This article presents an efficient method for modeling and analysis of memristor crossbar array circuit-based neuromorphic computing hardware. A novel volatile memristor-based artificial neuron circuit is proposed, realizing real-time generation of spike sequences, where the amplitude, width, and reserve time of the spikes are all adjustable. Based on proposed neuron circuits and nonvolatile memristor-based artificial synapses, a spike timing-dependent plasticity (STDP) circuit implementation with a simple spike-mapping method is proposed and used for a memristor crossbar array-based spiking neural network (SNN) circuit. A circuit model of a crossbar array combined with a partial equivalent element circuit (PEEC) and volume filament (VFI) is used to calculate the circuit capacitance, inductance, and resistance. It is demonstrated by a transient simulation of a crossbar array circuit that the proposed SNN method can perform image classification tasks, and the recognition accuracy decreases with the frequency increase. From the perspective of affecting the spike sequences and STDP rules, damage caused by device stochasticity, parasitic effects, and skin effect on SNN recognition performance is further analyzed and evaluated.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

When sharing the unlicensed band with Wi-Fi users in unlicensed LTE (U-LTE) systems, the most critical issue is how to guarantee the harmonious coexistence between the two systems. On the other hand, when sharing the licensed band with the micro base station (MBS) in the small cell base station (SBSs), the co-channel interference needs to be properly coordinated. To address these two issues, power control, licensed and unlicensed spectrum allocation are jointly considered at the SBS to maximize the spectrum efficiency while guaranteeing the QoS of small cell users (SUs) and at the same time providing fair resource sharing with Wi-Fi users. The convex optimization method is applied to design the optimal scheme. Then, numerical results are provided to verify the proposed scheme and demonstrate the tradeoff between the Wi-Fi performance and the achievable spectrum efficiency at the SBS.

In this study, a distributed biasing scheme is designed to achieve load balancing for heterogeneous networks. Based on the limited backhaul capacity and user distribution in the system, each small cell base station adaptively and distributively changes its cell range by setting the bias value, to effectively utilise the wireless resource and achieve load balancing as well. The Q ‐learning algorithm is adopted to design the biasing scheme in each small cell base station. The tradeoff between the backhaul resource utilisation and the quality‐of‐service of users is considered in the reward function of the Q ‐learning model. To examine the performance of the distributed scheme, a centralised scheme aiming at maximising the backhaul resource utilisation is also proposed for comparison, whose performance lower bound is derived. Numerical results show that the proposed distributed scheme can effectively utilise the backhaul resource for load balancing, and achieve a close performance to the centralised one.

In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co‐channel interference for small‐cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality‐of‐service cannot be satisfied because of the severe co‐channel interference from other users. Since the general bargaining problem is a non‐linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low‐complexity bargaining model which only takes into account the strongest co‐channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low‐complexity bargaining solution can achieve a close performance to the genetic algorithm‐based solution.