Irregular repetition slotted ALOHA (IRSA) based non-orthogonal multiple access (NOMA) with priority adaptive traffic load (ATL) (PATL-IRSA-NOMA) is proposed to prioritize the performance of emergency devices in 6G enabled massive Internet of Things. In PATL-IRSA-NOMA, emergency devices (ED) and regular devices (RD) are coexisting and differentiated by access probability before sending packets, where RD performing traffic load estimation and applying ATL to access the channel. Different from conventional ATL that is performed by all users/devices, priority ATL in PATL-IRSA-NOMA is only performed by RD and reduces the contention of RD in IRSA-NOMA channel. The superior of PATL-IRSA-NOMA over conventional IRSA-NOMA is shown in terms of packet loss rate and throughput for both ED and RD groups.

This letter proposes a priority access (PA) in non-orthogonal multiple access (NOMA)-based slotted ALOHA (SA) (PA-NOMA-SA) for overload 6G massive Internet of Things (IoT). First, a NOMA-SA is considered, where devices in massive machine-type communication exploit uncoordinated uplink NOMA for packet transmissions in physical layer and SA protocol in medium access control layer. Second, PA is proposed to prioritize emergency devices over regular devices to keep stable throughput at an overload network condition. Each device estimates the traffic of the IoT network via traffic load estimation and adopts an adaptive traffic load scheme to send packets. The PA controls the load of the IoT network above critical traffic load, and it is demonstrated that by appropriately employing priority access, the normalized throughput is enhanced. Last, the lower-bound of normalized throughput is derived to demonstrate the superiority of PA-NOMA-SA over the existing random access protocol.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of $256\times 256$ pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

The rapid growth in audio processing has given much help in advancing the development of digital music. It encourages the creation of method for the genre classification which is able to optimize the learning process to be done with ease, simple and has a good quality in a song search accuracy. Hence we need a development of the learning process with a variety of methods and better algorithms. This study discusses the genre classification with good quality in the classification accuracy using a frequency content characteristics and classification using Hidden Markov Models. From the testing scenario about the parameters of type and filter order, obtained the best parameters are the Butterworth filter order 5. The best system performance has 80% accuracy from the test of 3 genre songs: pop, rock, and dance, with 80% accuracy from the amount of 40 samples data from each genre, with 10 testing data of each genre, quantization characteristic of 20, and 150 iterations for HMM.

This paper presents dual-mode index modulation for spectral efficient frequency division multiplexing (SEFDM-DM). SEFDM is a non-orthogonal multicarrier technique created by compressing the subcarrier spacing of classical orthogonal frequency-division multiplexing (OFDM). SEFDM provides high spectrum efficiency (SE) at the expense of increased inter-carrier interference (ICI). SEFDM with index modulation (SEFDM-IM) has two information-bearing units, that is, a subcarrier activation pattern and modulated symbols, which reduce ICI at the expense of an SE deficit owing to inactive subcarriers. The proposed SEFDM-DM uses all available subcarriers while retaining the diversity gain of index modulation (IM). It enhances SE by transmitting distinct constellation modes through a subcarrier index selection mechanism. This study also presents SEFDM-DM with coordinate interleaving (SEFDM-CDM), which introduces space-time block codes with coordinate interleaving to enhance transmission diversity by sending real and imaginary parts of the constellation symbols over different subcarriers. Analytical and simulation results corroborate the benefits of the suggested work in terms of SE and bit error rate.

In this article, a spatial media-based modulation-aided uplink multiple-input–multiple-output (MIMO) nonorthogonal multiple access (SMBM-NOMA) system operating under imperfect channel state information (CSI) is proposed. In addition, two different detection techniques, with successive interference cancellation (SIC) and without SIC are used to detect the superimposed SMBM-NOMA signal. Furthermore, bit error rates (BER) of SMBM-NOMA users are derived theoretically under both perfect and imperfect CSI. The SMBM-NOMA system provides lower BER than the conventional SM-NOMA, MBM-NOMA, and MIMO-aided NOMA systems. Moreover, the detection technique without SIC guarantees successful data recovery and outperforms the SIC-based detection technique in terms of BER under both perfect and imperfect CSI.