15 papers
Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
Anis Hamadouche, Amir Hussain
Deep neural networks often contain substantial hidden-state redundancy, but most compression methods operate directly on weights, neurons, or quantised representations without expl…
Lyapunov-Guided Training for Hardware-Safe Neural Networks Under Fixed-Point Arithmetic
Anis Hamadouche, Amir Hussain
Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation…
Leveraging Kernel Symmetry for Joint Compression and Error Mitigation in Edge Model Transfer
Anis Hamadouche, Mathini Sellathurai
This paper investigates communication-efficient neural network transmission by exploiting structured symmetry constraints in convolutional kernels. Instead of transmitting all mode…
Stochastic Delayed Dynamics of Rumor Propagation with Awareness and Fact-Checking
Lamia Alyami, Anis Hamadouche, Amir Hussain
This paper presents a stochastic delayed differential model for rumor propagation during infodemic that incorporates human behavioral response, public skepticism and fact-checking…
Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
Anis Hamadouche, Haifeng Luo, Mathini Sellathurai +2
Real-time audio-visual speech enhancement (AVSE) is a key enabler for immersive and interactive multimedia services, yet its performance is tightly constrained by network latency,…
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II
Anis Hamadouche, Tharm Ratnarajah, Christos Masouros +2
This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aw…
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems I
Anis Hamadouche, Tharm Ratnarajah, Christos Masouros +2
This paper addresses the optimization challenges in dual-functional radar-communication (DFRC) systems with a focus on array selection and beamforming in dynamic and heterogeneous…
Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks
Anis Hamadouche, Mathini Sellathurai
This paper introduces a novel framework for jointly estimating unknown radar channels and transmit signals in millimeter-wave (mmWave) Joint Radar-Communication (JRC) systems, a pr…
Trade-offs in Reliability and Performance Using Selective Beamforming for Ultra-Massive MIMO
Anis Hamadouche, Mathini Sellathurai
This paper addresses the optimization challenges in Ultra-Massive MIMO communication systems, focusing on array selection and beamforming in dynamic and diverse operational context…
Context-Enhanced CSI Tracking Using Koopman-Inspired Dual Autoencoders in Dynamic Wireless Environments
Anis Hamadouche, Mathini Sellathurai
This paper introduces a novel framework for tracking and predicting Channel State Information (CSI) by leveraging Physics-Informed Autoencoders (PIAE) integrated with a learned Koo…
Reconfigurable FPGA-Based Solvers For Sparse Satellite Control
Anis Hamadouche, Yun Wu, Mathini Sellathurai +2
This paper introduces a novel reconfigurable and power-efficient FPGA (Field-Programmable Gate Array) implementation of an operator splitting algorithm for Non-Terrestial Network's…
Improved Convergence Bounds For Operator Splitting Algorithms With Rare Extreme Errors
Anis Hamadouche, Andrew M. Wallace, Joao F. C. Mota
In this paper, we improve upon our previous work[24,22] and establish convergence bounds on the objective function values of approximate proximal-gradient descent (AxPGD), approxim…
A Low-Power Hardware-Friendly Optimisation Algorithm With Absolute Numerical Stability and Convergence Guarantees
Anis Hamadouche, Yun Wu, Andrew M. Wallace +1
We propose Dual-Feedback Generalized Proximal Gradient Descent (DFGPGD) as a new, hardware-friendly, operator splitting algorithm. We then establish convergence guarantees under ap…
Probabilistic Verification of Approximate Algorithms with Unstructured Errors: Application to Fully Inexact Generalized ADMM
Anis Hamadouche, Yun Wu, Andrew M. Wallace +1
We analyse the convergence of an approximate, fully inexact, ADMM algorithm under additive, deterministic and probabilistic error models. We consider the generalized ADMM scheme th…
Sharper Bounds for Proximal Gradient Algorithms with Errors
Anis Hamadouche, Yun Wu, Andrew M. Wallace +1
We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tight…