arXiv subjects

Alister Burr

Publications and source records attributed to Alister Burr.

At least 19 recordsLinked to original sources

arXiv2025

CSI Compression Beyond Latents: End-to-End Hybrid Attention-CNN Networks with Entropy Regularization

Massive MIMO systems rely on accurate Channel State Information (CSI) feedback to enable high-gain beam-forming. However, the feedback overhead scales linearly with the number of antennas, presenting a major bottleneck. While recent deep learning methods have improved CSI compression, most overlook the impact of quantization and entropy coding, limiting their practical deployability. In this work, we propose an end-to-end CSI compression framework that integrates a Spatial Correlation-Guided Attention Mechanism with quantization and entropy-aware training. Our model effectively exploits the spatial correlation among the antennas, thereby learning compact, entropy-optimized latent representations for efficient coding. This reduces the required feedback bitrates without sacrificing reconstruction accuracy, thereby yielding a superior rate-distortion trade-off. Experiments show that our method surpasses existing end-to-end CSI compression schemes, exceeding benchmark performance by an average of 21.5% on indoor datasets and 18.9% on outdoor datasets. The proposed framework results in a practical and efficient CSI feedback scheme.

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arXiv2025

Lightweight Graph Neural Networks for Enhanced 5G NR Channel Estimation

Effective channel estimation CE is critical for optimizing the performance of 5G New Radio NR systems particularly in dynamic environments where traditional methods struggle with complexity and adaptability This paper introduces GraphNet a novel lightweight Graph Neural Network GNNbased estimator designed to enhance CE in 5G NR Our proposed method utilizes a GNN architecture that minimizes computational overhead while capturing essential features necessary for accurate CE We evaluate GraphNet across various channel conditions from slowvarying to highly dynamic environments and compare its performance to ChannelNet a wellknown deep learningbased CE method GraphNet not only matches ChannelNets performance in stable conditions but significantly outperforms it in highvariation scenarios particularly in terms of Block Error Rate It also includes builtin noise estimation that enhances robustness in challenging channel conditions Furthermore its significantly lighter computational footprint makes GraphNet highly suitable for realtime deployment especially on edge devices with limited computational resources By underscoring the potential of GNNs to transform CE processes GraphNet offers a scalable and robust solution that aligns with the evolving demands of 5G technologies highlighting its efficiency and performance as a nextgeneration solution for wireless communication systems

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arXiv2025

Pilot and Data Power Control for Uplink Cell-free massive MIMO

This paper introduces a novel iterative algorithm for optimizing pilot and data power control (PC) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems, aiming to enhance system performance under real-time channel conditions. The approach begins by deriving the signal-to-interference-plus-noise ratio (SINR) using a matched filtering receiver and formulating a min-max optimization problem to minimize the normalized mean square error (NMSE). Utilizing McCormick relaxation, the algorithm adjusts pilot power dynamically, ensuring efficient channel estimation. A subsequent max-min optimization problem allocates data power, balancing fairness and efficiency. The iterative process refines pilot and data power allocations based on updated channel state information (CSI) and NMSE results, optimizing spectral efficiency. By leveraging geometric programming (GP) for data power allocation, the proposed method achieves a robust trade-off between simplicity and performance, significantly improving system capacity and fairness. The simulation results demonstrate that dynamic adjustment of both pilot and data PC substantially enhances overall spectral efficiency and fairness, outperforming the existing schemes in the literature.

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arXiv2025

Testbed Development: An Intelligent O-RAN based Cell-Free MIMO Network

Cell-free multiple input multiple output (CF-MIMO) systems improve spectral and energy efficiencies using distributed access points (APs) to provide reliable service across an area equivalent to multiple conventional cells. This paper presents a novel design and implementation of a CF-MIMO network leveraging the open radio access network (O-RAN) architecture based testbed to enhance the performance of interference-prone user. The proposed prototype is developed based on open source software components and unlike many other prototypes, our testbed is able to serve commercial 5G user equipment (UE). The RAN intelligent controller (RIC) allows the cell-free (CF) network to access the embedded artificial intelligence and benefit from the network optimisation techniques that O-RAN brings. The testbed includes an intelligent antenna association xApp which determines the antenna group that serves each UE based on the live key performance measurements. The paper demonstrates the deployment and operation of the CF network and the xApp and discusses how the CF networks can benefit from the O-RAN architecture.

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arXiv2024

COST INTERACT Whitepaper on Signal Processing for Communications, Localization, and Intergrated Sensing and Communication

The upcoming next generation of wireless communication is anticipated to revolutionize the conventional functionalities of the network by adding sensing and localization capabilities, low-power communication, wireless brain computer interactions, massive robotics and autonomous systems connection. Furthermore, the key performance indicators expected for the 6G of mobile communications promise challenging operating conditions, such as user data rates of 1 Tbps, end-to-end latency of less than 1 ms, and vehicle speeds of 1000 km per hour. This evolution needs new techniques, not only to improve communications, but also to provide localization and sensing with an efficient use of the radio resources. The goal of INTERACT Working Group 2 is to design novel physical layer technologies that can meet these KPI, by combining the data information from statistical learning with the theoretical knowledge of the transmitted signal structure. Waveforms and coding, advanced multiple-input multiple-output and all the required signal processing, in sub-6-GHz, millimeter-wave bands and upper-mid-band, are considered while aiming at designing these new communications, positioning and localization techniques. This White Paper summarizes our main approaches and contributions.

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arXiv2024

Performance Analysis of Traditional and Network Coded Transmission in Infrastructure-less Multi-hop Wireless Networks

Infrastructure-less Multi-hop Wireless Networks are the backbone for mission critical communications such as in disaster and battlefield scenarios. However, interference signals in the wireless channel cause losses to transmission in wireless networks resulting in a reduced network throughput and making efficient transmission very challenging. Therefore, techniques to overcome interference and increase transmission efficiency have been a hot area of research for decades. In this paper two methods for transmitting data through infrastructure-less multi hop wireless networks, Traditional (TR) and Network Coded (NC) transmission are thoroughly examined for scenarios having one or two communication streams in a network. The study has developed network models in MATLAB for each transmission technique and scenario. The simulation results showed that the NC transmission method yielded a better throughput under the same network settings and physical interference. Furthermore, the impact of increasing numbers of hops between source and destination on the network capacity and the communications latency was also observed and conclusions were drawn.

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arXiv2024

Bussgang revisited: effect of quantization on signal to distortion plus noise ratio with non-Gaussian signals

Quantization plays an important role in the physical layer (PHY) disaggregation which is fundamental to the Open Radio Access Network (O-RAN) architecture, since digitized signals must be transmitted over fronthaul connections. In this paper we explore the effect of quantization on PHY performance, drawing on the Bussgang decomposition and the implications of the Bussgang theorem and extending it to the case of non-Gaussian signals. We first prove several theorems regarding the signal to distortion plus noise ratio for a general non-linearity, applicable to both the Gaussian and the non-Gaussian case, showing that the decomposition can be applied to the non-Gaussian case, but that formulae previously introduced should be amended. We then apply these results to the non-linearity created by quantization, both for Gaussian and non-Gaussian signal distributions, and give numerical results derived from both theory and simulation.

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arXiv2023

Ultra-dense Radio Access Networks for Smart Cities: Cloud-RAN, Fog-RAN and "cell-free" Massive MIMO

In this paper we discuss the requirements for a radio access network architecture for ultra-dense networks for "smart city" applications, and show that coordination is required between access points to overcome the effects of interference. We propose a new paradigm, Fog Massive MIMO, based on a combination of the "cell-free" massive MIMO concept and the Fog Radio Access Network (F-RAN). In particular we analyze the potential benefit of improved coordination between APs over different coordination ranges.

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arXiv2019

On The Uplink Throughput of Zero-Forcing in Cell-Free Massive MIMO with Coarse Quantization

The recently proposed Cell-Free massive MIMO architecture is studied for the uplink. In contrast to most previous works, joint detection is performed using global CSI. Therefore, we study strategies for transferring CSI to the CPU taking into account the fronthaul capacity which limits CSI quantization. Two strategies for pilot-based CSI acquisition are considered: estimate-and-quantize and quantize-and-estimate. These are analysed using the Bussgang decomposition. For a given quantization constraint for the data and CSI the achievable rate per user with Zero-Forcing is determined. Numerical results show that quantize-and-estimate (the simpler strategy) is similar to or better than estimate-and-quantize, especially for 1-bit resolution.

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arXiv2019

Few-Bit CSI Acquisition for Centralized Cell-Free Massive MIMO with Spatial Correlation

The availability and accuracy of Channel State Information (CSI) play a crucial role for coherent detection in almost every communication system. Particularly in the recently proposed cell-free massive MIMO system, in which a large number of distributed Access Points (APs) is connected to a Central processing Unit (CPU) for joint decoding, acquiring CSI at the CPU may improve performance through the use of detection algorithms such as minimum mean square error (MMSE) or zero forcing (ZF). There are also significant challenges, especially the increase in fronthaul load arising from the transfer of high precision CSI, with the resulting complexity and scalability issues. In this paper, we address these CSI acquisition problems by utilizing vector quantization with precision of only a few bits and we show that the accuracy of the channel estimate at the CPU can be increased by exploiting the spatial correlation subject to this limited fronthaul load. Further, we derive an estimator for the simple \emph{Quantize-and-Estimate} (QE) strategy based on the Bussgang theorem and compare its performance to \emph{Estimate-and-Quantize} (EQ) in terms of Mean Squared Error (MSE). Our simulation results indicate that the QE with few-bit vector quantization can outperform EQ and individual scalar quantization at moderate SNR for small numbers of bits per dimension.

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arXiv2018

Implementation of Uplink Network Coded Modulation for Two-Hop Networks

With the fast growing number of wireless devices and demand of user data, the backhaul load becomes a bottleneck in wireless networks. Physical layer network coding (PNC) allows Access Points (APs) to relay compressed, network coded user data, therefore reducing the backhaul traffic. In this paper, an implementation of uplink Network Coded Modulation (NetCoM) with PNC is presented. A 5-node prototype NetCoM system is established using Universal Software Radio Peripherals (USRPs) and a practical PNC scheme designed for binary systems is utilised. An orthogonal frequency division multiplexing (OFDM) waveform implementation and the practical challenges (e.g. device synchronisation and clock drift) of applying OFDM to NetCoM are discussed. To the best of our knowledge this is the first PNC implementation in an uplink scenario in radio access networks and our prototype provides an industrially-applicable implementation of the proposed NetCoM with PNC approach.

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arXiv2018

Cooperative access networks: Optimum fronthaul quantization in distributed Massive MIMO and cloud RAN

We consider cooperative radio access network architectures, especially distributed massive MIMO and Cloud RAN, considering their similarities and differences. We address in particular the major challenge posed to both by the implementation of a high capacity fronthaul network to link the distributed access points to the central processing unit, and consider the effect on uplink performance of quantization of received signals in order to limit fronthaul load. We use the Bussgang decomposition along with a new approach to MMSE estimation of both channel and data to provide the basis of our analysis.

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arXiv2017

Secure Energy Efficiency Optimization for MISO Cognitive Radio Network with Energy Harvesting

This paper investigates a secure energy efficiency (SEE) optimization problem in a multiple-input single-output (MISO) underlay cognitive radio (CR) network. In particular, a multi-antenna secondary transmitter (SU-Tx) simultaneously sends secured information and energy to a secondary receiver (SU-Rx) and an energy receiver (ER), respectively, in the presence of a primary receiver (PU-Rx). It is assumed that the SU-Rx, ER and PU-Rx are each equipped with a single antenna. In addition, the SU-Tx should satisfy constraints on maximum interference leakage to the PU-Rx and minimum harvested energy at the ER. In this CR network, we consider the transmit covariance matrix design with the assumption of perfect channel state information (CSI) at the SU-Tx. In addition, it is assumed that the ER is a potential passive eavesdropper due to broadcast nature of wireless transmission. On the other hand, we consider the worst-case scenario that ER's energy harvesting requirement is only satisfied when it performs only energy harvesting without intercepting or eavesdropping information intended for the SU-Rx. We formulate this transmit covariance matrix design as a SEE maximization problem which is a non-convex problem due the non-linear fractional objective function. To realize the solution for this non-convex problem, we utilize the non-linear fractional programming and difference of concave (DC) functions approaches to reformulate into a tractable form. Based on these techniques and the Dinkelbach's method, we propose iterative algorithms to determine the solution for the original SEE maximization problem. Numerical simulation results are provided to demonstrate the performance of the proposed transmit covariance matrix design and convergence of the proposed algorithms.

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arXiv2017

Low Complexity Coefficient Selection Algorithms for Compute-and-Forward

Compute-and-Forward (C&F) has been proposed as an efficient strategy to reduce the backhaul load for the distributed antenna systems. Finding the optimal coefficients in C&F has commonly been treated as a shortest vector problem (SVP), which is N-P hard. The point of our work and of Sahraei's recent work is that the C&F coefficient problem can be much simpler. Due to the special structure of C&F, some low polynomial complexity optimal algorithms have recently been developed. However these methods can be applied to real valued channels and integer based lattices only. In this paper, we consider the complex valued channel with complex integer based lattices. For the first time, we propose a low polynomial complexity algorithm to find the optimal solution for the complex scenario. Then we propose a simple linear search algorithm which is conceptually suboptimal, however numerical results show that the performance degradation is negligible compared to the optimal method. Both algorithms are suitable for lattices over any algebraic integers, and significantly outperform the lattice reduction algorithm. The complexity of both algorithms are investigated both theoretically and numerically. The results show that our proposed algorithms achieve better performance-complexity trade-offs compared to the existing algorithms.

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arXiv2016

Compute-and-Forward in Cell-Free Massive MIMO: Great Performance with Low Backhaul Load

In this paper, we consider the uplink of cell-free massive MIMO systems, where a large number of distributed single antenna access points (APs) serve a much smaller number of users simultaneously via limited backhaul. For the first time, we investigate the performance of compute-and-forward (C&F) in such an ultra dense network with a realistic channel model (including fading, pathloss and shadowing). By utilising the characteristic of pathloss, a low complexity coefficient selection algorithm for C\&F is proposed. We also give a greedy AP selection method for message recovery. Additionally, we compare the performance of C&F to some other promising linear strategies for distributed massive MIMO, such as small cells (SC) and maximum ratio combining (MRC). Numerical results reveal that C&F not only reduces the backhaul load, but also significantly increases the system throughput for the symmetric scenario.

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arXiv2016

Secrecy Rate Maximization for MISO Multicasting SWIPT System with Power Splitting Scheme

This paper considers transmit covariance matrix design for secrecy rate maximization problem in a multiple-input single-output (MISO) multicasting simultaneous wireless information and power transfer (SWIPT) system. In order to enhance the performance of the system, artificial noise (AN) is added to the transmit signal in the design for the following purposes: to reduce the received signal-to-noise ratio (SNR) at the eavesdroppers and increase the harvested energy. We assume that all the channel-state-information (CSI) is perfectly known at the transmitter and all legitimate users are capable of simultaneously receiving information and harvesting energy. In addition, all the eavesdroppers are passive and they can harvest energy only when they are not intercepting or eavesdropping the messages intended for the legitimate users. The original secrecy rate maximization problem is not convex in terms of transmit and artificial covariance matrices as well as the power splitting (PS) ratio. In order to circumvent this non-convexity issue, we exploit the \emph{Charnes-Cooper} Transformation and semidefinite relaxation (SDR) to convert this original problem into a convex one. However, this convex problem does not always yield the rank-one transmit and AN covariance matrices to obtain the solution of the original problem. Therefore, we analyze the optimal conditions and utilize a Gaussian randomization (GR) method to construct the rank-one solutions from the non-rank one results. Simulation results have been provided to demonstrate the performance of the proposed transmit covariance matrices design for MISO multicasting SWIPT system.

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arXiv2015

A Multilevel Framework for Lattice Network Coding

We present a general framework for studying the multilevel structure of lattice network coding (LNC), which serves as the theoretical fundamental for solving the ring-based LNC problem in practice, with greatly reduced decoding complexity. Building on the framework developed, we propose a novel lattice-based network coding solution, termed layered integer forcing (LIF), which applies to any lattices having multilevel structure. The theoretic foundations of the developed multilevel framework lead to a new general lattice construction approach, the elementary divisor construction (EDC), which shows its strength in improving the overall rate over multiple access channels (MAC) with low computational cost. We prove that the EDC lattices subsume the traditional complex construction approaches. Then a soft detector is developed for lattice network relaying, based on the multilevel structure of EDC. This makes it possible to employ iterative decoding in lattice network coding, and simulation results show the large potential of using iterative multistage decoding to approach the capacity.

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arXiv2015

Statistical Analysis of Multi-Antenna Relay Systems and Power Allocation Algorithms in a Relay with Partial Channel State Information

The performance of a dual-hop MIMO relay network is studied in this paper. The relay is assumed to have access to the statistical channel state information of its preceding and following channels and it is assumed that fading at the antennas of the relay is correlated. The cumulative density function (cdf) of the received SNR at the destination is first studied and closed-form expressions are derived for the asymptotic cases of the fully-correlated and non-correlated scenarios; moreover, the statistical characteristics of the SNR are further studied and an approximate cdf of the SNR is derived for arbitrary correlation. The cdf is a multipartite function which does not easily lend itself to further mathematical calculations, e.g., rate optimization. However, we use it to propose a simple power allocation algorithm which we call "proportional power allocation". The algorithm is explained in detail for the case of two antennas and three antennas at the relay and the extension of the algorithm to a relay with an arbitrary number of the antennas is discussed. Although the proposed method is not claimed to be optimal, the result is indistinguishable from the benchmark obtained using exhaustive search. The simplicity of the algorithm combined with its precision is indeed attractive from the practical point of view.

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