arXiv subjects

Mingjie Feng

Publications and source records attributed to Mingjie Feng.

9 recordsLinked to original sources

arXiv2026

Behavioral-Level Simulation of Digital Readout for COFFEE at LHCb Upstream Pixel Tracker

COFFEE series is a HVCMOS pixel sensor using the advanced 55 nm process, currently being developed for the Upstream Pixel (UP) tracker of the LHCb Upgrade II. To ensure that COFFEE will be able to handle the particle hit rates at UP tracker, which reach a maximum of 322.5 MHz/chip, detailed simulation of the digital readout circuitry was performed. Simulation results show that the column-drain readout mechanism achieves nearly 100\% efficiency when the single readout cycle does not exceed 100 ns. Meanwhile, the buffer depth and memory resources required for the peripheral readout adapted to the BXID-sharing data format are also evaluated. These provide guidance for the design of COFFEE. The column-drain readout mechanism was used in COFFEE3 (fabricated in 2025), while the peripheral readout architecture adapted to the BXID-sharing data format is implemented in CHiR (taped out in early 2026).

physics.ins-det↗

arXiv2026

A Telescope System for Charge and Position Measurement of High Energy Nuclei

A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address the challenges of nuclear charge measurement with SSDs. The system achieves a spatial resolution of $\mathcal{O}(1) \,$\SI{}{\micro\metre} and a charge resolution better than 0.16 charge units for nuclei from $Z = 1$ to $Z = 29$, with a sensitive area of $8 \times 8 \, \mathrm{cm}^2$. To the best of our knowledge, this represents the most precise charge and spatial resolution simultaneously achieved by a silicon telescope to date.

physics.ins-det↗

arXiv2026

Beam Test Characterization of Silicon Microstrip Detector Flight-Model Ladders for the AMS-02 Upgrade

The AMS-02 experiment plans to install a new silicon microstrip tracker layer (Layer-0) on top of the existing detector, increasing the cosmic-ray acceptance by a factor of 3. Layer-0 employs a design in which multiple silicon microstrip detectors (SSDs) are connected in series to form long detector ladders. We present a detailed performance study of the flight-model ladders using a 350~GeV mixed hadron beam at the CERN SPS. The study focuses on the following aspects: (i) the performance of ladders with different numbers of SSDs, for which the intrinsic spatial resolution at normal incidence varies from $9.5~\mu\mathrm{m}$ to $11.4~\mu\mathrm{m}$ for ladders composed of 8 to 12 SSDs; (ii) the response consistency for particles impacting on the \emph{Head} and \emph{Tail} regions of the ladder; and (iii) the dependence of the detector performance on the particle incidence angle.

physics.ins-det↗

arXiv2026

Prospects for discovering strongly decaying doubly heavy $T_{bc}$ tetraquark states at LHCb

We investigate the discovery potential of the $T_{bc}$ state with $J^P = 0^+$ in proton-proton ($pp$) collisions at LHCb at a center-of-mass energy of $\sqrt{s} = 13~\mathrm{TeV}$. The study focuses on the decay channel $T_{bc} \to B^- D^+$. A phenomenological approach is employed to construct the background model based on the associated production of $B$ and $D$ mesons, incorporating previously published LHCb results. Background processes are simulated using $\texttt{MadGraph5\_aMC@NLO}$ and $\texttt{Pythia8.3}$. We explore the parameter space of the $T_{bc}$ mass, width, production cross section, and the effective double-parton scattering cross section ($\sigma_{\mathrm{eff}}$) relevant for the $B D$ meson background. The integrated luminosity required for a $5\sigma$ discovery at LHCb is evaluated under various assumptions. In particular, we consider three representative $T_{bc}$ production cross section scenarios: an optimistic estimate of $103~\mathrm{nb}$, an intermediate value of $18~\mathrm{nb}$ obtained by scaling from the $T_{cc}^+$ production cross section, and a conservative lower bound of $0.3~\mathrm{nb}$. We find that a $5\sigma$ observation is achievable for a production cross section of $103~\mathrm{nb}$, which is expected to be within reach during Run~4. In contrast, the more realistic cross section estimate of $18~\mathrm{nb}$ requires the full Run~5 dataset ($300~\mathrm{fb}^{-1}$) under the most favorable parameter choices. For the conservative scenario, no significant signal would be observable even with $300~\mathrm{fb}^{-1}$. In addition, we estimate the minimum observable $\sigma(T_{bc}) \times BR(T_{bc} \to B^- D^+)$ for a $5\sigma$ discovery under different luminosity scenarios, providing guidance for future experimental searches at LHCb.

hep-ph↗

arXiv2018

Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach

Millimeter Wave (MmWave) communication is one of the key technology of the fifth generation (5G) wireless systems to achieve the expected 1000x data rate. With large bandwidth at mmWave band, the link capacity between users and base stations (BS) can be much higher compared to sub-6GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links, the backhaul connection becomes the new bottleneck that limits the system performance. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem backhaul resource allocation and present the DRL based solution. Finally, we discuss open problems for future research and conclude this article.

eess.SP↗

arXiv2017

Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges

To achieve the expected 1000x data rates under the exponential growth of traffic demand, a large number of base stations (BS) or access points (AP) will be deployed in the fifth generation (5G) wireless systems, to support high data rate services and to provide seamless coverage. Although such BSs are expected to be small-scale with lower power, the aggregated energy consumption of all BSs would be remarkable, resulting in increased environmental and economic concerns. In existing cellular networks, turning off the under-utilized BSs is an efficient approach to conserve energy while preserving the quality of service (QoS) of mobile users. However, in 5G systems with new physical layer techniques and the highly heterogeneous network architecture, new challenges arise in the design of BS ON-OFF switching strategies. In this article, we begin with a discussion on the inherent technical challenges of BS ON-OFF switching. We then provide a comprehensive review of recent advances on switching mechanisms in different application scenarios. Finally, we present open research problems and conclude the paper.

cs.NI↗

arXiv2016

Harvest the potential of massive MIMO with multi-layer techniques

Massive MIMO is envisioned as a promising technology for 5G wireless networks due to its high potential to improve both spectral and energy efficiency. Although the massive MIMO system is based on innovations in the physical layer, the upper layer techniques also play important roles in harvesting the performance gains of massive MIMO. In this article, we begin with an analysis of the benefits and challenges of massive MIMO systems. We then investigate the multi-layer techniques for incorporating massive MIMO in several important network deployment scenarios. We conclude this article with a discussion of open and potential problems for future research.

cs.NI↗

arXiv2016

Interference Management in Massive MIMO HetNets: A Nested Array Approach

The nested array, which is implemented by nonuniform antenna placement, is an effective approach to achieve O(N^2) degrees of freedom (DoF) with an antenna array of N antennas. Such DoF refers to the number of directions of incoming signals that can be resolved. With the increased number of DoF, an important application of nested array is to nullify the interference signals from multiple directions. In this paper, we apply nested array in a massive MIMO heterogeneous network (HetNet) for interference management. With nested array based interference nulling, each base station (BS) can nullify a certain number of interference signals. A key design issue is to select the interference sources to be nullified at each BS. We formulate this problem as an integer programming problem. The objective is to maximize the sum rate of all users, subject to BS DoF constraints. We propose an approximation scheme to solve this problem and derive a performance upper bound. Simulation results show that the proposed scheme effectively improves the sum rate and achieves a near optimal performance.

cs.NI↗

arXiv2016

BOOST: Base station on-off switching strategy for energy efficient massive MIMO HetNets

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

cs.NI↗