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Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

This paper investigates the maximization of data harvested by an uncrewed aerial vehicle (UAV) that supports Internet of Things (IoT) deployment scenarios. The novelty of the paper is that we study the feasibility of battery-free UAV and IoT device deployment where the UAV is powered by a ground laser source, and the IoT devices are powered by a power beacon via bistatic backscattering. We aim to optimize the UAV trajectory while minimizing the laser energy consumption throughout the entire flight by tuning the laser power and the power beacon radiated temporal power profiles. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of positive mixed convex/concave functions. Finally, the simulation results show that the proposed algorithm increases the data collected under different operation conditions by approximately 90%.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV's ({i} ) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of ({i} ) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities. Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-the-art DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including physical layer design; network optimization, organization, and management; network traffic analytics; cross-layer network security; and localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic/THz/near-field communications, ISAC, extremely large antenna arrays, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies.

A joint design of both sensing and communication can lead to substantial enhancement for both subsystems in terms of size, cost as well as spectrum and hardware efficiency. In the last decade, integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize the spectrum on a single and shared hardware platform. Recent studies focused on developing multi-function approaches to share the spectrum between radar sensing and communications. Index modulation (IM) is one particular approach to incorporate information-bearing communication symbols into the emitted radar waveforms. While IM has been well investigated in communications-only systems, the implementation adoption of IM concept in ISAC has recently attracted researchers to achieve improved energy/spectral efficiency while maintaining satisfactory radar sensing performance. This article focuses on recent studies on IM-ISAC, and presents in detail the analytical background and relevance of the major IM-ISAC applications., Comment: 11pages5figures, submitted to IEEE

Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power. At the higher end of the spectrum, ISAC systems operate at wideband using large antenna arrays to meet the stringent demands for high-resolution sensing and enhanced communications capacity. However, the wideband implementation entails beam-squint, that is, deviations in the generated beam directions because of the narrowband assumption in the analog components. This causes significant degradation in the communications capacity, target detection, and parameter estimation. This article presents the design challenges caused by beam-squint and its mitigation in ISAC systems. In this context, we also discuss several ISAC design perspectives including far-/near-field beamforming, channel/direction estimation, sparse array design, and index modulation. There are also several research opportunities in waveform design, beam training, and array processing to adequately address beam-squint in ISAC., Comment: Accepted Paper in IEEE Communications Magazine

Non-terrestrial networks (NTNs) are considered one of the key enablers in sixth-generation (6G) wireless networks; and with their rapid growth, system-level metrics analysis adds crucial understanding into NTN system performance. Applying stochastic geometry (SG) as a system-level analysis tool in the context of NTN offers novel insights into the network tradeoffs. In this paper, we study and highlight NTN common system-level metrics from three perspectives: NTN platform types, typical communication issues, and application scenarios. In addition to summarizing existing research, we study the best-suited SG models for different platforms and system-level metrics which have not been well studied in the literature. In addition, we showcase NTN-dominated prospective application scenarios. Finally, we carry out a performance analysis of system-level metrics for these applications based on SG models., Comment: 7 pages

Reconfigurable intelligent surfaces (RISs) have sparked a renewed interest in the research community envisioning future wireless communication networks. In this study, we analyzed the performance of RIS-enabled non-orthogonal multiple access (NOMA) based device-to-device (D2D) wireless communication system, where the RIS is partitioned to serve a pair of D2D users. Specifically, closed-form expressions are derived for the upper and lower limits of spectral efficiency (SE) and energy efficiency (EE). In addition, the performance of the proposed NOMA-based system is also compared with its orthogonal counterpart. Extensive simulation is done to corroborate the analytical findings. The results demonstrate that RIS highly enhances the performance of a NOMA-based D2D network., Comment: Accepted for Publication in the Proceedings of IEEE VTC-Fall 2022, 5 Pages, 4 Figures

Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has yet to be thoroughly studied. Therefore, this work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The presented quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05\%, 95.45\%, 68.71\%, and 99.43\% respectively) with small accuracy drops and up to 31$\times$ memory savings, which outperforms existing methods., Comment: 8 pages, 5 Figures, accepted at IJCNN'23

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, this paper introduces a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by first estimating both radar and communications parameters by developing beam-split-aware algorithms. Then, we propose to employ a family of hybrid beamforming techniques such as hybrid, SI, and subcarrier-dependent analog-only, and beam-split-aware beamformers. Numerical experiments demonstrate that the proposed SPIM-ISAC approach exhibits significantly improved spectral efficiency performance in the presence of beam-split than that of even fully digital non-SPIM beamformers., Comment: 30pages, Submitted to the IEEE journals

The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical, biological, and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended to different environments, such as the Internet of underWater things (IoWT), the Internet of Biomedical things (IoBT), and Internet of un- derGround things (IoGT). Even though radio frequency (RF) based wireless networks are regarded as the default means of connectivity, they are not always the best option due to the limited spectrum, interference limitations caused by the ever- increasing number of devices, and severe propagation loss in transmission mediums other than air. As a remedy, optical wireless communication (OWC) technologies can complement, replace, or co-exist with audio and radio wave-based wireless systems to improve overall network performance. To this aim, this paper reveals the full potential of OWC-based IoT networks by providing a top-down survey of four main IoT domains: IoTT, IoWT, IoBT, and IoGT. Each domain is covered by a dedicated and self-contained section that starts with a comparative analysis, explains how OWC can be hybridized with existing wireless technologies, points out potential OWC applications fitting best the related IoT domain, and discusses open communication and networking research problems. More importantly, instead of presenting a visionary OWC-IoT framework, the survey discloses that OWC-IoT has become a reality by emphasizing ongoing proof-of-concept prototyping efforts and available commercial off-the-shelf (COTS) OWC-IoT products.

Terahertz (THz) band is envisioned for the future sixth generation wireless systems thanks to its abundant bandwidth and very narrow beamwidth. These features are one of the key enabling factors for high resolution sensing with milli-degree level direction-of-arrival (DOA) estimation. Therefore, this paper investigates the DOA estimation problem in THz systems in the presence of two major error sources: 1) gain-phase mismatches, which occur due to the deviations in the radio-frequency circuitry; 2) beam-squint, which is caused because of the deviations in the generated beams at different subcarriers due to ultra-wide bandwidth. An auto-calibration approach, namely NoisE subspAce correcTion technique for MUltiple SIgnal Classification (NEAT-MUSIC), is proposed based on the correction of the noise subspace for accurate DOA estimation in the presence of gain-phase mismatches and beam-squint. To gauge the performance of the proposed approach, the Cramer-Rao bounds are also derived. Numerical results show the effectiveness of the proposed approach., Comment: Accepted paper in IEEE Wireless Communications Letters. arXiv admin note: text overlap with arXiv:2310.16724

Terahertz (THz) band communications and integrated sensing and communications (ISAC) are two main facets of the sixth generation wireless networks. In order to compensate the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-squint severely degrades the beamforming accuracy. Contrary to prior works that examine only either narrowband ISAC beamforming or far-field models, we introduce an alternating optimization technique for hybrid beamforming design in near-field THz-ISAC scenario. We also propose an efficient approach to compensate near-field beam-squint via baseband beamformers. Via numerical simulations, we show that the proposed approach achieves satisfactory spectral efficiency performance while accurately estimating the near-field beamformers and mitigating the beam-squint without additional hardware components., Comment: Accepted Paper in 2023 IEEE Global Communications Conference (GLOBECOM), Kuala Lumpur, Malaysia, 2023

Next-generation wireless networks strive for higher communication rates, ultra-low latency, seamless connectivity, and high-resolution sensing capabilities. To meet these demands, terahertz (THz)-band signal processing is envisioned as a key technology offering wide bandwidth and sub-millimeter wavelength. Furthermore, THz integrated sensing and communications (ISAC) paradigm has emerged jointly access spectrum and reduced hardware costs through a unified platform. To address the challenges in THz propagation, THz-ISAC systems employ extremely large antenna arrays to improve the beamforming gain for communications with high data rates and sensing with high resolution. However, the cost and power consumption of implementing fully digital beamformers are prohibitive. While hybrid analog/digital beamforming can be a potential solution, the use of subcarrier-independent analog beamformers leads to the beam-squint phenomenon where different subcarriers observe distinct directions because of adopting the same analog beamformer across all subcarriers. In this paper, we develop a sparse array architecture for THz-ISAC with hybrid beamforming to provide a cost-effective solution. We analyze the antenna selection problem under beam-squint influence and introduce a manifold optimization approach for hybrid beamforming design. To reduce computational and memory costs, we propose novel algorithms leveraging grouped subarrays, quantized performance metrics, and sequential optimization. These approaches yield a significant reduction in the number of possible subarray configurations, which enables us to devise a neural network with classification model to accurately perform antenna selection., Comment: 14pages10figures, submitted to IEEE

The higher speed, scalability and parallelism offered by ReRAM crossbar arrays foster development of ReRAM-based next generation AI accelerators. At the same time, sensitivity of ReRAM to temperature variations decreases R_on/Roff ratio and negatively affects the achieved accuracy and reliability of the hardware. Various works on temperature-aware optimization and remapping in ReRAM crossbar arrays reported up to 58\% improvement in accuracy and 2.39$\times$ ReRAM lifetime enhancement. This paper classifies the challenges caused by thermal heat, starting from constraints in ReRAM cells' dimensions and characteristics to their placement in the architecture. In addition, it reviews available solutions designed to mitigate the impact of these challenges, including emerging temperature-resilient DNN training methods. Our work also provides a summary of the techniques and their advantages and limitations., Comment: 18 pages

To efficiently utilize the wireless spectrum and save hardware costs, the fifth generation and beyond (B5G) wireless networks envisage integrated sensing and communications (ISAC) paradigms to jointly access the spectrum. In B5G systems, the expensive hardware is usually avoided by employing hybrid beamformers that employ fewer radio-frequency chains but at the cost of the multiplexing gain. Recently, it has been proposed to overcome this shortcoming of millimeter wave (mmWave) hybrid beamformers through spatial path index modulation (SPIM), which modulates the spatial paths between the base station and users and improves spectral efficiency. In this paper, we propose an SPIM-ISAC approach for hybrid beamforming to simultaneously generate beams toward both radar targets and communications users. We introduce a low complexity approach for the design of hybrid beamformers, which include radar-only and communications-only beamformers. Numerical experiments demonstrate that our SPIM-ISAC approach exhibits a significant performance improvement over the conventional mmWave-ISAC design in terms of spectral efficiency and the generated beampattern., Comment: Accepted paper in 2023 IEEE Radar Conference

Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacle by completely cutting off the backward path between modules and isolating their gradients to reduce memory cost and accelerate the training process. These methods prevent errors from flowing between modules and hence information exchange, resulting in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces inter-module backward dependency and allows errors to flow between modules. The algorithm facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, in ResNet32 with 16 local modules, our method surpasses the conventional greedy local training method by 4.00\% and a recent work by 1.83\% in accuracy on CIFAR10, respectively. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79\% reduction in memory cost and 52\% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms towards better efficiency and biological plausibility.

Content Addressable Memories (CAMs) are considered a key-enabler for in-memory computing (IMC). IMC shows order of magnitude improvement in energy efficiency and throughput compared to traditional computing techniques. Recently, analog CAMs (aCAMs) were proposed as a means to improve storage density and energy efficiency. In this work, we propose two new aCAM cells to improve data encoding and robustness as compared to existing aCAM cells. We propose a methodology to choose the margin and interval width for data encoding. In addition, we perform a comprehensive comparison against prior work in terms of the number of intervals, noise sensitivity, dynamic range, energy, latency, area, and probability of failure., Comment: This is a revised manuscript that is under consideration for publication at IEEE TCAS-I

In-memory computing is an emerging computing paradigm that overcomes the limitations of exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such paradigm, the computations are performed directly on the data stored in the memory, which highly reduces the memory-processor communications during computation. Hence, significant speedup and energy savings could be achieved especially with data-intensive applications. Associative processors (APs) were proposed in the seventies and recently were revived thanks to the high-density memories. In this tutorial brief, we overview the functionalities and recent trends of APs in addition to the implementation of each content-addressable memory with different technologies. The AP operations and runtime complexity are also summarized. We also explain and explore the possible applications that can benefit from APs. Finally, the AP limitations, challenges, and future directions are discussed., Comment: 7 pages

DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications, since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient hardware and software codesign that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and significantly accelerates the DNA pattern matching task compared to previous approaches described in the literature.

Satellite-terrestrial networks (STNs) have received significant attention from research and industry due to their capability of providing a stable connection to rural and distant areas, where the allocation of terrestrial infrastructures is uneconomical or difficult. Moreover, the STNs are considered as a promising enabler of fifth-generation communication networks. However, expected massive connectivity in future communication networks will face issues associated with spectrum scarcity. In this regard, the integration of cognitive radio and non-orthogonal multiple access (NOMA) techniques into STNs is considered as a promising remedy. Thereafter, in this article, we investigate NOMA-assisted cognitive STN under practical system conditions, such as transceiver hardware impairments, channel state information mismatch, imperfect successive interference cancellation, and interference noises. Generalized coverage probability formulas for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint and its correctness is verified through Monte Carlo simulation. Furthermore, to achieve performance fairness among the users, power allocation factors based on coverage fairness for primary and secondary NOMA users are provided. Moreover, the numerical results demonstrate superior performance compared to the ones obtained from an orthogonal multiple access scheme and examine the imperfection's impact on the system performance in terms of coverage and throughput.

This paper investigates the impact of practical limitations, such as transceiver hardware impairments and channel state information mismatch, on a cognitive hybrid satellite-terrestrial relay network. Moreover, it is assumed that the network is under the effect of independent and non-identically distributed interference noises emerging from neighboring device nodes. Generalized closed-form expressions of ergodic capacity for a terrestrial user are obtained considering the interference temperature constraint's influence. Finally, derived findings are validated through Monte Carlo simulation and the impact of impairments is examined.

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT., Comment: 16

In-memory associative processor architectures are offered as a great candidate to overcome memory-wall bottleneck and to enable vector/parallel arithmetic operations. In this paper, we extend the functionality of the associative processor to multi-valued arithmetic. To allow for in-memory compute implementation of arithmetic or logic functions, we propose a structured methodology enabling the automatic generation of the corresponding look-up tables (LUTs). We propose two approaches to build the LUTs: a first approach that formalizes the intuition behind LUT pass ordering and a more optimized approach that reduces the number of required write cycles. To demonstrate these methodologies, we present a novel ternary associative processor (TAP) architecture that is employed to implement efficient ternary vector in-place addition. A SPICE-MATLAB co-simulator is implemented to test the functionality of the TAP and to evaluate the performance of the proposed AP ternary in-place adder implementations in terms of energy, delay, and area. Results show that compared to the binary AP adder, the ternary AP adder results in a 12.25\% and 6.2\% reduction in energy and area, respectively. The ternary AP also demonstrates a 52.64\% reduction in energy and a delay that is up to 9.5x smaller when compared to a state-of-art ternary carry-lookahead adder.

Deep Neural Networks (DNNs), as a subset of Machine Learning (ML) techniques, entail that real-world data can be learned and that decisions can be made in real-time. However, their wide adoption is hindered by a number of software and hardware limitations. The existing general-purpose hardware platforms used to accelerate DNNs are facing new challenges associated with the growing amount of data and are exponentially increasing the complexity of computations. An emerging non-volatile memory (NVM) devices and processing-in-memory (PIM) paradigm is creating a new hardware architecture generation with increased computing and storage capabilities. In particular, the shift towards ReRAM-based in-memory computing has great potential in the implementation of area and power efficient inference and in training large-scale neural network architectures. These can accelerate the process of the IoT-enabled AI technologies entering our daily life. In this survey, we review the state-of-the-art ReRAM-based DNN many-core accelerators, and their superiority compared to CMOS counterparts was shown. The review covers different aspects of hardware and software realization of DNN accelerators, their present limitations, and future prospectives. In particular, comparison of the accelerators shows the need for the introduction of new performance metrics and benchmarking standards. In addition, the major concerns regarding the efficient design of accelerators include a lack of accuracy in simulation tools for software and hardware co-design.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. {However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation.} The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of \$\unit[\{4-10\}]{dB}\$ with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only \$\unit[\{1-1.5\}]{dB}\$, while significantly reducing complexity by two orders of magnitude., Comment: 16 pages, 8 figures, submitted to IEEE transactions on wireless communications. arXiv admin note: text overlap with arXiv:1704.08572 by other authors

Recently Full-Duplex (FD) communication has gained significant interest due to demonstrable increase in throughput and spectral efficiency. Conventional Half-duplex (HD) communication systems use either time-duplexing or frequency-duplexing to avoid self-interference. In contrast, full-duplex systems transmit and receive simultaneously on the same frequency band, thus optimally utilizing available resources. The main challenge in FD systems is managing the self-interference (SI) signal at each node, which is typically orders of magnitude larger than the intended signal of interest (SOI). To achieve sufficient SI suppression, FD systems rely on cancellation across multiple domains such as spatial, analog, and digital. However, number of practical, FD specific challenges arise impacting quality of service, when at least one node in a network operates in full-duplex mode.\par In this thesis, we consider practical issues of wireless networks containing a full-duplex node. The ultimate goal of this work is to design and implement real-time, end-to-end networks consisting of at least one FD node that is capable of improving network performance under limited available bandwidth constraint. First, we identify synchronization issues in a network consisting of a full-duplex base station communicating with half-duplex nodes. Novel synchronization techniques specific for full-duplex networks are proposed that allow compensation of synchronization errors in time and frequency. The proposed techniques are implemented and tested experimentally on a real-time full-duplex wireless network. Second, we characterize the dynamic environment impact on the received self-interference in the FD system, which is equipped with a reconfigurable antenna as a passive SI suppression mechanism. The self-interference channel delay profile is measured using the FD system operating on 5MHz, 10MHz, and 20MHz bandwidths. The measured channel profiles collected under suppressing and non-suppressing ante

Brain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices, different nanoscale-nonvolatile memory devices, such as RRAMs (memristors), STT-RAM and PCM, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs, they suffer from numerous nonidealities limiting the performance, including, high variability, asymmetric and nonlinear weight update, endurance, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. In this thesis, we first develop a compact SPICE-like framework for the resistive crossbar array that incorporates the RRAM device model and interconnect parasitics such as wire resistance, inductance, capacitance, and conductance. This framework is the corner-stone of the simulation infrastructure developed in this work, allowing for >1000\times faster simulation results as compared to SPICE. Second, we propose novel reading and writing techniques to read and write the entire word in one clock cycle with an optimized bias scheme to minimize the write errors. To complete the memory design, the required reading and writing CMOS peripheral circuits are designed as well. Due to the inevitable existence of the sneak path problem in crossbar arrays, nonstationary polar codes are designed to mitigate the effect of this pr

Brain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices, different nanoscale-nonvolatile memory devices, such as RRAMs (memristors), STT-RAM and PCM, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs, they suffer from numerous nonidealities limiting the performance, including, high variability, asymmetric and nonlinear weight update, endurance, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. In this thesis, we first develop a compact SPICE-like framework for the resistive crossbar array that incorporates the RRAM device model and interconnect parasitics such as wire resistance, inductance, capacitance, and conductance. This framework is the corner-stone of the simulation infrastructure developed in this work, allowing for >1000\times faster simulation results as compared to SPICE. Second, we propose novel reading and writing techniques to read and write the entire word in one clock cycle with an optimized bias scheme to minimize the write errors. To complete the memory design, the required reading and writing CMOS peripheral circuits are designed as well. Due to the inevitable existence of the sneak path problem in crossbar arrays, nonstationary polar codes are designed to mitigate the effect of this pr

Brain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices, different nanoscale-nonvolatile memory devices, such as RRAMs (memristors), STT-RAM and PCM, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs, they suffer from numerous nonidealities limiting the performance, including, high variability, asymmetric and nonlinear weight update, endurance, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. In this thesis, we first develop a compact SPICE-like framework for the resistive crossbar array that incorporates the RRAM device model and interconnect parasitics such as wire resistance, inductance, capacitance, and conductance. This framework is the corner-stone of the simulation infrastructure developed in this work, allowing for >1000\times faster simulation results as compared to SPICE. Second, we propose novel reading and writing techniques to read and write the entire word in one clock cycle with an optimized bias scheme to minimize the write errors. To complete the memory design, the required reading and writing CMOS peripheral circuits are designed as well. Due to the inevitable existence of the sneak path problem in crossbar arrays, nonstationary polar codes are designed to mitigate the effect of this pr

Recently Full-Duplex (FD) communication has gained significant interest due to demonstrable increase in throughput and spectral efficiency. Conventional Half-duplex (HD) communication systems use either time-duplexing or frequency-duplexing to avoid self-interference. In contrast, full-duplex systems transmit and receive simultaneously on the same frequency band, thus optimally utilizing available resources. The main challenge in FD systems is managing the self-interference (SI) signal at each node, which is typically orders of magnitude larger than the intended signal of interest (SOI). To achieve sufficient SI suppression, FD systems rely on cancellation across multiple domains such as spatial, analog, and digital. However, number of practical, FD specific challenges arise impacting quality of service, when at least one node in a network operates in full-duplex mode.\par In this thesis, we consider practical issues of wireless networks containing a full-duplex node. The ultimate goal of this work is to design and implement real-time, end-to-end networks consisting of at least one FD node that is capable of improving network performance under limited available bandwidth constraint. First, we identify synchronization issues in a network consisting of a full-duplex base station communicating with half-duplex nodes. Novel synchronization techniques specific for full-duplex networks are proposed that allow compensation of synchronization errors in time and frequency. The proposed techniques are implemented and tested experimentally on a real-time full-duplex wireless network. Second, we characterize the dynamic environment impact on the received self-interference in the FD system, which is equipped with a reconfigurable antenna as a passive SI suppression mechanism. The self-interference channel delay profile is measured using the FD system operating on 5MHz, 10MHz, and 20MHz bandwidths. The measured channel profiles collected under suppressing and non-suppressing ante

This letter investigates a non-orthogonal multiple access (NOMA) assisted millimeter-wave device-to-device (D2D) network practically limited by multiple interference noises, transceiver hardware impairments, imperfect successive interference cancellation, and channel state information mismatch. Generalized outage probability expressions for NOMA-D2D users are deduced and achieved results, validated by Monte Carlo simulations, are compared with the orthogonal multiple access to show the superior performance of the proposed network model, Comment: 4 pages, 3 figures

The conflation of cognitive radio (CR) and nonorthogonal multiple access (NOMA) concepts is a promising approach to fulfil the massive connectivity goals of future networks given the spectrum scarcity. Accordingly, this letter investigates the outage performance of imperfect cooperative CR-NOMA networks under hardware impairments and interference. Our analysis is involved with the derivation of the end-to-end outage probability (OP) for secondary NOMA users by accounting for imperfect channel state information (CSI), as well as the residual interference caused by successive interference cancellation (SIC) errors and coexisting primary/secondary users. The numerical results validated by Monte Carlo simulations show that CR-NOMA network provides a superior outage performance over orthogonal multiple access. As imperfections become more significant, CR-NOMA is observed to deliver relatively poor outage performance., Comment: 5 pages, 4 figures

In this letter, we propose a modified version of Fast Independent Component Analysis (FICA) algorithm to solve the self-interference cancellation (SIC) problem in In-band Full Duplex (IBFD) communication systems. The complex mixing problem is mathematically formulated to suit the real-valued blind source separation (BSS) algorithms. In addition, we propose a method to estimate the ambiguity factors associated with ICA lumped together with the channels and residual separation error. Experiments were performed on an FD platform where FICA-based BSS was applied for SIC in the frequency domain. Experimental results show superior performance compared to least squares SIC by up to 6 dB gain in the SNR.

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state of the art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including pre-synaptic, post-synaptic and write circuits required for online training, have been designed in the sub-threshold regime for power saving with a standard 180 nm CMOS process., Comment: 15 pages, 11 figures, Journal of Emerging Technology in Circuits and Systems (JETCAS)

Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been recognized as a promising technique to overcome issues of spectrum scarcity and support massive connectivity envisioned in next-generation wireless networks. In this paper, we investigate the deployment of an unmanned aerial vehicle (UAV) as a relay that fairly serves a large number of secondary users in a hot-spot region. The UAV deployment algorithm must jointly account for user clustering, channel assignment, and resource allocation sub-problems. We propose a solution methodology that obtains user clustering and channel assignment based on the optimal resource allocations for a given UAV location. To this end, we derive closed-form optimal power and time allocations and show it delivers optimal max-min fair throughput by consuming less energy and time than geometric programming. Based on optimal resource allocation, the optimal coverage probability is also provided in closed-form, which takes channel estimation errors, hardware impairments, and primary network interference into account. The optimal coverage probabilities are used by the proposed max-min fair user clustering and channel assignment approaches. The results show that the proposed method achieves 100% accuracy in more than five orders of magnitude less time than the optimal benchmark.

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture, that can learn in situ as accurately as conventional processors, is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art.

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state-of-the-art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including presynaptic, post-synaptic and write circuits required for online training, have been designed in the subthreshold regime for power saving with a standard 180 nm CMOS process.

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn in-situ as accurately as conventional processors is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art.

Traditional solutions based on Modulation and Coding Schemes (MCSs) have been exhaustively used to improve the performance of wireless systems, striving to reach close to the maximum theoretical limits for channel capacity. In-Band Full-Duplex (IBFD) is an emerging technique that enables a wireless node to transmit and receive at the same time and on the same assigned frequency. Consequently, IBFD wireless communications can potentially double the channel capacity compared to contemporary Half-Duplex (HD) wireless systems. Therefore, IBFD techniques provide new insights into how available resources can be exploited.In this dissertation, a novel IBFD Medium Access Control (MAC) protocol is presented for Wireless Local Area Networks (WLANs) using IEEE 802.11 Distributed Coordination Function (DCF). The concept of IBFD communications is examined from an unconventional perspective to propose a mechanism that increases the symmetry between uplink (UL) and downlink (DL) traffic loads in order to maximize the utilization of the channel. This dissertation also provides matching analytical and simulation results to show how frame collisions are reduced and how throughput increases when IBFD schemes are implemented for WLANs. Additionally, a collision-free mode enabled by IBFD communications is presented.In order to study the feasibility and benefits of IBFD networks, this dissertation presents an accurate analytical model based on Discrete-Time Markov Chain (DTMC) analysis for IEEE 802.11 DCF with IBFD capabilities. The model captures all parameters necessary to calculate important performance metrics including latency and link utilization, which quantify enhancements introduced as a result of IBFD solutions. Moreover, two frame aggregation schemes for WLANs with IBFD features are proposed to increase the efficiency of data transmission. The dissertation also presents an analytical model for power consumption in IBFD-WLANs. Energy-efficiency is compared for both HD and IBFD

Traditional solutions based on Modulation and Coding Schemes (MCSs) have been exhaustively used to improve the performance of wireless systems, striving to reach close to the maximum theoretical limits for channel capacity. In-Band Full-Duplex (IBFD) is an emerging technique that enables a wireless node to transmit and receive at the same time and on the same assigned frequency. Consequently, IBFD wireless communications can potentially double the channel capacity compared to contemporary Half-Duplex (HD) wireless systems. Therefore, IBFD techniques provide new insights into how available resources can be exploited.In this dissertation, a novel IBFD Medium Access Control (MAC) protocol is presented for Wireless Local Area Networks (WLANs) using IEEE 802.11 Distributed Coordination Function (DCF). The concept of IBFD communications is examined from an unconventional perspective to propose a mechanism that increases the symmetry between uplink (UL) and downlink (DL) traffic loads in order to maximize the utilization of the channel. This dissertation also provides matching analytical and simulation results to show how frame collisions are reduced and how throughput increases when IBFD schemes are implemented for WLANs. Additionally, a collision-free mode enabled by IBFD communications is presented.In order to study the feasibility and benefits of IBFD networks, this dissertation presents an accurate analytical model based on Discrete-Time Markov Chain (DTMC) analysis for IEEE 802.11 DCF with IBFD capabilities. The model captures all parameters necessary to calculate important performance metrics including latency and link utilization, which quantify enhancements introduced as a result of IBFD solutions. Moreover, two frame aggregation schemes for WLANs with IBFD features are proposed to increase the efficiency of data transmission. The dissertation also presents an analytical model for power consumption in IBFD-WLANs. Energy-efficiency is compared for both HD and IBFD

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn in-situ as accurately as conventional processors is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art.

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn in-situ as accurately as conventional processors is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art.

Resistive memories are considered a promising memory technology enabling high storage densities with in-memory computing capabilities. However, the readout reliability of resistive memories is impaired due to the inevitable existence of wire resistance, resulting in the sneak path problem. Motivated by this problem, we study polar coding over channels with different reliability levels, termed non-stationary polar codes, and we propose a technique improving its bit error rate (BER) performance. We then apply the framework of non-stationary polar codes to the crossbar array and evaluate its BER performance under two modeling approaches, namely binary symmetric channels (BSCs) and binary asymmetric channels (BSCs). Finally, we propose a technique for biasing the proportion of high-resistance states in the crossbar array and show its advantage in reducing further the BER. Several simulations are carried out using a SPICE-like simulator, exhibiting significant reduction in BER.

This paper presents an analytical model of power consumption for In-Band Full-Duplex (IBFD) Wireless Local-Area Networks (WLANs). Energy-efficiency is compared for both Half-Duplex (HD) and IBFD networks. The presented analytical model closely matches the results generated by simulation. For a given traffic scenario, IBFD systems exhibit higher power consumption, however at improved energy efficiency when compared to equivalent HD WLANs.

In-Band Full-Duplex (IBFD) is a technique that enables a wireless node to simultaneously transmit a signal and receive another on the same assigned frequency. Thus, IBFD wireless systems can provide up to twice the channel capacity compared to conventional Half-Duplex (HD) systems. In order to study the feasibility of IBFD networks, reliable models are needed to capture anticipated benefits of IBFD above the physical layer (PHY). In this paper, an accurate analytical model based on Discrete-Time Markov Chain (DTMC) analysis for IEEE 802.11 Distributed Coordination Function (DCF) with IBFD capabilities is proposed. The model captures all parameters necessary to calculate important performance metrics which quantify enhancements introduced as a result of IBFD solutions. Additionally, two frame aggregation schemes for Wireless Local Area Networks (WLANs) with IBFD features are proposed to increase the efficiency of data transmission. Matching analytical and simulation results with less than 1% average errors confirm that the proposed frame aggregation schemes further improve the overall throughput by up to 24% and reduce latency by up to 47% in practical IBFD-WLANs. More importantly, the results assert that IBFD transmission can only reduce latency to a suboptimal point in WLANs, but frame aggregation is necessary to minimize it.

Transistor-based memories are rapidly approaching their maximum density per unit area. Resistive crossbar arrays enable denser memory due to the small size of switching devices. However, due to the resistive nature of these memories, they suffer from current sneak paths complicating the readout procedure. In this paper, we propose a row readout technique with circuitry that can be used to read {selector-less} resistive crossbar based memories. High throughput reading and writing techniques are needed to overcome the memory-wall bottleneck problem and to enable near memory computing paradigm. The proposed technique can read the entire row of dense crossbar arrays in one cycle, unlike previously published techniques. The requirements for the readout circuitry are discussed and satisfied in the proposed circuit. Additionally, an approximated expression for the power consumed while reading the array is derived. A figure of merit is defined and used to compare the proposed approach with existing reading techniques. Finally, a quantitative analysis of the effect of biasing mismatch on the array size is discussed.

This paper presents approaches to creating tissue mimicking materials that can be used as phantoms for evaluating the performance of Body Area Networks (BAN). The main goal of the paper is to describe a methodology to create a repeatable experimental BAN platform that can be customized depending on the BAN scenario under test. Comparisons between different material compositions and percentages are shown, along with the resulting electrical properties of each mixture over the frequency range of interest for intra-body communications; 100 KHz to 100 MHz. Test results on a composite multi-layer sample are presented confirming the efficacy of the proposed methodology. To date, this is the first paper that provides guidance on how to decide on concentration levels of ingredients, depending on the exact frequency range of operation, and the desired matched electrical characteristics (conductivity vs. permittivity), to create multi-layer phantoms for intra-body communication applications.

The desire to have ultra compact, low power, wearable and implanted biosensors/actuators encourages researchers to develop new communication methods that can replace current Radio Frequency (RF) wireless communication links. RF links require power and area hungry analog circuitry that limits the usability of such systems. This thesis conducts a thorough study on a promising technique called intra-body communication which utilizes the human body as a transmission medium, where galvanic coupling and capacitive coupling represent the main approaches to implement the intra-body communication (IBC) system.IBCs literature review is presented showing the main motivation behind this research and the prior trials in the field, as well as highlighting the obstacles facing this technique. A circuit model is adopted to derive a transfer function that can capture the human channel properties to determine the transmission gain. Moreover, finite element method (FEM) technique is utilized to determine the path loss of the human channel, typically the human arm model, and to examine the current density distribution in human tissues using both a full and a reduced order model. In addition, the effect of bone fracture internal fixation implant effect on the channel parameters is investigated. Finally, to validate the FEM and circuit model, a fabricated human arm phantom is introduced in detail by covering the fabrication process, dielectric properties and transmission results.

The desire to have ultra compact, low power, wearable and implanted biosensors/actuators encourages researchers to develop new communication methods that can replace current Radio Frequency (RF) wireless communication links. RF links require power and area hungry analog circuitry that limits the usability of such systems. This thesis conducts a thorough study on a promising technique called intra-body communication which utilizes the human body as a transmission medium, where galvanic coupling and capacitive coupling represent the main approaches to implement the intra-body communication (IBC) system.IBCs literature review is presented showing the main motivation behind this research and the prior trials in the field, as well as highlighting the obstacles facing this technique. A circuit model is adopted to derive a transfer function that can capture the human channel properties to determine the transmission gain. Moreover, finite element method (FEM) technique is utilized to determine the path loss of the human channel, typically the human arm model, and to examine the current density distribution in human tissues using both a full and a reduced order model. In addition, the effect of bone fracture internal fixation implant effect on the channel parameters is investigated. Finally, to validate the FEM and circuit model, a fabricated human arm phantom is introduced in detail by covering the fabrication process, dielectric properties and transmission results.