We propose an improvement of the random spreading approach with polar codes for unsourced multiple access, for which each user first encodes its message by a polar code, and then the coded bits are spread using a random spreading sequence. The proposed approach divides the active users into different groups, and employs different power levels for each group in such a way that the average power constraint is satisfied. We formulate and solve an optimization problem to determine the number of groups, and the corresponding user numbers and power levels. Extensive simulations show that the proposed approach outperforms the existing methods, especially when the number of active users is large.

We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current iteration. Utilizing the previous signal vectors accelerates the convergence of the ELMS algorithm at the expense of higher steady-state error compared to the LMS algorithm. To further improve the performance, we propose the fast ELMS (FELMS) algorithm in which the influence of the signal vectors of previous iterations is controlled by optimizing the gradient of the mean-square deviation (GMSD). The FELMS algorithm converges faster than the ELMS algorithm and has steady-state errors comparable to that of the LMS algorithm. We analyze the mean-square performance of ELMS and FELMS algorithms theoretically and derive the respective convergence conditions as well as the predicted MSD values. In addition, we present an adaptive sampling strategy in which the sampling probability of each node is changed according to the GMSD of the node. Computer simulations using both synthetic and real data validate the theoretical results and demonstrate the merits of the proposed algorithms.

According to the interaction of nanoparticles with biological systems, enthusiasm for nanotechnology in biomedical applications has been developed in the past decades. Fe2O3 nanoparticles, as the most stable iron oxide, have special merits that make them useful widely for detecting diseases, therapy, drug delivery, and monitoring the therapeutic process. This review presents the fabrication methods of Fe2O3-based materials and their photocatalytic and magnetic properties. Then, we highlight the application of Fe2O3-based nanoparticles in diagnosis and imaging, different therapy methods, and finally, stimulus-responsive systems, such as pH-responsive, magnetic-responsive, redox-responsive, and enzyme-responsive, with an emphasis on cancer treatment. In addition, the potential of Fe2O3 to combine diagnosis and therapy within a single particle called theranostic agent will be discussed.

We study the problem of unsourced random access (URA) over Rayleigh block-fading channels with a receiver equipped with multiple antennas. We employ multiple stages of orthogonal pilots, each of which is randomly picked from a codebook. In the proposed scheme, each user encodes its message using a polar code and appends it to the selected pilot sequences to construct its transmitted signal. Accordingly, the received signal consists of superposition of the users' signals each composed of multiple orthogonal pilot parts and a polar coded part. We use an iterative approach for decoding the transmitted messages along with a suitable successive interference cancellation scheme. Performance of the proposed scheme is illustrated via extensive set of simulation results which show that it significantly outperforms the existing approaches for URA over multiple-input multiple-output fading channels.

No abstract