Symbol Error-Rate Analytical Expressions for a Two-User PD-NOMA System with Square QAM

Power domain non-orthogonal multiple access (PD-NOMA) is one of the most perspective multiplexing technologies that allows improving the capacity of actual networks. Unlike orthogonal multiple access (OMA), the PD-NOMA non-orthogonally schedules multiple users in the power domain in the same orthogonal time-spectrum resource segment. Thus, a non-orthogonal multiplexed signal is a combination of several user signals (usually, modulation and coding schemes (MCS) based on quadrature amplitude modulation) with different power weights. The symbol error rate (SER) and bit error rate (BER) performances are one of the main quality characteristics of any commutation channel. The issue is that a known analytical expression for BER and SER calculation for conventional OMA cannot be applied in terms of the PD-NOMA. In the following work, we have derived the SER and BER analytical expressions for gray-coded square quadrature amplitude modulation (QAM) user channels that are transmitted in two-user PD-NOMA channel under additive white Gaussian noise (AWGN). Through the simulation, the verification of the provided expressions is presented for four multiplexing configurations with various user power weights and QAM order combinations.

Weighted Lebesgue and central Morrey estimates for -adic multilinear Hausdorff operators and its commutators

UDC 517.9 We establish the sharp boundedness of -adic multilinear Hausdorff operators on the product of Lebesgue and central Morrey spaces associated with both power weights and Muckenhoupt weights. Moreover, the boundedness for the commutators of -adic multilinear Hausdorff operators on the such spaces with symbols in central BMO space is also obtained.

Mean Convergence of Entire Interpolations in Weighted Space

We investigate the convergence of entire Lagrange interpolations and of Hermite interpolations of exponential type $$\tau $$ τ , as $$\tau \rightarrow \infty $$ τ → ∞ , in weighted $$L^p$$ L p -spaces on the real line. The weights are reciprocals of entire functions that depend on $$\tau $$ τ and may be viewed as smoothed versions of a target weight w. The convergence statements are obtained from weighted Marcinkiewicz inequalities for entire functions. We apply our main results to deal with power weights.

The Weighted Lp and BMO Estimates for Fractional Hausdorff Operators on the Heisenberg Group

In the setting of Heisenberg group, we characterize those functions Φ, for which the fractional Hausdorff operators TΦ,β and Hausdorff operators TΦ, T˜Φ are bounded on Lp spaces with power weights, BMO space, and Hardy spaces, respectively. Meanwhile, the corresponding operator norms of TΦ and T˜Φ are worked out.

Equitable persistent coverage of non-convex environments with graph-based planning

In this article, we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring every point to be constantly revisited. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared with other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.

Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach

Over-the-air (OTA) radiated testing is an efficient solution to evaluate the performance of multiple-input multiple-output (MIMO) capable devices, which can emulate realistic multipath channel conditions in a controlled manner within lab environment. In a multiprobe anechoic chamber- (MPAC-) based OTA setup, determining the most appropriate probe locations and their power weights is critical to improve the accuracy of channel emulation at reasonable system costs. In this paper, a novel approach based on neural networks (NNs) is proposed to derive suitable angular locations as well as power weights of OTA probe antennas; in particular, by using the regularization technique, active probe locations and their weights can be optimized simultaneously with only one training process of the proposed NN. Simulations demonstrate that compared with the convex optimization-based approach to perform probe selection in the literature, e.g., the well-known multishot algorithm, the proposed NN-based approach can yield similar channel emulation accuracy with significantly reduced computational complexity.