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Najib Ahmed
العربية

Research

Research

My research is about wireless links that must not fail: short packets from machines, sensors and controllers that need very high reliability and very low delay. I work mostly at the physical layer, on channel codes, preambles and synchronisation, and on network design problems such as where to place gateways in a mesh.

Mission-critical machine-type communication

Mission-critical MTC covers industrial control, vehicle safety and remote operation, where a packet that arrives late is as bad as one that's lost. My IEEE Access survey maps the requirements and open problems on the road to 5G. My later work proposes MC-WiFi, a low-latency, ultra-reliable PHY architecture for this traffic.

  • URLLC
  • Short packets
  • Reliability–latency trade-off

Related publications

5G/6G physical layer & channel coding

For short packets, the usual channel-coding and preamble choices waste most of the frame on overhead. I benchmarked polar codes against LDPC and convolutional codes on an IEEE 802.11-based PHY, reaching packet-error rates of 10⁻⁵ and below. I also designed a short-preamble scheme that reduces overhead while keeping reliability.

  • Polar codes
  • LDPC
  • Preamble design
  • Link-level simulation

Related publications

Wireless mesh networks & gateway placement

In a backbone wireless mesh, every gateway carries the traffic of the routers around it. My placement algorithm balances that load across gateways under multi-hop and capacity constraints. The method is patented (MOSTI Malaysia; US application 15/266,079) and published in Future Generation Computer Systems.

  • Gateway placement
  • Load balancing
  • Multi-hop topology

Related publications

UAV, edge & AI-enabled networks

I'm extending this work to UAV-assisted and non-terrestrial networks, edge and fog computing for latency-critical services, and machine learning for link adaptation and network optimisation, including privacy-preserving deep learning.

  • UAV networks
  • Edge & fog computing
  • Machine learning for networks

Related publications

Methods & tools

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