arXiv subjects

Firooz B. Saghezchi

Publications and source records attributed to Firooz B. Saghezchi.

4 recordsLinked to original sources

arXiv2026

Blind and Asynchronous Initial Beam Acquisition and Clock-Assisted Re-Acquisition in Standalone Mobile MIMO-OFDM Systems

Building upon our preliminary study, this paper proposes a standalone beam-management framework for mobile MIMO-OFDM systems that integrates Blind Initial Beam Acquisition (BIBA) with Clock-Assisted Re-Acquisition (CARA). BIBA is a blind and asynchronous protocol that requires neither prior synchronization nor an out-of-band feedback channel, so the Base Station (BS) and User Equipment (UE) can independently identify the beam pair maximizing received signal strength. To handle subsequent link drops, the newly introduced CARA protocol exploits the clock synchronization established after BIBA to reduce the re-acquisition time by approximately 55% compared with BIBA. The operational reliability of both protocols is bounded by a probabilistic feasibility analysis under random UE mobility trajectories. Protocols are validated on a 28 GHz mmWave testbed under practical hardware constraints, including beam-switching and Time Division Duplex (TDD) latencies, and measured Software-Defined Radio (SDR) software processing delays. Finally, we formulate a unified Finite State Machine (FSM) model for both BIBA and CARA to ensure robust integration into practical wireless systems

eess.SP↗

arXiv2025

Deep Reinforcement Learning for Backhaul Link Selection for Network Slices in IAB Networks

Integrated Access and Backhaul (IAB) has been recently proposed by 3GPP to enable network operators to deploy fifth generation (5G) mobile networks with reduced costs. In this paper, we propose to use IAB to build a dynamic wireless backhaul network capable to provide additional capacity to those Base Stations (BS) experiencing congestion momentarily. As the mobile traffic demand varies across time and space, and the number of slice combinations deployed in a BS can be prohibitively high, we propose to use Deep Reinforcement Learning (DRL) to select, from a set of candidate BSs, the one that can provide backhaul capacity for each of the slices deployed in a congested BS. Our results show that a Double Deep Q-Network (DDQN) agent using a fully connected neural network and the Rectified Linear Unit (ReLU) activation function with only one hidden layer is capable to perform the BS selection task successfully, without any failure during the test phase, after being trained for around 20 episodes.

cs.NI↗

arXiv2025

Reconfigurable Intelligent Surface-Enabled Physical-Layer Network Coding for Higher Order M-QAM Signals

Physical-Layer Network Coding (PNC) is an effective technique to improve the throughput and latency in wireless networks. However, there are two major challenges for PNC, especially when using higher order modulations: 1) phase synchronization and power control at the paired User Equipments (UEs); and 2) the ambiguity removal of the PNC mapping at the relay node. To address these challenges, in this paper, we apply power control at transmitting UEs and exploit Reconfigurable Intelligent Surfaces (RISs) to synchronize the phase of the transmitted signals and ensure that they arrive at the relay with the same power and phase rotation. Then, we employ modular addition for an unambiguous PNC mapping for M-ary Quadrature Amplitude Modulations (M-QAM). We evaluate the performance of the system in the framework of Orthogonal Frequency Division Multiplexing (OFDM)-PNC for different RIS sizes and modulation orders. Furthermore, we study the sensitivity of PNC systems for Channel Estimation Error (CEE). The results reveal that 1) PNC systems show quite higher sensitivity to CEE compared with RIS-assisted one-way relay channel systems; 2) when the CEE is low, RIS can considerably enhance the Signal-to-Noise Ratio (SNR) of the PNC system, e.g., for a Bit Error Rate (BER) of $10^{-3}$ (without channel coding), increasing the RIS size from one to 256 elements in 28 GHz band leads to 200% improvement in SNR.

cs.NI↗

arXiv2025

Intelligent Backhaul Link Selection for Traffic Offloading in B5G Networks

Fifth Generation (5G) mobile networks considers an expansive set of heterogeneous services with stringent Quality of Service (QoS) requirements, and traffic demand with inherent spatial-temporal distribution, which places the backhaul network deployment under potential strain. In this paper, we propose to harness network slicing, Integrated Access and Backhaul (IAB) technology coupled with satellite connectivity to build a dynamic wireless backhaul network that can provide additional backhaul capacity to the base stations on demand when the wired backhaul link is temporarily out of capacity. To construct the network design, Deep Reinforcement Learning (DRL) models are used to select, for each network slice of the congested base station, an appropriate backhaul link from the pool of available IAB and satellite links that meets the QoS requirements (i.e., throughput and latency) of the slice. Simulation results show that around 20 episodes are sufficient to train a Double Deep Q-Network (DDQN) agent, with one fully-connected hidden layer and Rectified Linear Unit (ReLU) activation function, that adjusts the topology of the backhaul network.

cs.NI↗