas) compared with traditional MMSE equalizer (cubic in number of users). This is particularly concerning given the large number of BS antennas used in 5G and beyond systems. Further, it is illustrated in the present work that, under certain practical constraints, MMSE-IRC shows either the same or even worse performance compared with MMSE. Based on these observations, the present work proposes an opportunistic receiver that switches to MMSE from MMSE-IRC under favorable conditions, thereby reducing the average complexity without compromising in performance. The proposed receiver is validated through 5G link level simulations under practical channel models, demonstrating a significant reduction in complexity compared with MMSE-IRC.

Even though the 5G new radio (NR) system at mmWave frequencies provides large bandwidth and user data rates, it suffers severely from blockages and absorption losses, making its coverage limited to few meters. A wide range of new technologies and schemes are being investigated to overcome this coverage issue at high frequencies. Reconfigurable intelligent surfaces (RISs) is one among them. An RIS consists of a panel that reflects the incident signal towards the receiver, adding an extra signal path, thereby, aiding in extending the cell coverage. However, the placement and deployment of an RIS in the cellular system plays an important role in order to take the advantage of reflection. In this paper, we propose and analyse a practical method of RIS placement in an urban cellular network, that benefits all the users whose direct link from base station (BS) is blocked. We further propose a method in identifying the range of distance at which an RIS can be placed from a blocked region. We show that the RIS placement with the proposed method has around 8.2 dB gain in signal to interference and noise ratio (SINR), compared to randomly identified positioning methods, near to the network elements. We further show that the user coverage percentage improves significantly with the proposed method. We also discuss about a method through which blockage regions can be extracted from a real time system so that the suitable locations for RIS deployment can be chosen in 5G NR system.

In this paper, a non-coherent downlink synchronization technique in an orthogonal time-frequency space (OTFS) cellular system at the physical layer is proposed. Here, a Zadoff-Chu sequence (ZCS) carrying a secondary cell identity (SCID) is mapped in the two dimensional (2D) delay-Doppler domain is broadcast by the base station (BS) using OTFS modulation, which is non-coherently detected at the user equipment (UE). Unlike OFDM-based fourth (4G) or fifth generation (5G) systems, the detection of SCID with the proposed technique does not require channel estimation and equalization. This enables the UE to detect the SCID from multiple BSs with lower computational complexity. Further, the effect of OTFS modulation on 2D delay-Doppler ZCS is investigated and analyzed to show that the duality property of ZCS is conserved between delay-Doppler to time-frequency domains and between time-frequency and time domains. Unlike conventional 1D correlation properties, 2D correlation properties of ZCS are evaluated. Finally, to show the robustness of the proposed technique, the detection probabilities with multiple UE velocities are shown. Simulation results show that when the UE speed is 150 m/s (= 14 kHz Doppler frequency), a 100% detection probability is achieved at -21 dB SNR using the proposed technique, whereas the detection probability for OFDM drops to zero.

In this paper, we design a Joint Communication and Sensing (JCAS) waveform for Multiple Input Multiple Output (MIMO) wireless systems. The JCAS waveform is formed by combining Frequency Modulated Continuous Wave (FMCW) and Orthogonal Frequency Division Multiplexing (OFDM) waveforms. Specifically, it can be viewed as a combined MIMO-OFDM and MIMO-Radar waveform. The proposed waveform is used by the Base Station (BS) of the JCAS system, which consists of communication and sensing subsystems. The communication subsystem is used to transmit information to the desired user equipment (UE), while the sensing subsystem is used to detect multiple objects in the vicinity of the BS. For the communication subsystem, OFDM sub-carriers are used to carry UE’s data, while the FMCW acts as pilot signals and enable frequency domain channel estimation. For the sensing subsystem, after receiving the echos of JCAS signal, we remove the OFDM part of it and then form a Radar virtual array at the BS. The data from the virtual array is used to estimate the target parameters of interest, i.e., distance and velocity. Numerical results are presented to demonstrate the performance of both communication and sensing subsystems with the proposed JCAS waveform.

Acquiring accurate channel state information (CSI) is challenging in MIMO systems as it is quickly outdated due to fast variations in the channel. Particularly, in frequency division multiplexing (FDD) systems the problem is aggravated due to sparse frequency of CSI reporting used in practical systems; this leads to user throughput degradation. CSI prediction effectively addresses this issue, but generally at the cost of computational complexity. Traditional techniques predict using the spatial channel, hence the size of input and output scales with the number of antennas used, and the operating bandwidth, thus increasing the complexity. In this paper, we solve this problem using a DL based low-complexity approach called Smart-CSI prediction. In this approach, we predict the variation of channel capacity to predict the future CSI. Consequently, it significantly reduces the operating complexity compared to the existing prediction methods. Numerical results indicate that Smart-CSI effectively predicts the CSI in simulated and real-world channel conditions. Further, we evaluate the approach using a proof-of-concept implementation and show that Smart-CSI achieves 11.8% higher throughput, on a average, compared to the no-prediction scenario.

To compensate for high pathloss and improve coverage in millimeter-wave (mmWave) frequencies, highly directional transmit (Tx) and receive (Rx) beamforming is required. In addition, mmWave frequencies are extremely susceptible to blockages, and when blockage occurs, the link between Tx and Rx may be terminated and an alternate path is required to retain communication. Reconfigurable intelligent surface (RIS) can be considered as a possible solution to not only solve the blockage problem but also to cover dead spots. However, using conventional exhaustive search protocol, the overhead of beam training increases exponentially with the number of RIS reflective beams, as it requires finding the optimal beam pair in the Tx-RIS-Rx link. In this paper, a low-latency retro-reflective (LLRR) beam training protocol for RIS-assisted cellular system is proposed to reduce the overall beam training time by reflecting the RIS beams to the source. Furthermore, the feasibility of the RIS element to provide 180° phase shift is analyzed. Simulation results show that the proposed LLRR beam training significantly reduces the training time compared to the state-of-the-art and achieves gains of ≈ 1.5 bps/Hz and ≈ 9.8 bps/Hz at signal-to-noise ratio of 15 dB when the number of beams at RIS is 64 and 256, respectively.

An intelligent reflecting surface (IRS) is a cost and energy-efficient solution to improve wireless system performance. Transmit antenna selection (AS) harnesses the benefits of multiple antennas with a smaller number of radio frequency (RF) chains. We focus on joint optimization of antenna subset and transmit beamforming at the transmitter (Tx) and passive beamforming at the IRS to maximize the receive signal power. We derive a closed-form optimal AS rule for a Tx and receiver (Rx) equipped with single RF chain each and ideal IRS. We analyze its performance with a correlated channel model and then extend it to non-ideal IRS. We also propose a simpler rule that significantly reduces the number of computations and pilots. For an Rx that performs maximal ratio combining, we propose a manifold optimization algorithm and a low-complexity selection rule. For a Tx with multiple RF chains, we propose a subset selection algorithm that yields a locally optimal solution and an alternating optimization algorithm that reduces complexity. Our simulations study the impact of estimation errors, discrete phase shifts, and channel correlation on the proposed selection rules, which perform better than the existing AS rules. They also show that the proposed low-complexity rules are near-optimal.

With the exponential growth of cellular traffic in recent times, larger bandwidth and higher spectral efficiency (SE) have become indispensable for higher data rate applications. Discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) waveform is being considered as one of the potential candidates for the next generation communications, because of its low peak to average power ratio (PAPR), which is more suitable for large bandwidth systems that are limited by inefficient power amplifiers. However, DFT-s-OFDM suffers from low SE, thus affecting the system throughput. Recently, faster than Nyquist (FTN) signaling combined with DFT-s-OFDM is being studied for its improved spectral efficiency with less PAPR. In this paper, we analyze the performance benefits of applying FTN signaling on DFT-s-OFDM waveform with 5G new radio uplink system. We show that the FTN combined with DFT-s-OFDM has 2% ∼ 75% more throughput than that of of DFT-s-OFDM for the same signal to noise ratio (SNR), using fading channel model. We further show through link adaptation method that the SE of DFT-s-OFDM waveform improves significantly when combined with FTN signaling. We also discuss about the implementation aspects and the performance trade-off with FTN integration into beyond 5G systems.

Modeling realistic time-varying and frequency selective channels is critical for designing the wireless systems that are robust to fading channels. Modeling a wireless channel is a time consuming process which involves measurement campaigns in various environments and then fitting the right statistical model for each measurement scenario. In this paper, we propose a method to generate channel based on the generative adversarial network. This approach is helpful in generating time-varying and frequency-selective channels for the scenarios which do not have a known statistical model. Typically, this method is helpful in early stages of characterization of the channels for new bands or mediums. For example, millimeter wave, tera-hertz bands or new mediums like molecular communications. Furthermore, this method generates the channel trace by capturing the time-varying nature of the channel efficiently from the channel measurements itself and uses this learned correlation model in generating new such channels very efficiently. The accuracy of the channels generated by the proposed method is verified through conditional cumulative distribution function for the third generation partnership project (3GPP) defined spatial channel models.

Terahertz (THz) band (0.1 - 10 THz) communications is being considered as a promising technology for the sixth generation (6G), as it can provide the peak data rates in the order of Tbps, with large bandwidths available at these bands. However, due to high path loss and molecular absorption noise experienced in this band, the cell coverage will be limited to shorter distances. In this paper, we propose hierarchical transmit beam sequencing method for synchronization signals, that enables beam combining at the receiver for an efficient cell search in THz network, and show its benefits for improved cell coverage. We show that the proposed algorithm has upto 4.8 dB gain in signal to noise ratio (SNR) for 90% cell detection probability, and reduces latency by 35.54%, compared to conventional algorithm with no beam combining at the receiver. Further, it is shown that the combining gain in cell detection performance improves the THz cell coverage by 237.76%.

In this paper, we propose a neural network (NN) based algorithm for digitally pre-distorting the transmit signal to compensate for the non-linearities in power amplifier. Here, we studied a novel NN architecture inspired by Volterra series, which is widely used for modeling the power amplifier characteristics. We compare its performance with conventional and other known NN based digital pre-distorters (DPDs) in the literature. The proposed method has separate sub-networks for each memory tap. This helps in having flexible number of output nodes for the sub-network based on the chronology of samples. The proposed architecture performs the best in terms of adjacent channel power ratio (ACPR) and is second best in terms of the error vector magnitude (EVM) compared to the state of the art deep learning methods evaluated in the paper. The proposed method has a performance gain of more than 6 dB in EVM and 3.5 dB in ACPR compared to the best conventional method, generalized memory polynomial (GMP). The proposed architecture's complexity is the least among all deep learning based DPDs and$\approx$1.5 times complex compared to GMP.

Acquisition of accurate channel state information (CSI) is critical in multiple-input and multiple-output (MIMO) systems to achieve efficient link adaptation. As CSI is typically estimated at the receiver, effective and efficient acquisition at the transmitter is challenging. The primary concerns are estimation accuracy, reporting overhead, and channel aging effects caused due to delayed CSI usage. In this paper, we design a deep learning based CSI prediction framework (DCP) to address these challenges. The DCP consists of a channel prediction network to compensate the aging effects, followed by a deep learning based CSI estimator (DCE) for accurate estimation. Through simulations, we show that the DCE can yield up to 15% higher spectral efficiency (SE) due to the improved estimation accuracy while significantly lowering the complexity compared to conventional approaches. Moreover, for the same CSI reporting overhead, DCP can improve the SE by up to 20% over conventional techniques. Further, even at 50% of the CSI reporting overhead, DCP improves the SE by up to 6.5% over the conventional methods.

Turbo and low-density parity-check (LDPC) codes have been chosen in wireless communications because of their near channel capacity performance. However, they consume huge power and also induce delay because of the iterative nature of the decoders. Various types of early termination (ET) techniques have been introduced within these decoders to decrease the power consumption and delay. Recently, machine learning (ML) algorithms are being explored to replace or improve the complex receiver algorithms in wireless communications, as a part of 6G research. In this paper, we propose a novel algorithm to use ML within the turbo and LDPC decoders, to identify the iteration for ET. We show that the proposed algorithm outperforms the improved hard decision aided ET method by 25% ~ 57%, in reducing the average number of iterations (ANI) of turbo decoder at 10% block error rate (BLER), for multiple modulation schemes. We also show that the proposed ET method outperforms parity check equation ET method of LDPC decoder by 30% ~ 36%, in reducing the ANI at 10% BLER, for multiple modulation schemes. The proposed method has negligible loss in BLER performance compared to typical implementation of fixed iteration decoders.

Next generation wireless communication systems are considering orthogonal time frequency space (OTFS) waveform as an alternative to the existing orthogonal frequency division multiplexing (OFDM) waveform. It is more robust to high Doppler and high carrier frequencies. Multiple recent papers have evaluated OTFS at higher Doppler with long term evolution (LTE) system, to show its performance benefits over OFDM. In this paper, we provide the performance evaluation of OTFS waveform at mmWave frequencies using the third generation partnership project (3GPP) 5G new radio (NR) transmit-receive chain for different quadrature amplitude modulation (QAM) schemes. We compare the OTFS performance with that of OFDM, for 3GPP defined tapped delay line (TDL) and cluster delay line (CDL) channel models at multiple mobility conditions. We analyze the performance difference between OTFS and OFDM using both minimum mean-squared error (MMSE) and decision feedback equalizers (DFE) at the receiver, and show the need for DFE at higher modulation order, to compensate the inter symbol interference. We show that the OTFS has 10 dB gain in signal to noise ratio (SNR) over OFDM, for TDL-C channel with delay spread of 300 ns, at high speed train mobility of 500 kmph for 64-QAM, when evaluated with DFE. We further present the computational complexity of OTFS system, in comparison to OFDM.

5G millimeter-wave (mmWave) system provides ultra low latency and higher peak data rate with a major drawback of higher path loss at mmWave spectrum. Multiple beams are formed at base station (BS) and user equipment (UE) to compensate excessive path loss. To help find the best beam pair for data transmission, beam measurements are performed continuously, typically in round robin fashion. Time taken for the measurement of full beam pair set can be large which results in delay in finding the best beam pair, which in turn results in poor data rate and link quality. In this paper, we analyse the key factors affecting signal strength of beam pairs such as device orientation and angle of arrival (AoA) at system level. Further, we propose a method to predict top-K candidates for the best beam pair using recurrent neural networks (RNN) with sensor data and beam measurements as inputs. We evaluate the performance of the proposed method with a performance metric showing the number of times the best beam pair is in top-K predicted candidates. Further, we show gain in the throughput by scheduling the predicted candidates for measurement compared with conventional scheduling. We show that for an UE changing its orientation even at the rate of 90 degree per second, best UE beam is in Top-5 predicted UE beams 99% of the times and gain in the throughput is more than 50% compared to conventional methods.

In this paper, we propose a deep learning based algorithm for downlink channel estimation for 5G new radio. The channel estimation block in the downlink plays an important role in mobile device performance. Here, we explore the usage of convolutional neural network (CNN) and compare its performance with conventional and other known CNN based channel estimation methods. The novelty of this method is that, it separates the channel estimation method into three logical blocks. First, obtaining channel coefficients from pilots and filtering for noise reduction. Second, interpolating the channel in frequency domain using the noise filtered channel estimate on pilots. Third, interpolating the channel in time domain across orthogonal frequency-division multiplexing (OFDM) symbols. This architectures allows considerable flexibility in handling various combinations of delay spread (DS) and Doppler spread (DoS) that are possible in practical scenarios. This in turn helps in reducing the complexity and storage requirements. The proposed channel estimation method showed a performance gain of 4 dB in NMSE compared to linear minimum mean square error for clustered delay line channel model at signal to noise ratio of 30 dB, and is robust to mismatches in parameters like DS and DoS because of estimation errors.

Intelligent reflecting surface (IRS), which uses passive reflective elements instead of active radio frequency (RF) chains, is a cost and energy-efficient solution to improve the wireless system performance. With a similar objective, transmit antenna selection (AS) reduces the number of RF chains at the base station while harnessing the benefits of multiple antennas. In our work, we focus on joint optimization of antenna subset and transmit beamforming at the base station, and passive beamforming at the IRS to maximize the receive signal power. For single AS, we first derive a closed-form optimal rule. We then propose a simpler AS rule, which significantly reduces the computational complexity and the number of pilot transmissions required. For a system with Nt antennas at the base station and N IRS elements, the optimal AS rule requires Nt+ NtN pilots. However, the proposed simpler rule requires only 2Nt+N pilots. For subset AS, we develop a manifold optimization based algorithm. To reduce its subset search complexity, which is exponential in the number of RF chains at the base station, we propose an alternating optimization based iterative algorithm. Our numerical results show that the proposed simpler AS rules are near optimal.

The use of many narrow beams to overcome the adverse propagation conditions in millimeter-wave channels leads to large training durations and overheads in 5G systems. This causes the beam measurements to become outdated by different extents at the time the transmit and receive beams are selected. The rapid changes in user device orientation exacerbate this problem. We first present a novel modified bivariate Nakagami-$m$(MBN) model to tractably and accurately characterize the joint, non-stationary statistics of the channel gains seen at the times of measurement and data transmission. We then derive a novel and optimal beam selection rule that maximizes the average rate of the system. We use the MBN model to propose a near-optimal, practically amenable bound-based selection (PABS) rule. Our approach captures several pertinent aspects about the spatial channel model and 5G, such as transmission of periodic bursts of reference signals, feedback from the user to enable the base station to select its transmit beam, and the faster pace of updating the data rate compared to the transmit-receive beam pair. The PABS rule markedly outperforms the widely used conventional power-based selection rule and is less sensitive to user orientation changes.

Ahstract-5G millimeter wave(mmWave) systems are prone to blockage from various objects in the typical propagation environment. Among these, human blockage and vehicular blockage are more prominent scenarios that can lead to link failure. In this paper, we analyze the effect of vehicular blockage on 5G mmWave systems using stochastic geometric models. Using this analysis, we derive the blockage probability of the link and provide a relation to latency in communication with vehicular blockage in various scenarios. Further, we show and quantify the fact that with dynamic point selection (DPS) scheme where user maintains simultaneous links with geographically separated transmit and receive points (TRP), the coverage probability can be improved significantly and the latency due to blockage can be reduced. We analyze the blockage probability and latency for DPS system in terms of blocker density, length and typical speed of the vehicle and number of simultaneous links maintained by the user. The proposed analytical model is validated using system level simulations. We show the trade off between the number of simultaneous links and latency using these results which helps in 5G system design for latency critical applications.

The New Radio (NR) 5G technology uses communication in millimeter wave (mmWave) frequency bands to provide significantly high throughput and low latency. Since mmWave frequencies suffer from higher path-loss, NR employs directional communication generated using beamforming techniques. Higher the directionality, better the coverage, but also larger is the number of beams required to cover all directions. In scenarios such as cell search during initial access or neighbor cell measurements, typically directional information is unavailable. Hence, user equipment (UE) needs to blindly search in all receive directions until a successful cell detection. Thus, in case of cell search, usage of directional communication results in a trade off between coverage performance and detection latency. In this paper, we propose an efficient NR UE cell search algorithm to mitigate this problem. To improve latency, we propose a beam scheduling algorithm using receive beam power measurements, which selects a subset of beam candidates and their scan order, and we further optimize detection performance by combining powers from multiple receive directions. Using a 3GPP simulation setup, we show that the proposed beam combining algorithm can yield significant gains, up to 2dB for detection performance, and beam scheduling algorithm can achieve a mean latency reduction of up to 50% compared to the conventional approach while reducing the overall processing overhead.

Metallic frame of elevators attenuate the radio signals severely causing Radio Link Failure (RLF) at Mobile Device (MD). RLF results in connection interruption causing degradation in user experience. To overcome this problem, we propose a reinforcement learning based technique called Intelligent Elevator Detection and Network Adaptation (IE-DNA) algorithm, that can be implemented on MD. The IE-DNA algorithm has the capability to handle frequent RLF, detection of elevator movement and intelligent handover among the Cellular (i.e., 3G/4G/5G) or Wi-Fi networks. Using IE-DNA algorithm, MD can upload and store radio link statistics, experienced by the user, to a cloud server. Further, uploaded data can be utilized by any mobile user to know the radio link condition a-priory for a specific elevator, when visiting later in time and handle it by selecting a network that wouldn't cause interruption during elevator movement. Based on real data-set for Cellular/Wi-Fi collected by Samsung Galaxy S8 device, we conduct extensive experiments using real elevator environment to compute performance of the schemes with respect to the state of art.

Ultra-reliable and low-latency communications (URLLC) is an emerging service supported by the 5G new radio (NR). The reliability requirement for one transmission of a packet is 99.999% for 32 bytes with a user plane latency of 1 ms for the applications supported by URLLC. To support these requirements, third generation partnership project (3GPP) has introduced enhanced grant free transmission scheme in the uplink (UL) with multiple active configured grants (CGs) for URLLC user equipments (UEs). With multiple active CGs for UL, UE can choose any of these grants as soon as the data arrives. In this work, we propose an algorithm to optimally select one of the grants configured by the network that meets both the latency and reliability requirements, using received bit information rate (RBIR) based link prediction. We show that the proposed algorithm performs significantly better compared to the traditional grant selection methods in terms of reliability and latency when considered as a joint optimization problem. At 0.001% block error rate (BLER), the proposed algorithm shows at least 74.7% reduction in latency compared to the other algorithms which provide almost same or lesser reliability. Similarly, the proposed algorithm performs 1 dB better in reliability in terms of signal to noise ratio (SNR) compared to the latency optimizing algorithm.

Beamforming is the core principle used for communication in mmWave bands to overcome the excessive pathloss experienced at these bands. Usage of narrow beams results in a large number of beams at transmitter and receiver covering the given range of azimuth and elevation angles. Narrow beams require frequent beam alignment to ensure maximum beam gain of the link, requiring periodic beam search. Beam search complexity is proportional to the number of transmit-receive beam pairs. The larger beam-pair set will inherently delay the full search and hence the delay in finding the best possible beam pair. In this paper, we analyze the beam selection algorithm at user mobile equipment (UE) with device orientation change. We model a propagation channel with device orientation changes using the 3GPP channel model and understand the effect of it. Further, we propose a beam tracking and selection algorithm using orientation sensors to optimize the best beam selection procedure by reducing the beam search space. We show a possible practical implementation of the proposed algorithm on the actual mmWave device. Performance evaluation of the proposed algorithm is done both on the simulator and in the lab setup in various conditions. We illustrate the gains in downlink by throughput and in uplink by transmit power. For example, with this proposed algorithm, at 50Âo/sec orientation change rate, downlink throughput gain is 33% and the uplink power gain is 6 dB. Similarly, lab results show a throughput gain of 20% in the downlink with typical human usage scenarios.

Narrow Band Internet of Things (NB-IoT) is the specification defined by 3GPP for cellular IoT devices spanning a variety of IoT applications. Typical NB-IoT applications demand very long battery life. Improving battery life without impacting the application's communication needs is of great importance to extend the battery life in many applications. Coverage enhancement feature of NB-IoT requires eNB to choose a repetition from the finite set of values ranging from 1 to 2048. When the channel is favourable, user equipment (UE) may not need all the repetitions for decoding in downlink (DL). In this paper, we propose, an opportunistic early decode algorithm for downlink, using received bit information rate (RBIR) based link abstraction. This helps in reducing the total active time and hence the power consumption of device aiding the longer battery life. Performance gain of this algorithm is evaluated in various channel conditions of a fading environment and further validated the gains with a simplified analytical model of the considered system. Typical power savings observed are in the range of ∼45%.

Link failure is a cause of a major concern for network operators in enhancing user experience in present system and upcoming 5G systems as well. There are many factors which can cause link failures, for example Handover (HO) failures, poor coverage and congested cells. Network operators are constantly improving their coverage qualities to overcome these issues. However reducing the link failures needs further improvements for the present and next generation (5G) systems. In this paper, we study applicability of Machine Learning (ML) algorithms to reduce link failure at handover. In the method proposed, Signal conditions (RSRP/RSRQ) are continuously observed and tracked using Deep Neural Networks such as Recurrent Neural Network (RNN) or Long Short Term Memory network (LSTM) and thus behavior of these signal conditions are taken as inputs to another neural network which acts as a classifier classifying event in either HO fail or success in advance. This advance in decision allows UE to take action to mitigate the possible link failure. Algorithms and model proposed in this paper are first of its kind connecting the link between past signal conditions and future HO result. We show the performance of the proposed algorithms for both system simulated and field log data. Given the need for more proactive role of UE in most of the link level decision in 5G systems, algorithms proposed in this paper are more relevant.

Device to device (D2D) communication defined by 3GPP LTE in Rel-12 has been developed to handle D2D discovery and communications. LTE D2D transmissions occur in uplink resources, which mandates the UE supporting D2D feature to support reception of D2D signals in UL band or UL resources. Along with that, 3GPP has defined many new reference signals to support D2D discovery, communication and synchronization. Unlike legacy LTE UE, D2D UE also plays as a synchronization reference source by transmitting synchronization reference signals periodically in partial and out of coverage scenarios or when instructed by network to provide the synchronization reference for other D2D UEs. This makes the reception of D2D synchronization signals much more challenging compared to regular LTE synchronization. In this paper, we propose multiple low-complexity synchronization algorithms, covering various receiver design aspects and phases of complete synchronization procedure. A novel frequency hypothesis selection method for improved performance and sample normalization for robustness are introduced along with novel frequency offset localization and estimation method. The performance of the proposed methods is analyzed in detail. We also illustrate that these algorithms not just meet the 3GPP requirements, but have at least 2 dB of margin.

Machine Type Communication (MTC) device for cellular communication is being defined by 3GPP based on LTE in Rel-12 and Rel-13. Low cost has been the major objective of the definition for Rel-12 based specification. In Rel-13, coverage enhancement of MTC devices has been the major focus along with reduced power consumption, complexity and narrow bandwidth operation. Coverage enhancement (CE) of ∼15dB is expected to be achieved by using multiple power aggregation techniques, e.g. boosting Power Spectral Density (PSD), repetitions and relaxed requirements in few other cases. In CE scenarios, many of the receiver algorithms designed for normal coverage (NC) do not perform well. Requirement to operate at very low SINR changes the problem setup for few receiver algorithms. In this paper we introduce multiple UE receiver algorithms spanning various receiver functions, for coverage enhancement with reduced complexity. Specifically, practical cell search algorithm with complete procedure with joint time and frequency uncertainty hypothesis, novel timing estimation algorithm using PBCH, improved Channel Quality Index (CQI) estimation and novel method for opportunistic early decoding criterion based on mutual information metric. Algorithms proposed in this paper are first of its kind addressing coverage enhancement requirement for 3GPP LTE Rel-13 MTC devices. We show the performance of different algorithms proposed at very low SNRs as required for CE case.

Time tracking for Mobile Broadcast Single Frequency Network (MBSFN) subframes is known to be difficult due to very long delay spread channels. These kinds of channels induce several issues, e.g., Inter Symbol Interference (ISI), Inter Carrier Interference (ICI), Channel Estimation (CE) errors, which limit the throughput performance. In this paper, we propose an approach for timing correction, estimating the frame start time (FST) that maximizes the observed SINR. In contrast to the existing literature, we include the CE error besides the ISI and ICI into the analysis. For the simplicity of analysis, we consider linear interpolation method for CE. In particular, we show that the FST based on EAP need not be the best for the system performance. Moreover, our proposed alternate frame start time yields better system performance by balancing the ISI, ICI and CE error in effect. We then extend this method taking into account the practical limitations. Using simulation, we show that the proposed algorithm provides performance gain in the range of 3–8dB over conventional methods.