Narrowband Channels Research Articles

Caching in Narrow-Band Burst-Error Channels via Meta Self-Supervision Learning

We consider a coordinated small-cell network in narrow-band deep fading channels, where the transmissions from small base-stations (SBSs) to users usually encounter burst errors. Each SBS has limited cache-capacity via which partial contents can be cached to avoid being fetched from remote cloud and hence reduce the transmission delay. However, due to deep fading, the content transmission may occur numerous burst errors. To solve this problem, in this letter, a meta-based self-supervision learning method is introduced to find the optimal cache policy for minimizing the burst errors. In contrast to the traditional methods, the proposed one has better performance and learning efficiency. More particularly, the proposed can determine much proper cache policy by just training with a few samples of average fading duration of each channel. We demonstrate the effectiveness of the proposed method by numerical simulations which indicate that the proposed meta-learning method is superior to common existing methods in combat of the burst-error problem regarding convergence time and accuracy.

Compressive sensing-based channel estimation schemes for wideband millimeter wave wireless communication systems

The introduction of both hybrid precoder-combiner structure and low-bit resolution analog-to-digital converters (ADCs) makes channel estimation in millimeter (mm) Wave Massive MIMO systems to be challenging. In the literature, several channel estimation schemes presented focus on narrowband channels. However, few works are found to be presented for the case of wideband channels for this system. In this paper, two new channel estimation techniques are proposed with the first scheme based on the Projection-based Orthogonal Matching Pursuit (POMP) algorithm. The second estimation scheme is based on the Predictive Orthogonal Least Squares (POLS) algorithm. In all the simulation results, POLS shows the best performance but with the highest computational complexity cost, with POMB exhibiting the next best performance with a lower computational complexity cost. In terms of compromise between performance and computational complexity cost, the proposed POLS-based channel estimator is the best estimator for the wideband mmWave Massive MIMO systems.

Modeling and Analysis of MIMO Multipath Channels With Aerial Intelligent Reflecting Surface

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Dual-Polarization Bandwidth-Bridged Bandpass Sampling Fourier Transform Spectrometer from Visible to Near-Infrared on a Silicon Nitride Platform.

On-chip broadband optical spectrometers that cover the entire tissue transparency window (λ = 650–1050 nm) with high resolution are highly demanded for miniaturized biosensing and bioimaging applications. The standard spatial heterodyne Fourier transform spectrometer (SHFTS) requires a large number of Mach–Zehnder interferometer (MZI) arrays to obtain a broad spectral bandwidth while maintaining high resolution. Here, we propose a novel type of SHFTS integrated with a subwavelength grating coupler (SWGC) for the dual-polarization bandpass sampling on the Si3N4 platform to solve the intrinsic trade-off limitation between the bandwidth and resolution of the SHFTS without having an outrageous number of MZI arrays or adding additional active photonic components. By applying the bandpass sampling theorem, the continuous broadband input spectrum is divided into multiple narrow-band channels through tuning the phase-matching condition of the SWGC with different polarization and coupling angles. Thereby, it is able to reconstruct each band separately far beyond the Nyquist criterion without aliasing error or degrading the resolution. We experimentally demonstrated the broadband spectrum retrieval results with the overall bandwidth coverage of 400 nm, bridging the wavelengths from 650 to 1050 nm, with a resolution of 2–5 nm. The bandpass sampling SHFTS is designed to have 32 linearly unbalanced MZIs with the maximum optical path length difference of 93 μm within an overall footprint size of 4.7 mm × 0.65 mm, and the coupling angles of SWGC are varied from 0° to 32° to cover the entire tissue transparency window.

Geodesic Path Model for Indoor Propagation Loss Prediction of Narrowband Channels.

Indoor path loss models characterize the attenuation of signals between a transmitting and receiving antenna for a certain frequency and type of environment. Their use ranges from network coverage planning to joint communication and sensing applications such as localization and crowd counting. The need for this proposed geodesic path model comes forth from attempts at path loss-based localization on ships, for which the traditional models do not yield satisfactory path loss predictions. In this work, we present a novel pathfinding-based path loss model, requiring only a simple binary floor map and transmitter locations as input. The approximated propagation path is determined using geodesics, which are constrained shortest distances within path-connected spaces. However, finding geodesic paths from one distinct path-connected space to another is done through a systematic process of choosing space connector points and concatenating parts of the geodesic path. We developed an accompanying tool and present its algorithm which automatically extracts model parameters such as the number of wall crossings on the direct path as well as on the geodesic path, path distance, and direction changes on the corners along the propagation path. Moreover, we validate our model against path loss measurements conducted in two distinct indoor environments using DASH-7 sensor networks operating at 868 MHz. The results are then compared to traditional floor-map-based models. Mean absolute errors as low as 4.79 dB and a standard deviation of the model error of 3.63 dB is achieved in a ship environment, almost half the values of the next best traditional model. Improvements in an office environment are more modest with a mean absolute error of 6.16 dB and a standard deviation of 4.55 dB.

Modification over Dithered DPLL to reduce the effect of narrowband channel interference

Digital signal processing based digital phase locked loop (DSP DPLL) are most commonly used for carrier phase tracking in the recent times. Phase locked loop (PLL) behaves in nonlinear fashion at the time of signal acquisition. The linearity is restored in the PLL behavior once acquisition is over and signal tracking is taking place. But the same PLL or DSP-DPLL shows non-linearity both during acquisition and tracking when narrowband channel interference is present in the received signal. In this paper, a single tone signal is introduced as channel interference to study the effect on DPLL and its modified versions. To overcome the effect of channel interference, a single tone dither signal is included in the loop of DPLL. Although an improvement in acquisition and tracking performance is observed, but addition of dither signal contributes to the phase error variance and consequently output noise to increase. Therefore, a further modification is proposed by incorporating an additional phase control in the digital control oscillator (DCO) of the loop to improve the output SNR. The proposed loop is implemented on reconfigurable logic platform using System Generator, a tool from Xilinx used to design real time DSP application. The hardware simulation results demonstrate a comparison among traditional DPLL, dithered DPLL and phase controlled dithered DPLL where the proposed version of the loop outperforms others in terms of acquisition and noise rejection. Keywords DSP Digital Phase Locked Loop, Phase Controlled DPLL, Channel Interference, Dither DPLL, VHDL System Design, Modified DCO

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Detection of marine aerosols using ocean colour sensors

Ocean colour sensors are equipped with atmospheric correction bands operating in the near infrared region of the spectrum. At these wavelengths, the ocean surface, because of high infrared absorption by water, acts as a dark background and the sensor detected radiance is due to solar radiation backscattered by the atmospheric air molecules and aerosols called Rayleigh and aerosol path radiances respectively. From the radiances in the atmospheric correction bands, after accounting for Rayleigh path radiance, it is possible to determine aerosol parameters like aerosol optical depth (AOD) and particle size distribution index. 
 
 While sensors like IRS P3 MOS-B, IRS P4 OCM, NOAA-AVHRR, etc detect AOD in the total atmospheric column, IRS P3 MOS-A sensor can detect AOD in two atmospheric layers by exploiting the differential absorption property of oxygen at four narrow band channels at the O2 A band around 760nm. The method of determination from ocean colour sensors and the results from IRS P3 MOS-B, IRS P4-OCM and IRS P3 MOS-A radiance data are presented and discussed.

Proposal to modify the Russian standard for electronic digital signature in order to eliminate the broadband covert channel

Most digital signature algorithms have the ability to organize hidden channels for transmitting information. Their presence was revealed after the publication of a digital signature scheme based on the El-Gamal cipher. The bandwidth of these channels ranges from a few bits (narrowband channels) to 256-512 bits (broadband channels). The literature describes several ways to eliminate broadband channels with the help of a supervisor. The analysis of existing methods of elimination of hidden channels in digital signature algorithms is given. The shortcomings of the existing schemes are revealed. A method of modification of the Russian standard of electronic digital signature GOST R 34.10-2012 has been developed in order to eliminate the broadband hidden channel. The method provides for the presence of a supervisor who does not participate in the formation of the signature and can check signed messages for the absence of hidden channels.

A Comparison of Distribution Models for Fast Variations in the Indoor Radio Channel at 5G Frequency Range 1 Microwave Bands

The use of long-term (slow variations) for modelling radio propagation behavior in indoor scenarios limits the validity of such models, as most of the environments induced fast variations overlapped with the slow ones. Real world data gathered at different locations in two bands within Fifth Generation FR1 spectrum, 3 GHz and 5 GHz, provide insight on the effect of such fast variations and on the distribution models that would be useful to complement the long term analysis with short term behavior in order to improve the design of such 5G microwave networks. Comparisons among short term performance taking into account both the frequency, the visibility conditions and the environment size, shape and furnishing are presented with the focus on modelling as accurate as possible the narrowband channel. The use of ΔBIC (describing the difference between Bayesian Information Criteria indexes of each fitting) complements and confirms the insight provided by direct inspection on traditional fitting plots. This parameter could be interesting for future network deployments.

Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification

The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy.Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.

The Internet of Bodies: A Systematic Survey on Propagation Characterization and Channel Modeling

The Internet of Bodies (IoBs) is an imminent extension to the vast Internet of Things domain, where interconnected devices (e.g., worn, implanted, embedded, swallowed, etc.) are located in-on-and-around the human body form a network. Thus, the IoB can enable a myriad of services and applications for a wide range of sectors, including medicine, safety, security, wellness, entertainment, to name but a few. Especially, considering the recent health and economic crisis caused by the novel coronavirus pandemic, also known as COVID-19, the IoB can revolutionize today’s public health and safety infrastructure. Nonetheless, reaping the full benefit of IoB is still subject to addressing related risks, concerns, and challenges. Hence, this survey first outlines the IoB requirements and related communication and networking standards. Considering the lossy and heterogeneous dielectric properties of the human body, one of the major technical challenges is characterizing the behavior of the communication links in-on-and-around the human body. Therefore, this article presents a systematic survey of channel modeling issues for various link types of human body communication (HBC) channels below 100 MHz, the narrowband (NB) channels between 400 and 2.5 GHz, and ultrawideband (UWB) channels from 3 to 10 GHz. After explaining bio-electromagnetics attributes of the human body, physical, and numerical body phantoms are presented along with electromagnetic propagation tool models. Then, the first-order and the second-order channel statistics for NB and UWB channels are covered with a special emphasis on body posture, mobility, and antenna effects. For capacitively, galvanically, and magnetically coupled HBC channels, four different channel modeling methods (i.e., analytical, numerical, circuit, and empirical) are investigated, and electrode effects are discussed. Finally, interested readers are provided with open research challenges and potential future research directions.

Orthogonal Delay Scale Space Modulation: A New Technique for Wideband Time-Varying Channels

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed scheme for time-varying narrowband channels in terrestrial radio-frequency communications. Underwater acoustic (UWA) and ultra-wideband (UWB) communication systems, on the other hand, confront wideband time-varying channels. Unlike narrowband channels, for which time contractions or dilations due to Doppler effect can be approximated by frequency-shifts, the Doppler effect in wideband channels results in frequency-dependent non-uniform shift of signal frequencies across the band. In this paper, we develop an OTFS-like modulation scheme -- Orthogonal Delay Scale Space (ODSS) modulation -- for handling wideband time-varying channels. We derive the ODSS transmission and reception schemes from first principles. In the process, we introduce the notion of -convolution in the delay-scale space that parallels the twisted convolution used in the time-frequency space. The preprocessing 2D transformation from the Fourier-Mellin domain to the delay-scale space in ODSS, which plays the role of inverse simplectic Fourier transform (ISFFT) in OTFS, improves the bit error rate performance compared to OTFS and Orthogonal Frequency Division Multiplexing (OFDM) in wideband time-varying channels. Furthermore, since the channel matrix is rendered near-diagonal, ODSS retains the advantage of OFDM in terms of its low-complexity receiver structure.

Hybrid Precoding for Millimeter Wave MIMO: Trace Optimization Approach

The hybrid precoding problem in point-to-point millimeter wave (mmWave) multiple input multiple output (MIMO) for narrowband channel is established as a Frobenius norm minimization problem. It is translated into trace minimization problem, and two algorithms are proposed to solve it. In the first method based on alternating minimization, we alternately determine the analog precoder and digital precoder, keeping the other constant to minimize the trace. The analog precoding subproblem with the fixed digital precoder is converted into a semi-definite programming (SDP) problem and solved by block coordinate descent (BCD) algorithm with suitable modifications. In the second method, we segregate the analog precoding and digital precoding subproblems by considering orthogonality of analog precoder. The analog precoding is further rephrased as a trace maximization problem and solved by an iterative power method by enforcing orthogonality constraint on the analog precoder. The adapted form of modified Gramm-Schmidt orthogonalization procedure is employed to impose orthogonality on the analog precoder. The proposed methods are extended for wideband channel by considering orthogonal frequency division multiplexing (OFDM). The proposed methods not only exhibit a good performance but also come with lower computational complexity when compared to existing methods with comparable performances.

FLGS-04. Fluorescence lifetime imaging (FLIm) is a dye-free, high sensitivity approach for fluorescence guided surgery in high-grade and low-grade gliomas

Abstract INTRODUCTION Fluorescence-guided surgery can improve tumor identification and extent of surgical resection. 5-ALA is the standard for GBM, but is limited by lack of quantitative fluorescence, a need to work in the dark, and a lack of sensitivity for low grade tumors. We have developed a novel instrument for dye-free tissue autofluorescence lifetime imaging (FLIm) to identify glioma tissue during resection. This approach utilizes time-resolved autofluorescence measurements in narrow-band channels to assess markers of tissue metabolism. Compared to intensity-based imaging of exogenous fluorophores, FLIm has greater sensitivity without dependence on background lighting. The advantages of FLIm include quantitative tissue analysis, the ability to work under full light conditions, sensitivity for high and low grade gliomas, and the potential ability to identify IDH mutational status. In this study, we validated the use of FLIm for identification of glioma tissue at tumor resection margins. METHODS FLIm was used to image tissue margins during glioma resections and compared to microbiopsies from imaged regions to correlate fluorescence with histopathology. RESULTS FLIm was applied intraoperatively to 11 GBM and 5 LGG patients (7 imaged biopsies per patient). In GBM, tumor infiltration of cortex was associated with significantly decreased fluorescence lifetime (FL) in channels 2 (470/28nm;p<0.05) and 3 (542/50nm;p<0.002). In subcortical margins, FL was inversely proportional to the density of tumor in channels 2,3 (p<0.05). When IDH wild-type GBMs were compared to IDH1-mutant tumors, FL was noted to be significantly longer in channel 1 (390/40nm;p<0.05), and trended towards longer FL in channel 2, shorter FL in channel 3. In LGG, FL was significantly correlated with tumor density in channel 2 (p<0.01). CONCLUSIONS FLIm is a dye-free, quantitative alternative to 5-ALA for fluorescence guided glioma resections with sensitivity to high and low-grade tumors, and the ability to predict IDH mutations in GBM. Further validation studies are on-going.

Narrow- or wide-band channel for a high baud rate fiber communication system: a judgment based on a temporal and spectral evolution PMD model.

In optical fiber communication systems, the tendency to prefer increasingly higher baud rates, up to 100 or 200 Gbaud, compels us to deal with wideband fiber channels in which polarization effects must be treated in a more complicated manner. In contrast to the conventional 50 GHz bandwidth wavelength division multiplexing channel, polarization mode dispersion (PMD) is observed in an all-order manner in a wideband fiber channel beyond 100 GHz, rather than only first- and second-order. In this study, certain principles that should be followed in establishing a PMD emulation model in a wideband fiber channel were put forward. Then, a temporal and spectral evolution PMD model that can reflect and emulate the evolution features of the polarization effects in a real fiber channel was proposed and demonstrated. The analyses show the PMD model satisfies all the principles for the wideband channels. Finally, we suggested some approaches to define a narrowband or wideband channel only considering the polarization effects to deal with PMD in a high baud rate optical fiber communication system.

A High Speed Underwater Wireless Communication Through a Novel Hybrid Opto-Acoustic Modem Using MIMO-OFDM

For efficient underwater opto/acoustic communication, this research proposes the use of MIMO in conjunction with OFDM. OFDM (Orthogonal Frequency-Division Multiplexing) and MIMO (Multiple Input Multiple Output) systems may be widely used in wireless networks to provide high data transfer rates, resistance to multipath fading, and an increase in the channel's Spatial Multiplexing and Spatial Diversity Gain. Transmission speed can be increased by altering bandwidth or spectral efficiency (or both) in wireless data transmission systems. Systems that use Multi-Input Multi-Output (MIMO) technologies have the potential to improve spectral efficiency by employing several transmitters and receivers in tandem. To maximize spectrum efficiency and minimize inter-symbol interference, Orthogonal Frequency Division Multiplexing (OFDM) divides signals into a number of narrow band channels (ISI). In other words, combining the benefits of MIMO with OFDM will boost spectral efficiency while also increasing the link's dependability and spectral gain. MIMO and OFDM approaches are integrated in this research to increase opto-acoustic modem performance. MATLAB Simulink tool was used to design and simulate the proposed hybrid opto-acoustic modem with MIMO-OFDM for optical and acoustic (EM) signal transmission and reception. The simulation results verify the viability of the proposed method, and the measured bit-error rate (BER) for acoustic (EM) signal is 0.4958 and optical signal is 0.5101. The overall bandwidth of the system is from -150 MHz to +150 MHz.

Channel Measurement for Multiple Frequency Bands in Subway Tunnel Scenario

In next-generation radio communication systems, the use of higher frequency bands and the massive multiple-input-multiple-output (MIMO) systems has turned into hot research topics because they have the potential to increase network capacity significantly by exploiting the available narrowband and broadband spectrums. Therefore, the narrowband channel measurements are executed at the following five potential frequency bands, including 2.6 GHz, 3.5 GHz, 5.6 GHz, 10 GHz, and 28 GHz in the Shanghai subway tunnel environment in order to fulfill the latest standards of fifth generation (5G). Moreover, in the broadband channel measurements, the center frequency is 3.5 GHz and 5.6 GHz and the bandwidth is considered as 160 MHz, respectively. At the transmitter (Tx) side, a uniform rectangular antenna array composed of 32 elements is fixed on the platform near the tunnel walls. The receiver (Rx) is equipped with a uniform cylindrical antenna array consisting of 64 elements, which is set on a trolley along the track. Based on the acquired massive MIMO channel impulse responses, delay spread, angle spread, eigenvalue and channel capacity are analyzed. The results reveal that the multipath delay in the tunnel scenario is quite short, the delay spread and angle spread drop rapidly as the distance between Tx and Rx increases and the channel matrix gradually becomes serious. This research provides a reference for the deployment of future 5G systems in the subway tunnel.

Spectrum-Agile Cognitive Radios Using Multi-Task Transfer Deep Reinforcement Learning

This work proposes a cognitive engine design that enables a radio to find transmission opportunities in non-contiguous wideband spectrum to avoid interference. The radio’s objective is to apply both frequency hopping and transmit power adjustment to maintain a required level of quality-of-service (QoS). The spectrum is partitioned into sub-bands each made of a number of narrowband channels. A multi-task deep Q-network (DQN) is utilized to solve the underlying problem where communications over each sub-band represents a single task. The proposed technique exploits transfer learning between tasks to speed up learning operation for new tasks. The proposed multi-task transfer DQN is proved to be converged. It is shown through simulations that the radio is able to learn an efficient strategy to evade interference signals in a partially observable environment. The experimental results indicate that the proposed approach offers up to 24% improvement to the percentage of successful communications when compared to other RL-based approaches found in existing literature.

On the performance of OFDM and single carrier communication over wideband HF channel: theory and practice

Using wideband High Frequency (WBHF) communication has not been a luxury now due to the urgent need for the transfer of large files over long distances without the need for sophisticated infrastructure. The channel model helps in evaluating the performance of communications systems without the need for a real transmission. The existing HF ITU-R(International Telecommunication Union Radio) channel models are valid only for narrowband channels of bandwidth 3KHz and are valid up to 12KHz. Another channel model for WBHF with bandwidth up to 1MHz was given by Vogler yet, the model was mathematically intractable and allow no further analysis to take place. Noting that, WBHF demands channel bandwidth greater than 12KHz (e.g:24KHz). In this paper, we present a WBHF channel model that is both accurate and mathematically tractable laying the foundation for an informative comparison between different communication systems. The accuracy of the proposed model is first to cross verified against Vogler’s wideband model in terms of the channel impulse response, frequency response and channel scattering characteristics. This comparison leaves our model qualified to evaluate a newly developed standard of HF wideband Waveforms presented to support high data rates and link reliability requirements. Further, we establish a detailed comparison between OFDM and single carrier signaling schemes over the considered WBHF proposed ionospheric channel, under many parameters that affect the performance of both systems. We investigate the justification of using both systems under different channel conditions, we also derive the necessary rate formulas for each signaling scheme and provide simulation environment of both systems on selected samples of the ITU-R channel models for the HF ionospheric channel representing most of the channel conditions. It is coming up that in quiet channel conditions the OFDM outperforms SCCP, while in disturbed conditions SCCP shows more robustness against channel conditions than OFDM. Simulation results are shown that support our analytical results. In addition, we present a Software Defined Radio Hardware implementation of an OFDM wideband HF Transceiver. The developed implementation has been evaluated using an equivalent baseband channel model based on the ITU-R HF channel model.