arXiv subjects

Maziar Nekovee

Publications and source records attributed to Maziar Nekovee.

At least 19 recordsLinked to original sources

arXiv2026

Compensating Coarse Quantization in Massive MIMO: Channel Estimation and BER under Imperfect CSI

Low-resolution quantization is essential to reduce implementation cost and power consumption in massive multiple-input multiple-output (MIMO) systems for 5G and 6G. While most existing studies assume perfect channel state information (CSI), we model the impact of coarse quantization noise on both channel estimation and data transmission, yielding a more realistic assessment of system performance under imperfect CSI conditions in the uplink. We develop a tight approximation for the bit-error ratio (BER) of uncoded M-QAM with zero-forcing detection, based on the linear minimum mean-square error (LMMSE) channel estimate. These analytical results enable compensation strategies that jointly optimize quantization resolution, transmit power, and pilot length across different numbers of users and base station antennas. We further demonstrate the applicability of the proposed framework through several design scenarios that highlight its effectiveness in optimizing system parameters and improving energy efficiency under quantization constraints. For example, in a 16-QAM system, extending the pilot sequence by 2.5 times and lowering transmit power by 0.5 dB enables a 3-bit quantized system to match the BER of the full-resolution case. The proposed framework offers a fast and accurate alternative to Monte Carlo simulations, enabling practical system optimization under realistic quantization constraints.

eess.SP↗

arXiv2026

LLM-Based Net Analyzer rApp for Explainable and Safe Automation in O-RAN Non-RT RIC

Modern 5G/6G radio access networks are increasingly programmable through O-RAN, yet their operational complexity has grown with disaggregation, open interfaces, and fine-grained control parameters. While RAN-side analytics and telemetry mechanisms, such as KPI-based monitoring and mobility event reporting, provide visibility into network behavior, operators still face challenges in correlating heterogeneous events and safely translating observations into actionable configuration changes. This paper presents an LLM-based Net Analyzer rApp for the O-RAN Non-RT RIC that enables explainable and safe, human-in-the-loop automation for RAN operations. The proposed rApp adopts an event-informed, batch-triggered reasoning framework in which mobility events are first interpreted, anomalies are confirmed through targeted log inspection, configurations are inspected via tool-gated access, and minimal configuration changes are proposed only after explicit operator approval. The architecture enforces a strict separation between reasoning and actuation, ensuring auditability and operational safety. The system is implemented and demonstrated on a real O-RAN testbed using a reproducible ping-pong handover scenario, illustrating how large language models can function as reasoning co-pilots that transform raw RAN telemetry into structured explanations and controlled remediation workflows, complementing existing analytics-only approaches in the NonRT RIC.

cs.NI↗

arXiv2025

Enabling On-demand Guaranteed QoS for Real Time Video Streaming from Vehicles in 5G Advanced with CAPIF & NEF APIs

This paper presents the design and implementation of a Proof of Concept (PoC) that demonstrates how 5G Advanced Network Functions can be integrated with the Common API Framework (CAPIF) to support enhanced connectivity for automotive applications. The PoC shows the continuous monitoring of the mobile network performance and the on-demand and dynamic adaptation of Quality of Service (QoS) for selected 5G User Equipment (UE) video streaming traffic flows using standard 3GPP Network Exposure Function (NEF) APIs exposed via CAPIF. Moreover, traffic flows are redirected to the edge to improve latency and optimize network resource utilization.

cs.NI↗

arXiv2025

Partially Reflected Surface (PRS)-Loaded Graphene-Based Patch Antenna for 6G

This work investigates a slotted patch antenna integrated with a partially reflected surface (PRS) to operate in the TeraHertz (THz) frequency range for 6G. The antenna is based on graphene material, on a Rogers RT Duroid 6010 substrate. The proposed antenna achieves a bandwidth of 70 GHz (750 GHz to 820 GHz). The PRS sheet consists of 5x4 unit cells, which are optimised to enhance the overall realized gain of the antenna. The overall realized gain has increased by 1.07 dBi. Also, the PRS enhanced the antenna radiation pattern, showing stable properties over the operating bandwidth. The improved antenna performance is validated via simulations.

eess.SP↗

arXiv2025

Power Allocation and RIS Elements Optimisation for Reconfigurable Intelligent Surfaces assisted RSMA

This paper proposes power allocation and the number of reconfigurable intelligent surfaces (RIS) elements optimisation in a RIS-assisted rate splitting multiple access (RSMA) system. The optimised RIS-RSMA (ORIS-RSMA) method determines the optimal number of RIS elements and the power allocation factors for both common and private parts of a message. Additionally, it maximises the sum rate while ensuring that a target common rate is satisfied. The performance of the proposed ORIS-RSMA is compared to that of the conventional RIS-RSMA and RSMA. Simulation results show that ORIS-RSMA achieves a higher sum rate.

eess.SP↗

arXiv2023

Smart Energy Management with Optimized Prosumerism for Achieving Dynamic Net-Zero Balance in Electrified Road Transport Networks

The increasing number of Electric Vehicles (EVs) have led to rising energy demands which aggregates the burden on grid supply. A few solutions have been proposed to reduce grid load, for example, using storage systems for storing surplus energy from EVs or time-scheduling supply. These solutions are costly and limited to specific regions and times. This paper proposes a smart energy management solution for a massively electrified road transport network. It comprises of energy supplies from grid, charging stations, renewable sources and EVs connected by 5G-enabled aggregators. We propose EVs as prosumers, which are energy consumers but also supply back their surplus energy via Vehicle-to-Grid (V2G) technology. We use machine learning to forecast hourly energy output from renewable sources, surplus supply from EVs and their demands. A grid cost minimization using Mixed Integer Linear Programming solution is proposed to dynamically alter supply according to demand and energy provision from EVs. The upper bounds of surplus supply and demand of EVs are theoretically derived. An incentive distribution mechanism is presented to reward EVs offering their surplus supply and to discourage them to become selfish which is analyzed using Prisoner's dilemma game. The paper also presents an optimum number of charging stations on a road. Simulation results show that the proposed solution can effectively meet the demand requirements even if the supply from grid is limited, and can averagely reduce 38.21% of grid load. It results in 5.3% of average cost reduction compared with optimization without prosumers. The proposed penalty charge for CO2 emissions results in over 50% cost reduction by using renewable resources in the proposed solution as compared to fossil fuels. The communication and computation complexity of the proposed solution is reduced by 5G-enabled aggregator.

eess.SY↗

arXiv2021

Transformation from 5G for Verticals Towards a 6G-enabled Internet of Verticals

5G will enable and greatly accelerate digital transformation of vertical sectors. In the longer term this will evolve from per-vertical connectivity services in 5G to the emergence of the 6G-enabled Internet of Verticals (6G-IoV). In this paper we describe and examine enabling technologies for this further transformation of verticals and examine some examples of 6G-IoV, next generation cloud manufacturing and manufacturing as a service, next generation smart energy grids and the Internet of Robotics (IoR).

cs.NI↗

arXiv2020

Secure Non-public Health Enterprise Networks

Increasing demand for secure remote operation in industry and technology advancements to support delivering efficient services and tele-mentoring have opened a new market in healthcare sector and emergency services based on 5G and Tactile internet capabilities. In a connected world, hospitals would benefit from providing the on-time availability either for continuous health monitoring or critical services to the citizens in need. In this paper, we propose a secure non-public health enterprise network concept to enable an end-to-end secure and location-agnostic communication between a patient and a healthcare service provider, and other contacts with patients consent either in case of an emergency or to be stored in the medical records. We present how applying non-public enterprise networks can address market demand in health care sector for improved end-to-end security and privacy when dealing with personal and critical information. We present the three tier architecture model describing continuous authentication mechanisms based on biometric collection as well as the dynamic network solutions in the healthcare domain. The biometric collection can be done using ambient or IoT sensors as well as wearable or implantable devices to monitor the patient unobtrusively. Furthermore, end-to-end security solutions should adapt dynamically based on the user profile and situation awareness to address the required level of security at the network side. We discuss the related research challenges for developing the presented non-public health enterprise platform and provide suggestions for future work based on the healthcare sector requirements and opportunities.

cs.NI↗

arXiv2019

An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels

Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different levels from weak to very strong interferences based on a coupling parameter α, a DL neural network (NN) based autoencoder is designed to train the data set and decode the received signals. The performance such a DL autoencoder for different interference scenarios are studied, with α known or partially known, where we assume that α is predictable but with a varying up to 10\% at the training stage. The results demonstrate that DL based approach has a significant capability to mitigate the effect induced by a poor signal-to-noise ratio (SNR) and a high interference-to-noise ratio (INR). However, the enhancement depends on the knowledge of α as well as the interference levels. The proposed DL approach performs well with α up to 10\% offset for weak interference level. For strong and very strong interference channel, the offset of α needs to be constrained to less than 5\% and 2\%, respectively, to maintain similar performance as α is known.

cs.LG↗

arXiv2019

Reconfigurable Liquid Crystal Reflectarray Metasurface for THz Communications

We present computational studies on a proposed 20 by 20 elements electronically reconfigurable liquid crystal (LC) based binary phase reflectarray metasurface, operational at 108 GHz. LC was modelled after Mer's GT3-23001, and full wave simulations have shown a phase difference of 177 degrees between ON and OFF states, while reflection amplitudes were both 0.88 for ON and OFF. We present preliminary full wave simulation results on the Genetic Algorithm (GA) optimised far-fields. We also present the basic design procedures and cross-platform implementations on optimisation routines involving Matlab, CST and VBA environments.

physics.app-ph↗

arXiv2019

Modelling the Impact of Organization Structure and Whistle Blowers on Intra-Organizational Corruption Contagion

We complement the rich conceptual work on organizational corruption by quantitatively modelling the spread of corruption within organizations. We systematically vary four organizational culture-related parameters, i.e., organization structure, location of bad apple, employees propensity to become corrupted (corruption probability), and number of whistle-blowers. Our simulation studies find that in organizations with flatter structures, corruption permeates the organization at a lower threshold value of corruption probability compared to those with taller structures. However, the final proportion of corrupted individuals is higher in the latter as compared to the former. Also, we find that for a 1,000 strong organization, 5 percent of the workforce is a critical threshold in terms of the number of whistle-blowers needed to constrain the spread of corruption, and if this number is around 25 percent, the corruption contagion is negligible. Implications of our results are discussed.

physics.soc-ph↗

arXiv2019

Opportunities and Enabling Technologies for 5G Spectrum Sharing

In this paper an overview is given of the current status of 5G industry standards, spectrum allocation and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G and the underlying technologies to enable efficient sharing, considering both licensed and unlicensed scenarios and spectrum both below 6 GHz and in the millimeter-wave frequency range

cs.NI↗

arXiv2018

Interference Management via Space and Frequency Domain Resource Partitioning

One key requirement for the fifth-Generation (5G) mobile system is the enhancement of cell edge user performance to ensure that every user is supported with consistent experience anywhere in the network. An active interference design to improve anywhere performance, particularly in the low SINR regime, can be achieved by applying a recently proposed new type of modulation scheme, frequency quadrature-amplitude modulation (FQAM), which could change the distribution of interference and therefore improve channel capacity. In this paper, a resource partitioning scheme to support FQAM in interference intensive scenarios is proposed. The proposed scheme slices radio resources into orthogonal parts for QAM and FQAM, respectively, along two different resource dimensions, namely, space and frequency. This can be achieved by incorporating advanced beamforming algorithms, e.g., full-dimension (FD-MIMO), and performing a frequency-based split of FQAM resources to effectively improve the data rate of the edge users experiencing heavy interference. Two resource partitioning mechanisms are proposed followed by complexity analysis. Evaluation results on both space and frequency-based FQAM interference management are presented, showing significant performance improvements when compared to the regular QAM modulation.

cs.IT↗

arXiv2018

Single cell and multi-cell performance analysis of OFDM index modulation

This paper addresses the achievable rate of single cell and sum rate of multi-cell orthogonal frequency division multiplexing (OFDM) index modulation (IM). The single cell achievable rate of OFDM-IM with Gaussian input is calculated using a multi-ary symmetric channel. Then, the cumulative distribution function (CDF) of multi-cell OFDM-IM is investigated by stochastic geometry. Furthermore, it is proved in this paper that the probability density function (PDF) of noise plus intercell-interference (ICI) in multi-cell OFDM-IM with quadrature amplitude modulation (QAM) follows a mixture of Gaussians (MoG) distribution. Next, parameters of the MoG distribution are estimated using a simplified expectation maximization (EM) algorithm. Upper bound of sum rates of multi-cell OFDM-IM is derived. Furthermore, analytic and simulated results are compared and discussed.

cs.IT↗

arXiv2018

Frequency and Quadrature Amplitude Modulation for 5G Networks

The emerging fifth generation (5G) wireless access network, aiming at providing ubiquitous and high data rate connectivity, is envisaged to deploy large number of base stations with higher density and smaller sizes, where inter-cell interference (ICI) becomes a critical problem. Frequency quadrature amplitude modulation (FQAM) has been shown to reduce the ICI at the cell edge therefore achieve a higher transmission rate for cell edge users. This paper investigates the detection of FQAM symbols and noise plus ICI in a multi-cell FQAM communication network. Turbo-coded bit error rate (BER) and frame error rate (FER) of multi-cell FQAM are studied. Also, the cumulative distribution function (CDF) of signal to noise plus interference (SINR) of multi-cell FQAM is computed using stochastic geometry. It is demonstrated via simulations FQAM outperforms quadrature amplitude modulation (QAM) in BER and FER when ICI is significant. Furthermore, FQAM can achieve better SINR than QAM.

cs.IT↗

arXiv2017

5G Spectrum Sharing

In this paper an overview is given of the current status of 5G industry standards, spectrum allocation and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G using cognitive radio techniques, considering both licensed and unlicensed scenarios. A particular attention is given to sharing millimeter-wave frequencies, which are of prominent importance for 5G.

cs.NI↗

arXiv2016

Intra-cluster Characteristics of 28 GHz Wireless Channel in Urban Micro Street Canyon

This paper investigates intra-cluster channel characteristics of non line-of-sight (NLOS) 28 GHz channels in street canyon scenarios. These channel characteristics include cluster numbers, number of subpaths within each cluster, intra-cluster delay spreads, and intra-cluster angular spreads. Both measurement and ray tracing results are presented and compared. Furthermore, distribution fittings are performed and models and parameters for different intra-cluster channel characteristics are proposed.

cs.IT↗

arXiv2016

Distributed Beam Scheduling for Multi-RAT Coexistence in mm-Wave 5G Networks

Millimetre-wave communication (licensed or unlicensed) is envisaged to be an important part of the fifth generation (5G) multi-RAT ecosystem. In this paper, we consider the spectrum bands shared by 5G cellular base stations and some existing networks, such as WiGig. Sharing the same band among such multiple radio access technologies (RATs) is very challenging due to the lack of centralized coordination and demands novel and efficient interference mitigation and coexistence mechanisms to reduce the mutual interference. To address this important challenge, we propose in this paper a novel multi-RAT coexistence mechanism where neighbouring 5G and WiGig base stations, each serving their own associated UEs, schedule their beam configurations in a distributed manner such that their own utility function, e.g. spectral efficiency, is maximized. We formulate the problem as a combinatorial optimization problem and show via simulations that our proposed distributed algorithms yield a comparable spectral efficiency for the entire networks as that using an exhaustive search, which requires global coordination among coexisting RATs and also has a much higher algorithmic complexity.

cs.IT↗