Showing papers on "Orthogonal frequency-division multiplexing published in 2018"

Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

Abstract: This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect/recover the transmitted symbols using the estimated CSI, the proposed deep learning-based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference.

Interference Cancellation and Iterative Detection for Orthogonal Time Frequency Space Modulation

Abstract: The recently proposed orthogonal time–frequency–space (OTFS) modulation technique was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) over delay-Doppler channels. In this paper, we first derive the explicit input–output relation describing OTFS modulation and demodulation (mod/demod). We then analyze the cases of: 1) ideal pulse-shaping waveforms that satisfy the bi-orthogonality conditions and 2) rectangular waveforms which do not. We show that while only inter-Doppler interference (IDI) is present in the former case, additional inter-carrier interference (ICI) and inter-symbol interference (ISI) occur in the latter case. We next characterize the interferences and develop a novel low-complexity yet efficient message passing (MP) algorithm for joint interference cancellation (IC) and symbol detection. While ICI and ISI are eliminated through appropriate phase shifting, IDI can be mitigated by adapting the MP algorithm to account for only the largest interference terms. The MP algorithm can effectively compensate for a wide range of channel Doppler spreads. Our results indicate that OTFS using practical rectangular waveforms can achieve the performance of OTFS using ideal but non-realizable pulse-shaping waveforms. Finally, simulation results demonstrate the superior error performance gains of the proposed uncoded OTFS schemes over OFDM under various channel conditions.

IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular Communication-Radar System

Abstract: Millimeter-wave (mmWave) radar is widely used in vehicles for applications such as adaptive cruise control and collision avoidance. In this paper, we propose an IEEE 802.11ad-based radar for long-range radar (LRR) applications at the 60 GHz unlicensed band. We exploit the preamble of a single-carrier physical layer frame, which consists of Golay complementary sequences with good correlation properties that make it suitable for radar. This system enables a joint waveform for automotive radar and a potential mmWave vehicular communication system based on the mmWave consumer wireless local area network standard, allowing hardware reuse. To formulate an integrated framework of vehicle-to-vehicle communication and LRR, we make typical assumptions for LRR applications, incorporating the full duplex radar operation. This new feature is motivated by the recent development of systems with sufficient isolation and self-interference cancellation. We develop single- and multi-frame radar receiver algorithms for target detection as well as range and velocity estimation for both single- and multi-target scenarios. Our proposed radar processing algorithms leverage channel estimation and time–frequency synchronization techniques used in a conventional IEEE 802.11ad receiver with minimal modifications. Analysis and simulations show that in a single-target scenario, a gigabits-per-second data rate is achieved simultaneously with cm-level range accuracy and cm/s-level velocity accuracy. The target vehicle is detected with a high probability (above 99.99 $\%$ ) at a low false alarm rate of 10 $^{-6}$ for an equivalent isotropically radiated power of 40 dBm up to a vehicle separation distance of about 200 m. The proposed IEEE 802.11ad-based radar meets the minimum accuracy/resolution requirement of range and velocity estimates for LRR applications.

Orthogonal Time Frequency Space Modulation

Abstract: This paper introduces a new two-dimensional modulation technique called Orthogonal Time Frequency Space (OTFS) modulation. OTFS has the novel and important feature of being designed in the delay-Doppler domain. When coupled with a suitable equalizer, OTFS modulation is able to exploit the full channel diversity over both time and frequency. Moreover, it converts the fading, time-varying wireless channel experienced by modulated signals such as OFDM into a time-independent channel with a complex channel gain that is essentially constant for all symbols. This design obviates the need for transmitter adaptation, and greatly simplifies system operation. The paper describes the basic operating principles of OTFS as well as a possible implementation as an overlay to current or anticipated standardized systems. OTFS is shown to provide significant performance improvement in systems with high Doppler, short packets, and/or large antenna array. In particular, simulation results indicate at least several dB of block error rate performance improvement for OTFS over OFDM in all of these settings.

Modulation and Multiple Access for 5G Networks

Abstract: Fifth generation (5G) wireless networks face various challenges in order to support large-scale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore, in this paper we present a comprehensive overview of the most promising modulation and MA schemes for 5G networks. Unlike other surreys of 5G networks, this paper focuses on multiplexing techniques, including modulation techniques in orthogonal MA (OMA) and various types of non-OMA (NOMA) techniques. Specifically, we first introduce different types of modulation schemes, potential for OMA, and compare their performance in terms of spectral efficiency, out-of-band leakage, and bit-error rate. We then pay close attention to various types of NOMA candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration, we can identify the opportunities and challenges that will have the most significant impacts on modulation and MA designs for 5G networks.

Modulation in the Air: Backscatter Communication Over Ambient OFDM Carrier

Abstract: Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors and tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology, also called “modulation in the air,” has emerged as a promising solution to achieve green communication for future Internet of Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed as an integer multiplication of the OFDM symbol period, and the waveform for BD bit “0” maintains the same state within the BD symbol period, while the waveform for BD bit “1” has a state transition in the middle of each OFDM symbol period within the BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic which is a linear combination of the per-antenna test statistics and derive the corresponding optimal detector. The proposed optimal detectors require only knowing the strength of the backscatter channel, thus simplifying their implementation. Moreover, practical timing synchronization algorithms are proposed for the designed AmBC system, and we also analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate performance and the operating range significantly and achieve much higher data rate, as compared with the conventional design.

Cooperative Ambient Backscatter Communications for Green Internet-of-Things

Abstract: Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.

Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems

Abstract: Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.

A Novel PAPR Reduction Scheme for OFDM System Based on Deep Learning

Abstract: High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.

ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers

Abstract: In this letter, we propose a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications. Different from the data-driven fully connected deep neural network (FC-DNN) method, we adopt the block-by-block signal processing method that divides the receiver into channel estimation subnet and signal detection subnet. Each subnet is constructed by a DNN and uses the existing simple and traditional solution as initialization. The proposed model-driven DL receiver offers more accurate channel estimation comparing with the linear minimum mean-squared error method and exhibits higher data recovery accuracy comparing with the existing methods and FC-DNN. Simulation results further demonstrate the robustness of the proposed approach in terms of signal-to-noise ratio and its superiority to the FC-DNN approach in the computational complexities or the memory usage.

OFDM-Autoencoder for End-to-End Learning of Communications Systems

Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely single-tap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators. We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training. We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels. Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.

Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems

Abstract: Configuring the hybrid precoders and combiners in a millimeter wave multiuser multiple-input multiple-output system is challenging in frequency selective channels. In this paper, we develop a system that uses compressive estimation on the uplink to configure precoders and combiners for the downlink. In the first step, the base station (BS) simultaneously estimates the channels from all the mobile stations on each subcarrier. To reduce the number of measurements required, compressed sensing techniques are developed that exploit common support on the different subcarriers. In the second step, exploiting reciprocity and the channel estimates the BS designs hybrid precoders and combiners. Two algorithms are developed for this purpose, with different performance and complexity tradeoffs: First, a factorization of the purely digital solution; and second, an iterative hybrid design. Extensive numerical experiments evaluate the proposed solutions comparing to the state-of-the-art strategies, and illustrating design tradeoffs in overhead, complexity, and performance.

Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

Abstract: Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

LTE receiver design and multipath analysis for navigation in urban environments

Abstract: Mitigating multipath of cellular long-term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase-locked loop (PLL)–aided delay-locked loop (DLL) to track the received LTE signals, is presented. The PLL-aided DLL uses orthogonal frequency division-multiplexing (OFDM)–based discriminator functions to estimate and track the time-of-arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).

Secure and Precise Wireless Transmission for Random-Subcarrier-Selection-Based Directional Modulation Transmit Antenna Array

Abstract: In this paper, a practical wireless transmission scheme is proposed to transmit confidential messages to the desired user securely and precisely by the joint use of multiple techniques, including artificial noise (AN) projection, phase alignment/beamforming, and random subcarrier selection (RSCS) based on orthogonal frequency division multiplexing (OFDM), and directional modulation (DM), namely RSCS-OFDM-DM. This RSCS-OFDM-DM scheme provides an extremely low-complexity structure for the desired receiver and makes the secure and precise wireless transmission realizable in practice. For illegal eavesdroppers, the receive power of confidential messages is so weak that their receivers cannot intercept these confidential messages successfully once it is corrupted by AN. In such a scheme, the design of phase alignment/beamforming vector and AN projection matrix depends intimately on the desired direction angle and distance. It is particularly noted that the use of RSCS leads to a significant outcome that the receive power of confidential messages mainly concentrates on the small neighboring region around the desired receiver and only small fraction of its power leaks out to the remaining large broad regions. This concept is called secure precise transmission. The probability density function of real-time receive signal-to-interference-and-noise ratio (SINR) is derived. Also, the average SINR and its tight upper bound are attained. The approximate closed-form expression for average secrecy rate is derived by analyzing the first-null positions of the SINR and clarifying the wiretap region. Simulation and analysis show that the proposed scheme actually can achieve a secure and precise wireless transmission of confidential messages in line-of-propagation channel, and the derived theoretical formula of average secrecy rate is verified to coincide with the exact results well for medium and large scale transmit antenna array or in the low and medium SNR regions.

Filtered OFDM Systems, Algorithms, and Performance Analysis for 5G and Beyond

Abstract: Filtered orthogonal frequency division multiplexing (F-OFDM) system is a promising waveform for 5G and beyond to enable the multi-service system and spectrum efficient network slicing. However, the performance for F-OFDM systems has not been systematically analyzed in the literature. In this paper, we first establish a mathematical model for an F-OFDM system and derive the conditions to achieve the interference-free one-tap channel equalization. In the practical cases (e.g., insufficient guard interval, asynchronous transmission, and so on), the analytical expressions for inter-symbol interference, inter-carrier interference, and adjacent-carrier interference are derived, where the last term is considered as one of the key factors for asynchronous transmissions. Based on the framework, an optimal power compensation matrix is derived to make all of the subcarriers having the same ergodic performance. Another key contribution of this paper is that we propose a multi-rate F-OFDM system to enable low-complexity low-cost communication scenarios, such as narrow-band Internet of Things, at the cost of generating inter-subband interference (ISubBI). Low computational complexity algorithms are proposed to cancel the ISubBI. The result shows that the derived analytical expressions match the simulation results, and the proposed ISubBI cancelation algorithms can significantly save the original F-OFDM complexity (up to 100 times) without significant performance loss.

On OTFS Modulation for High-Doppler Fading Channels

Abstract: Orthogonal time frequency space (OTFS) modulation is a 2-dimensional (2D) modulation scheme designed in the delay-Doppler domain, unlike traditional modulation schemes which are designed in the time-frequency domain. Through a series of 2D transformations, OTFS converts a doubly-dispersive channel into an almost non-fading channel in the delay-Doppler domain. In this domain, each symbol in a frame experiences an almost constant fade, thus achieving significant performance gains over existing modulation schemes such as OFDM. The sparse delay-Doppler impulse response which reflects the actual physical geometry of the wireless channel enables efficient channel estimation, especially in high-Doppler fading channels. This paper investigates OTFS from a signal detection and channel estimation perspective, and proposes a Markov chain Monte-Carlo sampling based detection scheme and a pseudo-random noise (PN) pilot based channel estimation scheme in the delay-Doppler domain.

Embedded Pilot-Aided Channel Estimation for OTFS in Delay-Doppler Channels

Abstract: Orthogonal time frequency space (OTFS) modulation was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in delay--Doppler channels. In order to detect OTFS modulated data, the channel impulse response needs to be known at the receiver. In this paper, we propose embedded pilot-aided channel estimation schemes for OTFS. In each OTFS frame, we arrange pilot, guard, and data symbols in the delay--Doppler plane to suitably avoid interference between pilot and data symbols at the receiver. We develop such symbol arrangements for OTFS over multipath channels with integer and fractional Doppler shifts, respectively. At the receiver, channel estimation is performed based on a threshold method and the estimated channel information is used for data detection via a message passing (MP) algorithm. Thanks to our specific embedded symbol arrangements, both channel estimation and data detection are performed within the same OTFS frame with a minimum overhead. We compare by simulations the error performance of OTFS using the proposed channel estimation and OTFS with ideally known channel information and observe only a marginal performance loss. We also demonstrate that the proposed channel estimation in OTFS significantly outperforms OFDM with known channel information. Finally, we present extensions of the proposed schemes to MIMO and multi-user uplink/downlink.

Spatial-Wideband Effect in Massive MIMO with Application in mmWave Systems

Abstract: Massive MIMO, especially in the millimeter- wave frequency bands, has been recognized as a promising technique to enhance spectrum and energy efficiency, as well as network coverage for wireless communications. Most research in massive MIMO just uses the extended conventional MIMO channel model by directly assuming that the channel dimensionality becomes large. With massive numbers of antennas, however, there exists a non-negligible propagation delay across the large array aperture, which then causes a transmitted symbol to reach different antennas with different delays, thereby rendering conventional MIMO channel models inapplicable. Such a phenomenon is known as the spatial-wideband effect in the areas of array signal processing and radar signal processing, and introduces the beam squint effect in beamforming. However, the spatial-wideband effect and the related beam squint issue are seldom studied in massive MIMO communications. To design a practical massive MIMO system, it is important to understand when the spatial-wideband effect appears and how it affects signal transmission, how the spatial-wideband effect interacts with the frequency-wideband effect (frequency selectivity), especially for multi-carrier modulations such as orthogonal frequency- division multiplexing (OFDM), and how we should re-design the transceiver. In this article we suggest a new massive MIMO channel model that embraces both the spatial- and frequency- wideband effects, and discuss these issues.

Low Complexity Modem Structure for OFDM-Based Orthogonal Time Frequency Space Modulation

Abstract: Orthogonal time frequency space (OTFS) modulation is a 2-D signaling technique that has recently emerged in the literature to tackle the time-varying (TV) wireless channels. OTFS deploys the Doppler-delay plane to multiplex the transmit data where the time variations of the TV channel are integrated over time and hence the equivalent channel relating the input and output of the system boils down to a time-invariant one. This signaling technique can be implemented on the top of a given multicarrier waveform with the addition of precoding and post-processing units to the modulator and demodulator. In this letter, we present discrete-time formulation of an orthogonal frequency division multiplexing-based OTFS system. We argue against deployment of window functions at the OTFS transmitter in realistic scenarios and thus limit any sort of windowing to the receiver side. We study the channel impact in discrete-time providing deeper insights into OTFS systems. Moreover, our derivations lead to simplified modulator and demodulator structures that are far simpler than those in the literature.

Low-complexity iterative detection for orthogonal time frequency space modulation

Abstract: We elaborate on the recently proposed orthogonal time frequency space (OTFS) modulation technique, which provides significant advantages over orthogonal frequency division multiplexing (OFDM) in Doppler channels. We first derive the input-output relation describing OTFS modulation and demodulation (mod/demod) for delay-Doppler channels with arbitrary number of paths, with given delay and Doppler values. We then propose a low-complexity message passing (MP) detection algorithm, which is suitable for large-scale OTFS taking advantage of the inherent channel sparsity. Since the fractional Doppler paths (i.e., not exactly aligned with the Doppler taps) produce the inter Doppler interference (IDI), we adapt the MP detection algorithm to compensate for the effect of IDI in order to further improve performance. Simulations results illustrate the superior performance gains of OTFS over OFDM under various channel conditions.

OFDM-Autoencoder for End-to-End Learning of Communications Systems

Abstract: We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations. This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators. We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training. We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels. Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.

Mixed Numerologies Interference Analysis and Inter-Numerology Interference Cancellation for Windowed OFDM Systems

Abstract: Extremely diverse service requirements are one of the critical challenges for the upcoming fifth-generation (5G) radio access technologies. As a solution, mixed numerologies transmission is proposed as a new radio air interface by assigning different numerologies to different subbands. However, coexistence of multiple numerologies induces the inter-numerology interference (INI), which deteriorates the system performance. In this paper, a theoretical model for INI is established for windowed orthogonal frequency division multiplexing (W-OFDM) systems. The analytical expression of the INI power is derived as a function of the channel frequency response of interfering subcarrier, the spectral distance separating the aggressor and the victim subcarrier, and the overlapping windows generated by the interferer's transmitter windows and the victim's receiver window. Based on the derived INI power expression, a novel INI cancelation scheme is proposed by dividing the INI into a dominant deterministic part and an equivalent noise part. A soft-output ordered successive interference cancelation (OSIC) algorithm is proposed to cancel the dominant interference, and the residual interference power is utilized as effective noise variance for the calculation of log-likelihood ratios for bits. Numerical analysis shows that the INI theoretical model matches the simulated results, and the proposed interference cancelation algorithm effectively mitigates the INI and outperforms the state-of-the-art W-OFDM receiver algorithms.

Generalized Multiple-Mode OFDM With Index Modulation

Abstract: Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM), which transmits an OFDM signal with information bits embedded onto multiple distinguishable signal constellations of the same cardinality and their permutations, is a recently proposed IM technique in the frequency domain. It is capable of achieving higher spectral efficiency and better error performance than classical OFDM and existing frequency-domain IM schemes. In this paper, we propose the scheme of generalized (G-) MM-OFDM-IM, which allows a different subcarrier to utilize a signal constellation of a different size while conveying the same number of IM bits. Considering phase shift keying constellations, we present design guidelines for GMM-OFDM-IM to achieve an optimal error performance in the asymptotically high signal-to-noise ratio region. A computationally efficient and near-optimal detector based on the idea of sequential decoding is also tailored to GMM-OFDM-IM, which avoids the detection of an illegitimate constellation permutation. Monte Carlo simulations are conducted to validate the inherent properties and advantages of GMM-OFDM-IM.

Performance Assessment of IEEE 802.11p with an Open Source SDR-Based Prototype

Abstract: We present a complete simulation and experimentation framework for IEEE 802.11p. The core of the framework is a Software Defined Radio (SDR)-based Orthogonal Frequency Division Multiplexing (OFDM) transceiver that we validated extensively by means of simulations, interoperability tests, and, ultimately, by conducting a field test. Being SDR-based, the transceiver offers important benefits: It provides access to all data down to and including the physical layer, allowing for a better understanding of the system. Based on open and programmable hardware and software, the transceiver is completely transparent and all implementation details can be studied and, if needed, modified. Finally, it enables a seamless switch between simulations and experiments and, thus, helps to bridge the gap between theory and practice. Comparing the transceiver’s performance with independent results from simulations and experiments, we underline its potential to be used as a tool for further studies of IEEE 802.11p networks both in field operational tests as well as for simulation-based development of novel physical layer solutions. To make the framework accessible to fellow researchers and to allow reproduction of the results, we released it under an Open Source license.

Analysis of Discrete-Time MIMO OFDM-Based Orthogonal Time Frequency Space Modulation

Abstract: Orthogonal Time Frequency Space (OTFS) is a novel modulation scheme designed in the Doppler-delay domain to fully exploit time and frequency diversity of general time-varying channels. In this paper, we present a novel discrete-time analysis of OFDM-based OTFS transceiver with a concise and vectorized input-output relationship that clearly characterizes the contribution of each underlying signal processing block in such systems. When adopting cyclic prefix in the time domain, our analysis reveals that the proposed MIMO OTFS and OFDM systems have the same ergodic capacity despite the well-known fact that the former has great advantages in low-complexity receiver design for high Doppler channels. The proposed discrete-time vectorized formulation is applicable to general fast fading channels with arbitrary window functions. It also enables practical low-complexity receiver design for which such a concise formulation of the input-output relationship is of great benefits.

On OTFS Modulation for High-Doppler Fading Channels

Abstract: Orthogonal time frequency space (OTFS) modulation is a 2-dimensional (2D) modulation scheme designed in the delay-Doppler domain, unlike traditional modulation schemes which are designed in the time-frequency domain Through a series of 2D transformations, OTFS converts a doubly-dispersive channel into an almost non-fading channel in the delay-Doppler domain In this domain, each symbol in a frame experiences an almost constant fade, thus achieving significant performance gains over existing modulation schemes such as OFDM The sparse delay-Doppler impulse response which reflects the actual physical geometry of the wireless channel enables efficient channel estimation, especially in high-Doppler fading channels This paper investigates OTFS from a signal detection and channel estimation perspective, and proposes a Markov chain Monte-Carlo sampling based detection scheme and a pseudo-random noise (PN) pilot based channel estimation scheme in the delay-Doppler domain

Non-Orthogonal Multiple Access: A Unified Perspective

Abstract: Non-orthogonal multiple access (NOMA) is a promising technique for future mobile communication systems, which can approach multiuser channel capacity by sharing the same time-frequency resources with multiple users. In this article, we provide a unified framework for NOMA and review the principles of various NOMA schemes in different domains with the objective of creating a unified framework. A systematic performance comparison of different NOMA schemes regarding their peak-to-average power ratio, receiver complexity, latency, grant-free access, user load, and peak throughput is also provided for different application scenarios. Relying on our unified framework, we generalize the current understanding of the NOMA principle from the conventional code and power domains to the spatial domain as well as to their hybrids and to the networking domain. Finally, the challenges in terms of resource allocation, channel estimation, security, system flexibility, and implementation issues are also discussed.

Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM

Abstract: Reservoir computing (RC) is a class of neuromorphic computing approaches that deals particularly well with time-series prediction tasks. It significantly reduces the training complexity of recurrent neural networks and is also suitable for hardware implementation whereby device physics are utilized in performing data processing. In this paper, the RC concept is applied to detecting a transmitted symbol in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Due to wireless propagation, the transmitted signal may undergo severe distortion before reaching the receiver. The nonlinear distortion introduced by the power amplifier at the transmitter may further complicate this process. Therefore, an efficient symbol detection strategy becomes critical. The conventional approach for symbol detection at the receiver requires accurate channel estimation of the underlying MIMO-OFDM system. However, in this paper, we introduce a novel symbol detection scheme where the estimation of the MIMO-OFDM channel becomes unnecessary. The introduced scheme utilizes an echo state network (ESN), which is a special class of RC. The ESN acts as a black box for system modeling purposes and can predict nonlinear dynamic systems in an efficient way. Simulation results for the uncoded bit error rate of nonlinear MIMO-OFDM systems show that the introduced scheme outperforms conventional symbol detection methods.