Intelligent reflecting surface (IRS) is capable of constructing the favorable wireless propagation environment by leveraging massive low-cost reconfigurable reflectarray elements. In this paper, we investigate the IRS-aided MIMO simultaneous wireless information and power transfer (SWIPT) for Internet of Things (IoT) networks, where the active base station (BS) transmit beamforming and the passive IRS reflection coefficients are jointly optimized for maximizing the minimum signal-tointerference-plus-noise ratio (SINR) among all information decoders (IDs), while maintaining the minimum total harvested energy at all energy receivers (ERs). Moreover, the IRS with practical discrete phase shifts is considered, and thereby the max-min SINR problem becomes a NP-hard combinatorial optimization problem with a strong coupling among optimization variables. To explore the insights and generality of this maxmin design, both the Single-ID Single-ER (SISE) scenario and the Multiple-IDs Multiple-ERs (MIME) scenario are studied. In the SISE scenario, the classical combinatorial optimization techniques, namely the special ordered set of type 1 (SOS1) and the reformulation-linearization (RL) technique, are applied to overcome the difficulty of this max-min design imposed by discrete optimization variables. Then the optimal branch-and-bound algorithm and suboptimal alternating optimization algorithm are respectively proposed. We further extend the idea of alternating optimization to the MIME scenario. Moreover, to reduce the iteration complexity, a two-stage scheme is considered aiming to separately optimize the BS transmit beamforming and the IRS reflection coefficients. Finally, numerical simulations demonstrate the superior performance of the proposed algorithms over the benchmarks in both the two scenarios.

The use of simultaneous wireless information and power transfer (SWIPT) is a key enabler of achieving convenience and prolonging the energy supply lifetime of wireless networks. To address the low efficiencies of far-field power transfer, a reconfigurable intelligent surface (RIS) is adopted to enhance the energy harvesting (EH) performance, which can construct a favorable wireless propagation environment. In this paper, we consider an RIS-assisted SWIPT system with wireless transfer from the access point (AP) to multiple-antenna receivers, which include information receivers (IRs) and energy receivers (ERs). First, we formulate the problem of maximizing the minimum rates of the IRs as a nonconvex constrained optimization problem. For ideal and nonideal channels, we propose two different solutions. Second, we simplify the objective function and decompose the problem into several subproblems by using sorting and iterative optimization algorithms. Moreover, under optimal boundary and Karush-Kuhn-Tucker (KKT) conditions, we successfully solve this problem. The simulation results illustrate a promising approach for wireless communication by comparing ideal RIS and unsatisfactory situation with no RIS cases under various conditions.INDEX TERMS Reconfigurable intelligent surface (RIS), simultaneous wireless information and power transfer (SWIPT), beamforming, alternating optimization.

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Severe multi-path effects in the process of high-speed data transmission cause inter-symbol interference (ISI). The single carrier frequency domain equalization (SC-FDE) method combining the benefits of Feher-patented quadrature phase shift keying (FQPSK) modulation is raised to solve this problem, including the constant envelope, high spectrum efficiency, and insensitivity to nonlinear distortion. Furthermore, a fractionally spaced frequency domain equalization (FS-FDE) system based on FQPSK modulation is modeled based on the proposed algorithm. Simulation results confirm the superior performance of the proposed model on erasing ISI, comparing to the symbol-spaced FDE (SS-FDE) system based on FQPSK modulation and the FS-FDE system based on QPSK modulation.

In order to reduce the peak load on the power grid, various types of demand response (DR) programs have been developed rapidly, and an increasing number of residents have participated in the DR. The change in residential electricity consumption behavior increases the randomness of electricity load power, which makes residential load forecasting relatively difficult. Aiming at increasing the accuracy of residential load forecasting, an innovative electricity consumption pattern clustering is implemented in this paper. Six categories of residential load are clustered considering the power consumption characteristics of high-energy-consuming equipment, using the entropy method and criteria importance though intercrieria correlation (CRITIC) method. Next, based on the clustering results, the residential load is predicted by the fully-connected deep neural network (FDNN). Compared with the prediction result without clustering, the method proposed in this paper improves the accuracy of the prediction by 5.21%, which is demonstrated in the simulation.