arXiv subjects

Ahmed El-Mahdy

Publications and source records attributed to Ahmed El-Mahdy.

7 recordsLinked to original sources

arXiv2026

Optimal Base Station Placement for Beyond 5G Networks with Non-Convex Topology

This paper investigates the optimal placement of a millimeter-wave (mmWave) base station (BS) within a realistic U-shaped environment with non-convex topology. The problem is challenging and NP-hard due to the non-convex topology and the non-convex objective functions which are the sum-rate maximization and max-min fairness, the latter being additionally non-smooth. To address this challenge, the BS placement is formulated as a Markov Decision Process (MDP). Then, we propose two deep reinforcement learning (DRL) techniques: First, the deployment area is discretized into a grid and optimized using a Deep Q-Network (DQN). Second, the U-shaped region is partitioned into continuous subspaces, where a Deep Deterministic Policy Gradient (DDPG) agent is dedicated to each subspace then the best BS placement is selected among partitions. Results demonstrate that optimal placement achieves full coverage and yields a Jain index of 0.99. Furthermore, the proposed partitioned multi-space DDPG achieves better solution than DQN with lower complexity.

eess.SP↗

arXiv2026

On the Sensitivity of Active RIS Systems to CSI Errors: Joint Optimization and Performance Trade-off

In this paper, the problem of maximizing the sum-rate is addressed for a multi-user uplink scenario that is assisted by an active reconfigurable intelligent surface (RIS). The maximization is achieved by optimizing the beamforming at the base station, the users' transmit power, active RIS elements phase shifts, and active gains in presence of imperfect channel state information (CSI). The non-convex maximization problem is decomposed into sub-problems and solved via iterative approaches including the Lagrangian method, the projected gradient descent, multi-variate Taylor expansion and fractional programming. Numerical results show that the active RIS is more sensitive to CSI imperfections than passive one at high error variances.

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arXiv2025

Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications

Active Reconfigurable Intelligent Surfaces (RIS) are a promising technology for 6G wireless networks. This paper investigates a novel hybrid deep reinforcement learning (DRL) framework for resource allocation in a multi-user uplink system assisted by multiple active RISs. The objective is to maximize the minimum user rate by jointly optimizing user transmit powers, active RIS configurations, and base station (BS) beamforming. We derive a closed-form solution for optimal beamforming and employ DRL algorithms: Soft actor-critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3) to solve the high-dimensional, non-convex power and RIS optimization problem. Simulation results demonstrate that SAC achieves superior performance with high learning rate leading to faster convergence and lower computational cost compared to DDPG and TD3. Furthermore, the closed-form of optimally beamforming enhances the minimum rate effectively.

eess.SP↗

arXiv2017

An Efficient Load Balancing Method for Tree Algorithms

Nowadays, multiprocessing is mainstream with exponentially increasing number of processors. Load balancing is, therefore, a critical operation for the efficient execution of parallel algorithms. In this paper we consider the fundamental class of tree-based algorithms that are notoriously irregular, and hard to load-balance with existing static techniques. We propose a hybrid load balancing method using the utility of statistical random sampling in estimating the tree depth and node count distributions to uniformly partition an input tree. To conduct an initial performance study, we implemented the method on an Intel Xeon Phi accelerator system. We considered the tree traversal operation on both regular and irregular unbalanced trees manifested by Fibonacci and unbalanced (biased) randomly generated trees, respectively. The results show scalable performance for up to the 60 physical processors of the accelerator, as well as an extrapolated 128 processors case.

cs.DC↗

arXiv2016

If-Conversion Optimization using Neuro Evolution of Augmenting Topologies

Control-flow dependence is an intrinsic limiting factor for pro- gram acceleration. With the availability of instruction-level par- allel architectures, if-conversion optimization has, therefore, be- come pivotal for extracting parallelism from serial programs. While many if-conversion optimization heuristics have been proposed in the literature, most of them consider rigid criteria regardless of the underlying hardware and input programs. In this paper, we propose a novel if-conversion scheme that preforms an efficient if-conversion transformation using a machine learning technique (NEAT). This method enables if-conversion customization overall branches within a program unlike the literature that considered in- dividual branches. Our technique also provides flexibility required when compiling for heterogeneous systems. The efficacy of our approach is shown by experiments and reported results which il- lustrate that the programs can be accelerated on the same archi- tecture and without modifying the original code. Our technique applies for general purpose programming languages (e.g. C/C++) and is transparent for the programmer. We implemented our tech- nique in LLVM 3.6.1 compilation infrastructure and experimented on the kernels of SPEC-CPU2006 v1.1 benchmarks suite running on a multicore system of Intel(R) Xeon(R) 3.50GHz processors. Our findings show a performance gain up to 8.6% over the stan- dard optimized code (LLVM -O2 with if-conversion included), in- dicating the need for If-conversion compilation optimization that can adapt to the unique characteristics of every individual branch.

cs.DC↗

arXiv2013

A Linear-Time and Space Algorithm for Optimal Traffic Signal Durations at an Intersection

Finding an optimal solution of signal traffic control durations is a computationally intensive task. It is typically O(T3) in time, and O(T2) in space, where T is the length of the control interval in discrete time steps. In this paper, we propose a linear time and space algorithm for the same problem. The algorithm provides for an efficient dynamic programming formulation of the state space, the prunes non-optimal states, early on. The paper proves the correctness of the algorithm and provides an initial experimental validation.

cs.DS↗

arXiv2013

Thread-Based Obfuscation through Control-Flow Mangling

The increasing use of cloud computing and remote execution have made program security especially important. Code obfuscation has been proposed to make the understanding of programs more complicated to attackers. In this paper, we exploit multi-core processing to substantially increase the complexity of programs, making reverse engineering more complicated. We propose a novel method that automatically partitions any serial thread into an arbitrary number of parallel threads, at the basic-block level. The method generates new control-flow graphs, preserving the blocks' serial successor relations and guaranteeing that one basic-block is active at a time using guards. The method generates m^n different combinations for m threads and n basic-blocks, significantly complicating the execution state. We provide a correctness proof for the algorithm and implement the algorithm in the LLVM compilation framework.

cs.CR↗