A single feed broadband circularly polarized (CP) microstrip antenna operating in millimeter wave (mmWave) band is proposed. An Archimedean spiral radiator is used to realize circular polarization. By adding a special-shaped ring slot structure with two stubs connecting the inner patch to the outer ground, the radiating direction, bandwidth and axial ratio (AR) of the proposed antenna can be significantly improved. The antenna prototype is fabricated and its performance is evaluated. Measurement results show that its impedance bandwidth (IBW) is 13 GHz from 21.1 GHz to 34.1 GHz (46.43% referring to 28 GHz). The axial ratio bandwidth (ARBW) is 8.35 GHz from 23.85 GHz to 32.2 GHz (29.82% referring to 28 GHz). The gain at 28 GHz is 6.49 dBi.

A millimeter-wave (mmWave) triple-band substrate integrated waveguide (SIW) antenna with dual-sense circular polarization (CP) etched on a single substrate is proposed. A SIW cavity operating in TM210 mode is carved with a circular slot to realize upper band radiation. A circular patch is placed in the circular slot for lower band radiation. Middle band radiation is realized by the coupling between the SIW cavity and circular patch. By introducing asymmetry on both circular slot and circular patch, CP is obtained at two bands. The polarization states of the three bands are right-hand circular polarization (RHCP), linear polarization (LP) and left-hand circular polarization (LHCP). Three pairs of vias are introduced in SIW transmission line to realize good impedance matching for the three bands. A prototype is fabricated and its performances are evaluated. Simulated and measured results agree very well. Small frequency ratios are achieved and each band can be adjusted flexibly. The proposed antenna has characteristics of low-profile and simple structure which will make it easy to be applied in future mmWave wireless communication systems.

Current mobile networks are facing dramatic growth in wireless traffics due to the prosperity of streaming media services. Cooperative edge caching, enabling multiple edge nodes to cache and share contents by exploiting the spatial/temporal user request differentiation, is regarded as a promising method to enhance Quality of Experience (QoE). However, frequent content sharing between BSs consumes operation cost such as the usage of cross-edge bandwidth and energy consumption. Therefore, new challenges incurred by performance-cost trade-off arise. In this paper, we propose a user preference-aware content caching and migration (PACM) scheme for video content delivery in a cost-efficient edge network. In this scheme, the dynamic user request preference and the long-term content migration cost budget are considered for content placement and delivery. To navigate a good performance-cost trade-off, we formulate the content caching and migration to be a long-term optimization problem. Then, the Lyapunov optimization method is used to decompose the problem into a series of real-time optimizations. As the decomposed problem is NP-hard, we design a novel collective reinforcement learning (CRL) algorithm that can realize online efficient decision-making by interacting with training experience. Simulation results show that the CRL algorithm has a high convergence rate and the proposed scheme can achieve quasi-optimal performance in terms of user-perceived latency, cache hit rate, and video stalling rate.

Unmanned aerial vehicles (UAVs)-assisted multiaccess edge computing (MEC) platforms are becoming an increasingly popular solution for infrastructure-less Internet of Vehicles (IoV) due to their mobility and flexibility. To address the challenges of uneven task offloading and vehicle mobility, in this article, we propose a mobility-aware service offloading and migration scheme for UAV-assisted IoV. We formulate the service placement, service migration (SM), and UAV deployment as an optimization problem to minimize the serving delay of task addressing for IoV, under a predefined long-term migration cost budget. To solve the problem, we use the Lyapunov optimization method to transform the long-term optimization into a real-time optimization problem. Additionally, we design a multiagent deep deterministic policy gradient (MADDPG) algorithm to solve the problem. Compared with traditional central optimization methods, the proposed algorithm can achieve a near-global optimal policy by leveraging only local observation information. Simulation results show that the proposed MADDPG algorithm can achieve good convergence performance, and the proposed scheme can achieve quasi-optimal performance in terms of serving delay, service offloading rate, and SM cost.

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.