This paper presents the design and implementation of a reconfigurable wide-band spectrum-sensing platform developed within the Open Network Shared Spectrum Innovation and Design Environment (ON-SIDE) project. Most existing implementations lack integration between modern SDR wide-band capture and reconfigurable cognitive spectrum management. The proposed platform addresses this by implementing a fully custom real-time wide-band monitoring directly on AMD Zynq UltraScale+ RFSoC 670 DFE hardware, targeting the 3.8–4.2 GHz shared-access band in the UK. The system monitors up to 400 MHz of instantaneous bandwidth, tunable from 0 to 4.9 GHz. The 400 MHz span was selected to cover the full n77 shared-access band (3.8 - 4.2 GHz) allocated by Ofcom, while the 3.84 MHz per-channel resolution aligns with the smallest 5G NR OFDM sub-carrier spacing. A polyphase channeliser and energy detection pipeline implemented on the FPGA fabric provides 106 parallel channel outputs, enabling real-time energy monitoring. An embedded PYNQ-based control dashboard allows configuration and live visualisation, and a Python ZeroMQ publisher-subscriber framework distributes sensing data to multiple remote clients. Field trials on live 5G NR signals in the n77 and n78 bands verified accurate, low-latency energy detection and remote operation. The platform demonstrated an efficient and reconfigurable hardware foundation for future cognitive radio and dynamic spectrum management applications.

As demand for wireless connectivity increases across various applications, optimizing the available radio spectrum has become crucial. In certain situations, traditional static license allocation often leads to spectrum shortages. This study presents a dynamic approach to assigning spectrum and power resources to requesting nodes based on real-time demand and their unique properties. A dynamic shared-spectrum environment is created to train a central Deep Reinforcement Learning (DRL) agent, referred to as the Resource Management Model (RMM). The RMM jointly optimizes and assigns limited spectrum and transmit power resources to continuously changing located base stations/nodes, based on node specifications. The model effectively manages interference in accordance with IEEE 802.19 principles, achieving an average assignment rate of 98%, on par with exclusive allocation techniques. It outperformed random, recursive, and two-stage Q-learning algorithms in providing satisfactory quality of service (QoS) to requesting nodes. Similarly, the RMM’s spectrum reuse improved by 20% over traditional exclusive-sharing methods. However, the percentage improvement declined as the number of shared bands and nodes increased.

G New Radio (NR) aims to provide a technological solution to the growing demand for faster data rates and lower latency in mobile communications through its improved features and flexibility; however, this comes at the cost of increased design complexity when targeting hardware devices. To address this, high-level design tools such as Simulink can be used with iterative model-based design to shorten development cycles and reduce human error. This paper demonstrates this design flow through the implementation of a hardware-compatible NR Physical Uplink Shared Channel (PUSCH) model, targeting the Zynq UltraScale+ Radio Frequency System-on-Chip (RFSoC) ZCU216 development board. By interfacing with the hardware through MATLAB, the design supports multiple symbol modulation schemes, and parameters such as the number of layers, antenna ports, and selected precoding matrix can be altered. The design was tested by targeting a 10 MHz bandwidth, 60 kHz subcarrier spacing (SCS) waveform with 132 active subcarriers and met timing with 0.276 ns Worst Negative Slack (WNS) for a 245.76 MHz clock frequency, demonstrating its standard compliancy. The Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) modulated signal generated on the board was looped back to MATLAB and verified against an existing software implementation, and the hardware usage was recorded.

This paper presents a flexible FPGA digital beamforming architecture to steer an array for wideband radio frequency signals. The architecture combines True Time-Delay (TTD) units and phase-shifting to beamform at digital baseband. The TTDs utilize low order, coefficient-symmetric Farrow structures to rapidly and flexibly adjust fractional sample delays applied to wideband signals with minimal FPGA resources. Bandpass sampling is employed to further reduce resource and power consumption. The designed Farrow structure’s group delay and magnitude characteristics are evaluated. Measured beam patterns are demonstrated through simulation of the proposed FPGA receive array using fixed-point arithmetic. The multiplier utilization of the proposed system is estimated and compared with the literature. Promising results open discussions to hardening Farrow structure cores on FPGAs to serve multiple signal processing techniques required in future communications systems without consuming programmable logic fabric.

Recently, FPGAs have been coupled with processors to form System on Chip (SoC) devices. SoC design is challenging as it combines both hardware and software elements. A variety of tools must therefore be utilised to design for these components, each requiring different knowledge.

This demonstration will present a single chip spectrum analyser that has been developed using the Xilinx Zynq Radio Frequency System on Chip (RFSoC), and the PYNQ software framework. The design uses the RFSoC’s high speed Radio Frequency Analogue to Digital Converters (RF ADCs) to capture 2.048 GHz of instantaneous bandwidth and directly sample signals at frequencies up to 4.096 GHz using higher order Nyquist Zone techniques. All signal processing, software control, and graphical user interface functions are hosted entirely on the same RFSoC chip. As well as presenting the functionality achieved by the spectrum analyser, we will describe its underlying architecture and demonstrate its use in exploring the radio spectrum by showing examples of ambient signals.

Floating point Convolutional Neural Networks(CNNs) are computationally expensive and deeper networks can be impractical to deploy on FPGAs - consuming a large number of resources and power, as well as having lengthy development times. Previous work has shown that CNNs can be quantised heavily using fixed point arithmetic to combat this without significant loss in classification accuracy. We aim to quantize an existing CNN architecture for radio modulation classification to 2-bit weights and activations, while retaining a level of accuracy close to the original paper, for deployment on a Zynq System on Chip (SoC). To improve the development time for hardware synthesisable CNNs, we make use of MATLAB System Objects and HDL Coder. The PYNQ framework is presented as a practical means for accessing the functionality of the CNN. Our preliminary results show a high classification accuracy even with 2-bit weights and activations.

Due to its wide application range and attractive features, wireless body area networks (WBANs) is considered as a revolutionary technology, which is envisaged to change how people manage and think about their health and their life styles. In this paper, we propose a self-organized dynamic clustering (SDC) method and its multiple access mechanism to mitigate the interference and improve the QoS in multiple WBANs environment. To the best of our knowledge, this is the first paper that focuses on the spectrum allocation for multiple WBANs. We borrow the concepts of cell and cluster from cellular networks to allocate the channels for different WBANs. The clustering is self-organized to improve the data transmission for intra-WBAN communication by the information exchange via inter-WBAN communication. Additionally, based on the cluster architecture, an inter-WBAN relaying (IWR) protocol for packets with low privacy or high reliability is also investigated. The simulation results show that SDC has better signal to interference ratio compared with existing framework. Besides, SDC and IWR also provide better QoS performance in terms of higher data packet delivery ratio and lower packet delay.

Adaptive radiotherapy is a technique intended to increase the accuracy of radiotherapy. Currently, it is not clinically feasible due to the time required to process the images of patient anatomy. Hardware acceleration of image processing algorithms may allow them to be carried out in a clinically acceptable timeframe. This paper presents the experiences encountered using high-level synthesis tools to design an accelerated segmentation algorithm for computed tomography images targeted for implementation on a System on Chip. Hardware coprocessors and their interfaces for optimal threshold generation and 3D mean filter algorithms were synthesised from C++ functions. Hardware acceleration significantly outperformed the software only implementation. The high-level synthesis tools allowed the rapid exploration of different design options. However, hardware design knowledge was still necessary in order to interpret the results effectively.

In this paper, we consider minimum-mean-square error (MMSE) training-based channel estimation for two-hop multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) relaying systems. The channel estimation process is divided into two main phases. The relay-destination channel is estimated in the first phase and can be obtained using well-known point-to-point MIMO OFDM estimation methods. In the second phase, the source-relay channel is estimated at the destination with the use of a known training sequence that is transmitted from the source and forwarded to the destination by a nonregenerative relay. To obtain an estimate of the source-relay channel, the source training sequence, relay precoder, and destination processor, require to be optimized. To solve this problem, we first derive an iterative algorithm that involves sequentially solving a number of convex optimization problems to update the source, relay, and destination design variables. Since the iterative algorithm may be too computationally expensive for practical implementation, we then derive simplified solutions that have reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms.

In recent years, the classic method of Coordinate Rotation by Digital Computer (CORDIC) arithmetic has been widely implemented as part of the computational requirements of the well known QR-RLS (Recursive Least Squares) algorithm. In order to operate Givens rotation on a complex number value system, double angle complex rotation (DACR) was adopted to simplify the computational requirement of Complex Givens Rotation. This paper presents a new architecture of high speed CORDIC based single Processor Element (PE) that can be used to accomplish the complex value QR update based RLS. The implementation results on Xilinx FPGA implementaton demonstrates that the proposed structure results in a lower latency and lower cost.

In this paper we consider the design of minimum mean square error (MMSE) transceivers for non-regenerative multiple input multiple output (MIMO) relay systems. Our design utilises Tomlinson Harashima precoding (THP) at the source along with linear processors in each stage of the network. Assuming full channel state information (CSI) is available at each node in the network the various processors are jointly optimised to minimise the system arithmetic mean square error (MSE) whilst abiding by average power constraints at both the source and relay terminals in the network. Simulations show that the proposed schemes outperform existing methods in terms of bit error ratio (BER).

Recently, the third generation partnership standards bodies (3GPP/3GPP2) have defined a two-dimensional channel model for multiple-input multiple-output (MIMO) systems, where the propagating plane waves are assumed to arrive only from the azimuthal direction and therefore not include the elevation domain. As a result of this assumption, the derived angle-of-arrival (AoA) distribution is characterised only by the azimuth direction of these waves. The AoA distribution of multipaths is implemented with a novel three-dimensional approach. The von Mises-Fisher (VMF) probability density function is used to describe their distribution within the propagation environment in both azimuth and co-latitude. More specifically, the proposed model uses a mixture of VMF distributions. A mixture can be composed of any number of clusters and this is clutter specific. The parameters of the individual cluster of scatterers within the mixture are derived and an estimation of those parameters is achieved using the spherical K-means algorithm and also the expectation maximisation algorithm. Statistical tests are provided to measure the goodness of fit of the proposed model. The results indicate that the proposed model fits well with MIMO experimental data obtained from a measurement campaign in Germany.

In this paper new expressions for the Spatial Fading Correlation (SFC) functions of Antenna Arrays (AA) in a 3-dimensional (3D) multipath channel are derived. In particular the Uniform Circular Array (UCA) antenna topology is considered. The derivation of the novel SFC function uses a Probability Density Function (PDF) originating from the field of directional statistics, the Von Mises Fisher (VMF) PDF. In particular the novel SFC function is based on the concept of mixture modeling and hence uses a mixture of VMF distributions. Since the SFC function is dependent on the Angle of Arrival (AoA) as well as the power of each cluster, the more appropriate power azimuth colatitude spectrum term has been used. The choice of distribution is validated with the use of Multiple Input Multiple Output (MIMO) experimental data that was obtained in an outdoor drive test campaign in Germany. A mixture can be composed of any number of clusters and this is mainly dependent on the clutter type encountered in the propagation environment. The parameters of the individual clusters within the mixture are derived and an estimation of those parameters is achieved using the soft-Expectation Maximization (EM) algorithm. The results indicate that the proposed model fits well with the MIMO data.

Recently, the Third Generation Partnership standards bodies (3GPP/3GPP2) have defined a channel model for multiple input multiple output (MIMO) systems. This model is a 2D model where the propagating plane waves are assumed to arrive only from the azimuthal direction and does not include the elevation domain. As a result of this assumption the derived angle of arrival (AoA) distribution is characterized only by the azimuth direction of these waves. In this paper the distribution of scatterers is modeled in a novel 3D approach. The Von Mises Fisher (VMF) probability density function (PDF) is used to describe their distribution within the propagation environment in azimuth and coelevation. As a result the AoA distribution of the incoming multipaths is characterized in the same way. More specifically the model proposed uses a mixture of VMF distributions. A mixture can be composed of any number of clusters and this is environment/clutter specific. The parameters of the individual clusters of scatterers within the mixture are derived and an estimation of those parameters is achieved using the soft-expectation maximization (EM) algorithm. The results indicate that the proposed model fits well with MIMO experimental data obtained from a measurement campaign in Germany. The measurements obtained from the RUSK channel sounder were post-processed using the RiMAX algorithm. The data was subsequently clusterized using the soft-EM for mixtures of VMF distributions.

This paper presents preliminary investigations of the short range (~10 cm) narrowband wireless channel appropriate to specks operating in the 2.4 GHz ISM band. Path loss measurements have been made in the laboratory using rectaxial antennas at 2.45 GHz. The transmitted signal is an unmodulated carrier and the receiver is a spectrum analyzer. Characterization of signal power variations are important for system design and so path loss and fading models have been derived. The paper presents a comprehensive narrowband channel model (including interference due to neighboring nodes) for application to asynchronous short-range wireless networks. Medium access is assumed to be CSMA based and SIR will therefore depend on inhibition distance and antenna characteristics. Aggregated interference due to neighboring nodes has been calculated. Finally, cumulative SIR values have been used to construct link budgets and bit error rates.

Many of today's electronic design automation (EDA) tools include intellectual property (IP) cores that are fully pipelined to increase data throughput. Using these cores to implement data paths that do not involve feedback can result in fast, efficient designs. However, if they are used within a feedback loop, this is not always the case. The paper examines the effects that using pipelined cores in feedback loops can have on a design. By considering two designs that implement a Givens rotation using feedback, which is used in QR decomposition (Haykin, S., 1990), it is shown that, even though a pipelined design can be clocked faster, its data throughput is less than a non-pipelined design. Also, the non-pipelined design is shown to be smaller and consumes less power. Finally, a suggestion for a more efficient use of pipelining in feedback loops is presented, based on channel interleaving (Parhi, K.K., 1999).

A new algorithm is presented that synthesises multiplier blocks with the goal of minimising FPGA hardware cost. Comparisons with existing algorithms are made via implementing synthesised blocks as the multiplication hardware of fully-pipelined, full-parallel transposed form FIR filters. Results establish that the classic optimisation goal of minimising adders does not minimise FPGA hardware. Instead, minimising multiplier block logic depth is shown to be the primary factor for low area FPGA implementation. Filters generated using the new algorithm are also shown to consume less FPGA area than equivalents implemented using the distributed arithmetic technique.

This paper proposes the use of a polynomial interpolator structure (based on Horner's scheme) which is efficiently realizable in hardware, for high-quality geometric transformation of two- and three-dimensional images. Polynomial-based interpolators such as cubic B-splines and optimal interpolators of shortest support are shown to be exactly implementable in the Horner structure framework. This structure suggests a hardware/software partition which can lead to efficient implementations for multidimensional interpolation.

In this paper we present a library for the rapid prototyping of adaptive signal processing algorithms, architectures and applications. The library is hosted by the DSP simulation software SystemView and covers virtually the complete spectrum of linear, and non-linear adaptive algorithms currently in use in contemporary DSP and communications applications. The library can be easily used with real signals, with variable system wordlengths, sampling frequencies and so on. Therefore in this paper we discuss the design philosophy behind the library and overview the various algorithms and applications that are implemented. The paper shows an implementation of an adaptive multiuser CDMA receiver/decision feedback equaliser (DFE) as an example of the relevance and rapid development that is possible. Copies of the library and example files can be downloaded from the Web following the instructions in the paper.

A teaching and evaluation tool for adaptive algorithms using the Java platform is presented. The tool has been developed for use in teaching adaptive signal processing and gives the students the facility to observe a comprehensive set of algorithms executing in the time, frequency and z-domain, vary any parameter and thereby augment the traditional learning process. Another key aim in the development was to provide a simple tool so that the feasibility of adaptive algorithms for a particular problem can be evaluated quickly. The Java platform has been chosen for this task since it is possible to run the tool on any computer system (e.g. Unix, Windows, Linux) using a Java virtual machine via the World Wide Web.

The paper describes a residual analysis-synthesis system which exploits the human perception mechanism on temporal varying signals using Zwicker's (1990) three dimensional excitation-critical-bandrate time pattern as the framework. Temporal information is retrieved using a linear predictive analysis on the discrete cosine transformed signal and critical band intensity information is obtained by using non-uniform filter banks. The system is characterized by high frequency resolution and good time resolution. Novel phase prediction and phase correction techniques are employed to eliminate any boundary discontinuities between two time frames. Experimental results illustrate that high quality residual signals can be reproduced using a few parameters regardless of the temporal characteristics of the signal.

A new multibit multiplier free structure for a least mean squares (LMS) algorithm with /spl Sigma//spl Delta/ modulated input signals is proposed. An additional filter in the error path is necessary to allow adaptation of the filter. Simulation results for system identification demonstrate that the performance of described design is comparable to the performance of an equivalent pulse code modulated (PCM) system.

We present a pole estimation algorithm which is based on an overdetermined adaptive IIR filter with an additional postprocessing stage to extract the pole locations from the adaptive weights. The adaptive filtering algorithm used, is a pseudo-linear regression algorithm which is solved by a time-recursive QR decomposition. Two pole classification schemes are presented to separate the true poles and the superfluous poles. The classification schemes are based on the occurrence of pole-zero cancelation and on the pole movement in the z-plane. Floating point simulations are presented to demonstrate the performance of the proposed algorithm.

This paper introduces a novel adaptive IIR filter structure and its corresponding LMS weight update equations. This "full-feedback" structure has performance advantages over the conventional adaptive IIR filter structure in certain situations. A full-feedback filtered-u adaptive IIR algorithm is also presented, which may be applied to active noise control in ducts.

An approach to adaptive IIR filtering based on a pseudo-linear regression and a QR matrix decomposition is developed. The algorithm has proved to be stable and has good convergence properties if the unknown system satisfies the strictly positive real condition. The derivation of the algorithm is straightforward and the computational complexity is less than the computational complexity of the IIR-RPE algorithm. Simulation results of system identification with synthetic and real world data are shown comparing the algorithm with the IIR-RPE and the IIR-LMS algorithm.

An adaptive nonlinear filter based on a second order Volterra series and on an IIR filter structure is presented. This filter is able to model higher than second order nonlinearities for systems where the nonlinearities are harmonically related. This solution represents an alternative to using higher than second order Volterra filters. We present a full derivation of this gradient search based adaptive nonlinear filter and also highlight the various assumptions and simplifications which require to be made in order to produce a practical algorithm. A comparison is made in terms of the performance and computational complexity between an adaptive second order IIR Volterra filter and an adaptive second and third order Volterra filters.

In this letter, an adaptive recursive nonlinear filter based on the Volterra series and an infinite impulse response (IIR) structure is considered. For certain types of nonlinear systems where high-order nonlinearities are recursively generated, we show that the adaptive recursive second-order polynomial filter has improved performance over the well-known (nonrecursive) adaptive second-order Volterra filter and a third-order Volterra filter. This filter represents an alternative to using a traditional Volterra filter whose order has been increased to match that of the system being modeled.

The design for a DSP (digital signal processing) subsystem for a health monitoring system for gas turbine engines is described. Knowledge-based techniques are emerging as useful tools for health and condition monitoring of high value engineering systems. A demonstrator system that uses these techniques for monitoring the health and performance of a marine gas turbine engine is described, with particular emphasis on the DSP subsystem which interfaces directly with the raw sensor data coming from the monitored system. The DSP subsystem is a coupled system using both numerical and symbolic methods for signal interpretation; i.e., the DSP subsystem provides a description of the sensors signals in meaningful symbolic terms that reflect the state of the monitored system. The signal abstractions are put in a form suitable for symbolic processing by the knowledge-based diagnostic subsystem of the monitoring system to determine the health of the monitored system.>

The stability of the noncanonical least mean square (NCLMS) algorithm is investigated. The NCLMS effectively uses a different step size for each tap coefficient position during adaptation. The classical LMS step size bound cannot be directly applied to the NCLMS. The weight error vector is modeled as a first-order difference equation and a stability bound for the NCLMS is derived. Simulation results are presented to back up the analysis.>

The authors present analysis and simulations of an LMS (least mean square) based adaptive filtering algorithm called the NCLMS (non-canonical LMS). Rather than using the standard FIR (finite impulse response) filter as for the LMS algorithm, a modified structure called the NCFIR (non-canonical FIR) is used. The NCFIR allows a faster VLSI implementation than the conventional FIR. A comparison of the performances of the NCLMS and conventional LMS algorithm is presented for an inverse system modeling application. Simulation results are given which show a reduced EMSE (excess mean square error) level and an improved performance in an impulsive noise environment for the NCLMS over the LMS algorithm.>

Consideration is given to Kalman filtering algorithms from the viewpoint of fast and stable implementation. A number of authors have reformulated certain signal processing and linear algebra algorithms to be square-root-free in an effort to simplify parallel implementation. Following these derivations a number of Kalman filter algorithms and parallel array architectures have been realized that also avoid square-root computations. It is shown that, contrary to the motivation for realizing these algorithms, the standard algorithms (utilizing square roots) can be implemented more quickly than the square-root-free versions. Furthermore, the square-root-free versions suffer from overflow/underflow and in some cases are numerically unstable.>

For fast and numerically stable algorithms, array processors with floating point multiplication, division, and square rooting are necessary. The authors consider the use of the arithmetic operation of square rooting in the QR algorithm as used in many linear algebraic signal processing algorithms. Rather than reformulating the algorithms to be square root free with the inherent problems of numerical instability, loss of orthogonality, and overflow/underflow, the square root is reconsidered from first principles and arrays are designed that are as fast and have a smaller chip area than the analogous division arrays. This implies that implementations such as square foot free Givens rotations should not be considered in an application-specific integrated circuit or similar design due to their potential instability and susceptibility to overflow.>

A technique is described for partitioning 2-D signal flow graph (SFG)/systolic arrays to a 2-D triangular array (triarray) of N(N+1)/2 processors. The folding technique can be used to partition 2-D SFGs onto smaller subarrays of the SFG. The stages of development are all highly suited to CAD from algorithm specification to array implementation, and can be performed in an integrated mapping methodology using the Occam algebra. Because of the Occam algebra-based partitioning, the data flow of the SFG has been preserved and the SFG implementation on the triarray is guaranteed to be functionally correct. The control overheads introduced by this scheme are minimal and very straightforward, i.e. a very simple mux and demux processes at the link interfaces of each transputer to code and decode the data flow tags. It is concluded that a triarray of transputers (or transputer link wavefront devices) is a highly versatile and flexible array processor when the folding scheme is used.>