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Abstract-This paper includes design and implementation result of an adaptive beam forming antenna for upcoming 5G and Internet of Things (IoT). Switched parasitic array antennas are low cost, small sized and compact circular array antennas that steer beam in a desired direction by variation in switching pattern of parasitic elements. The proposed antenna design has an active center element, which is surrounded by several symmetrically placed parasitic elements. The designed antenna has a gain of 8 dB and is capable of 360 degrees beam steering in steps of 60 degrees each. Simulations are validated with results of the fabricated antenna. Antenna beam is steered by controlling parasitic elements. Future application of Electronically Steerable Parasitic Array Radiator (ESPAR) antennas and switched parasitic array antennas in next generation communication networks and methods for reducing size of the antenna are also highlighted.

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In this paper, a direction-of-arrival (DoA) estimation method based on compressive sensing is proposed for an electronically steerable parasitic array radiator (ESPAR) antenna, which uses only a single radio frequency (RF) chain, and is thereby suited for application in compact wireless terminals. Unlike a conventional multi-active antenna array, signals impinging on parasitic elements in an ESPAR array cannot be processed, and only the output of the sole active element can be processed. In this context, for an ESPAR array, a sparse representation of the DoA estimation problem is formulated by first using an overcomplete dictionary composed of samples from the array manifold and then projecting them onto a set of directional beampatterns. The projection matrix is designed to divide the angle space of the receive antenna array into sectors which are accessed via their corresponding sector beampatterns formed on a time division basis. The sparse signal spectrum is reconstructed by the l1-SVD (singular value decomposition) method [1], where the sparsity is enforced by the l1-norm penalty. Simulation results are presented to demonstrate the efficiency of the proposed method.

Summary Mobile edge computing (MEC) deployed cloud computing resources (such as storage and computing power) to the edge of wireless access networks to better meet the development of 5G communication and computing‐intensive applications. As the first step of MEC architecture deployment, the placement of the edge server (ES) is the foundation and key, and its location affects the user experience and system performance. In this article, we study the placement of ESs in the heterogeneous networks and express it as an optimization problem. Weighing the response delay and energy consumption as the task overhead, and place the ESs on the optimal access point (AP). An adaptive clustering algorithm MTO based on AP suitability evaluation is proposed to solve the optimal solution, which minimizes the task overhead of computing tasks. Extensive experimental simulations evaluate the performance of the algorithm, and the results show that the MTO algorithm is superior to other representative methods.