Sum-Rate Maximization of IRS-Aided SCMA System

We study an intelligent reflecting surface (IRS)-aided downlink sparse code multiple access (SCMA) system for massive connectivity in future machine -type communication networks. Our objective is to maximize the system sum-rate subject to the constraint of minimum user data rate, the total power of base station, SCMA codebook structure, and IRS channel coefficients. To this end, a joint optimization problem involving IRS phase vector, factor graph matrix assignment, and power allocation problem is formulated, which is non-convex in nature. This problem is solved by developing an alternating optimization (AO) algorithm. A key idea is to first divide the formulated non-convex problem into three subproblems (i.e., factor graph matrix assignment, power allocation, and phase vector of IRS) and then tackle them iteratively. The validity of the proposed schemes is shown using the simulation results. Moreover, compared to the SCMA system without IRS, a significant performance improvement in the IRS-aided SCMA system is shown in terms of achievable sum-rate

Resource Allocation for Sum-Rate Maximization in Multi-UAV SCMA Networks

This work investigates a sparse code multiple access (SCMA) assisted multiple unmanned aerial vehicles (UAVs) downlink communication network for improved data services to multiple ground users. Our objective is to maximize the sum-rate of the multi-UAV SCMA network by optimizing the SCMA factor graph matrix used for resource allocation, considering the inter-UAV and intra-UAV interference components. The formulated problem is non-convex in nature and is subject to the SCMA codebook constraints. We propose a factor graph matrix assignment algorithm to solve this optimization problem. Our simulation results demonstrate the superiority of the proposed scheme in terms of rate performance over the benchmark schemes. Thus, compared with orthogonal multiple access strategies, SCMA emerge as a promising candidate for next generation multiple access (NGMA) techniques

A Tutorial on Decoding Techniques of Sparse Code Multiple Access

Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonal multiple access (NOMA) scheme to enable future massive machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive sparse codebooks (CBs). Efficient multiuser detection is carried out at the receiver by employing the message passing algorithm (MPA) that exploits the sparsity of CBs to achieve error performance approaching to that of the maximum likelihood receiver. In spite of numerous research efforts in recent years, a comprehensive one-stop tutorial of SCMA covering the background, the basic principles, and new advances, is still missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we provide a holistic introduction to the principles of SCMA encoding, CB design, and MPA based decoding in a self-contained manner. As an ambitious paper aiming to push the limits of SCMA, we present a survey of advanced decoding techniques with brief algorithmic descriptions as well as several promising directions

Low-Complexity Codebook Design for SCMA-Based Visible Light Communication

Sparse code multiple access (SCMA), as a code-domain non-orthogonal multiple access (NOMA) scheme, has received considerable research attention for enabling massive connectivity in future wireless communication systems. In this paper, we present a novel codebook (CB) design for SCMA based visible light communication (VLC) system, which suffers from shot noise. In particular, we introduce an iterative algorithm for designing and optimizing CB by considering the impact of shot noise at the VLC receiver. Based on the proposed CB, we derive and analyze the theoretical bit error rate (BER) expression for the resultant SCMA-VLC system. The simulation results show that our proposed CBs outperform CBs in the existing literature for different loading factors with much less complexity. Further, the derived analytical BER expression well aligns with simulated results, especially in high signal power regions

Diagnosis of skin cancer using deep learning

Skin diseases consists a wide range of ailments that affect the skin, including microbial infections, viral, fungal, allergies, epidermis malignancies, and parasitic diseases. In South-Asian countries like India people don’t care much about the skin conditions. In our country, people prefer home remedies to cure skin conditions instead of visiting a dermatologist which can lead to serious skin conditions. Early diagnosis of skin disease is very important as it can reduce the severity of the condition. Melanoma is the deadliest type of skin cancer, and it is the most prominent form of cancer. Melanoma could be diagnosed early, which would reduce overall illness and death. The odds of dying from the ailment is proportional to the extent of the malignancy, which is proportional to the length of time it has been growing. The keys to early detection are patient self-examination of the skin, full-body skin screenings by a dermatologist, and patient engagement. This work aims to categorize skin cancer into two types: malignant and benign. Two different approaches were used. Starting with a simple Convolutional Neural Network and then moving on to transfer learning. In our experiment, we were able to attain a classification accuracy of 82 percent

Resource Management for Sum-rate Maximization in SCMA-Assisted UAV System

This work presents a resource management framework for optimizing the sum-rate in a sparse code multiple access (SCMA)-assisted UAV downlink system. We formulate two optimization problems for maximizing the overall sum-rate: the first problem addresses UAV 3D deployment and trajectory optimization with energy constraints, while the second focuses on optimizing SCMA subcarrier and power allocation optimization, subject to factor graph matrix (FGM) constraints and a minimum user data rate. Since the optimization problems are non-convex, the complexity of finding the global optimal solutions is prohibitive. We propose a gradient ascent-based iterative algorithm to compute the optimal UAV 3D deployment and trajectory. Further, an effective channel state informationbased algorithm is proposed for FGM assignment, followed by a Lagrange dual decomposition method to solve the power allocation problem efficiently. Our research findings demonstrate that the optimization of the UAV trajectory gives improved sum-rate within the specified energy budget. Further, employing CSIbased multiple subcarrier allocation and strategic power allocation can significantly improve system performance compared to the benchmark schemes

A Tutorial on Decoding Techniques of Sparse Code Multiple Access

Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonalmultiple access (NOMA) scheme to enable \color{black}future massivemachine-type communication networks. As an evolved variant of code divisionmultiple access (CDMA), multiple users in SCMA are separated by assigningdistinctive sparse codebooks (CBs). Efficient multiuser detection is carriedout at the receiver by employing the message passing algorithm (MPA) thatexploits the sparsity of CBs to achieve error performance approaching to thatof the maximum likelihood receiver. In spite of numerous research efforts inrecent years, a comprehensive one-stop tutorial of SCMA covering thebackground, the basic principles, and new advances, is still missing, to thebest of our knowledge. To fill this gap and to stimulate more forthcomingresearch, we provide a holistic introduction to the principles of SCMAencoding, CB design, and MPA based decoding in a self-contained manner. As anambitious paper aiming to push the limits of SCMA, we present a survey ofadvanced decoding techniques with brief algorithmic descriptions as well asseveral promising directions

Characterization of Aggregating Agents towards Sensitive Optical Detection of Tryptophan Using Lab-on-a-Chip

The analysis of body fluids is desirable to minimize the invasiveness of diagnostic tests and non-destructive forensic investigations. In this study, surface-enhanced Raman spectroscopy (SERS) is employed for sensitive and reproducible detection of biomolecule focusing on ‘hot spots’ generation and automated flow system. Here, we have demonstrated how the plasmon frequency of nanoparticles can be tuned using different aggregating agents for optimal SERS signals. We have compared the effect of different aggregating agents on silver colloids and the resulting enhancement in Raman signals for Tryptophan which is an important amino acid present as an integral component of various body fluids including blood, saliva, tears, and cerebrospinal fluid. The automated segmented flow system, Lab-on-a-chip (LOC), is employed to trap the analyte in droplets while obtaining reproducible SERS spectra of Tryptophan at μM concentration. Further for a thorough interpretation of enhanced vibrational modes of Tryptophan, a theoretical approach has been applied. By combining both experimental and computational approaches we have identified the most preferable site of Tryptophan for interaction with metal nanoparticles and accurately assigned the enhanced Raman bands. The present study demonstrates that the union of SERS and microfluidics has the potential for spectral fingerprinting of biomolecules present in body fluids with high sensitivity