One of the main targets of the forthcoming fifth-generation (5G) cellular network will be the support of the communications for billions of sensors and actuators, so as to finally realize the Internet of things (IoT) paradigm. This pervasive scenario unavoidably requires the design of cheap antenna systems with beamforming capabilities for compensating the strong attenuations that characterize the millimeter-wave (mmWave) channel. To address this issue, this paper proposes an iterative algorithm for sparse antenna arrays that enables to derive the number of elements, their amplitudes, phases, and positions in the presence of constraints on the far-field pattern. The algorithm, which relies on the compressive sensing approach, is formulated by transforming the original nonconvex optimization problem into a convex one. To prove the suitability of the conceived solution for 5G IoT mmWave applications, numerical examples and comparisons with other existing methods are provided, considering synthesis problems with different pattern and aperture specifications.

In this paper, a novel class of serially concatenated convolutional codes (SCCCs) is addressed. In contrast to standard SCCCs, where high rates are obtained by puncturing the outer code, the heavy puncturing is moved to the inner code, which can be punctured beyond the unitary rate. We derive analytical upper bounds on the error probability of this code structure by considering an equivalent code construction consisting of the parallel concatenation of two codes, and address suitable design guidelines for code optimization. It is shown that the optimal puncturing of the inner code depends on the outer code, i.e., it is interleaver dependent. This dependence cannot be tracked by the analysis for standard SCCCs, which fails in predicting code performance. Based on the considerations arising from the bounds analysis, we construct a family of rate-compatible SCCCs with a high level of flexibility and a good performance over a wide range of code rates, using simple constituent codes. The error rate performance of the proposed codes is found to be better than that of standard SCCCs, especially for high rates, and comparable to the performance of more complex turbo codes.

Multibeam antenna arrays are currently recognized as one of the enabling technologies for the next-generation communication standards. One of the key components of these systems is the beamforming network (BFN) that implements the array element excitations. This article addresses this issue by presenting a novel strategy to realize an analog feeding network, which allows an arbitrary linear array (LA) to radiate multiple arbitrary beams. In particular, an iterative procedure is conceived to design a Blass matrix using an identical directional coupler for all nodes, resulting in a very simple structure suitable for large-scale production. Two applications with arbitrary directions are illustrated as proofs-of-concept for the developed architecture: a dual-beam configuration with a null involving an aperiodic LA, and a four-beam configuration involving a periodic LA. For this second application, the effectiveness of the proposed solution is further verified by full-wave simulations and experimental measurements carried out on a fabricated prototype.

This paper presents an iterative algorithm for the synthesis of the three-dimensional (3D) radiation pattern generated by an antenna array of arbitrary geometry. The algorithm is conceived to operate in fifth-generation (5G) millimeter-wave scenarios, thus enabling the support of multi-user mobile streaming and massive peer-to-peer communications, which require the possibility to synthesize 3D patterns with wide null regions and multiple main beams. Moreover, the proposed solution adopts a phase-only control approach to reduce the complexity of the feeding network and is characterized by a low computational cost, thanks to the closed-form expressions derived to estimate the phase of each element at the generic iteration. These expressions are obtained from the minimization of a weighted cost function that includes all the necessary constraints. To finally check its versatility in a 5G environment, the developed method is validated by numerical examples involving planar and conformal arrays, considering desired patterns with different numbers of main beams and nulls.

Since irregular low‐density parity‐check (LDPC) codes are known to perform better than regular ones, and to exhibit, like them, the so‐called ‘threshold phenomenon’, this Letter investigates a low‐complexity upper bound on belief‐propagation decoding thresholds for this class of codes on memoryless binary input additive white Gaussian noise channels, with sum‐product decoding. A simplified analysis of the belief‐propagation decoding algorithm is used, i.e. consider a Gaussian approximation for message densities under density evolution, and a simple algorithmic method, defined recently, to estimate the decoding thresholds for regular and irregular LDPC codes.