This paper presents sub-band full-duplex (SBFD) as an alternative to in-band full-duplex (IBFD) for enabling simultaneous wireless communication and sensing in cell-free massive MIMO (CF-mMIMO) systems. Unlike IBFD integrated sensing and communication (ISAC) systems that require self-interference cancellation and the decoupling of mutual interference between uplink communication and radar signals, SBFD employs non-overlapping frequency resources for uplink and downlink or radar transmissions within a predefined SBFD timeslot. In the proposed SBFD CF-mMIMO ISAC system, we demonstrate a multi-target position tracking scheme, where range and angle-of-arrival (AoA) measurements are fused with an extended Kalman filter. We further characterize the joint impact of residual self-interference (SI), access point (AP)-to-AP cross-link interference (CLI), uplink communication interference, and AP-user equipment association on the Cramér-Rao lower bounds (CRLBs) of range and AoA estimation. Results indicate that, in the high residual SI/CLI power regime, SBFD experiences less degradation in range and AoA CRLBs compared to IBFD due to its inherent frequency isolation. Specifically, increasing residual SI/CLI power from 30 dBm to 50 dBm increases the CRLBs by 3.01 m2 and 0.09 rad2 for SBFD compared to an increase of 12.94 m2 and 0.34 rad2 for IBFD at a residual UL communication interference of -10 dBm. Further, performance analysis reveals a performance dependence on power allocation and the ratio of radar to communication sub-band allocation.

The ever-increasing computational load and shrinking power budget have accentuated the need for energy-efficient operation of edge devices. In this article, a combination of static CMOS logic and Hybrid Pass transistor logic with Static CMOS output (HPSC), which has no floating or weak nodes and is thus as robust to noise as static CMOS logic, is used for designing toolchain-compatible super-Vth standard cells. Optimized HPSC variants of a 2/3-input XOR cell, a 2/3-input XNR cell, a half adder cell, a full adder cell, and two variants of a 1-bit multiply-accumulate combinational cell are presented in a commercial 65nm Low-Power CMOS technology. Measurements of test structures based on ring oscillators and dummy path techniques show an average frequency and average energy-delay product improvement of up to 30.3% and 32.5% respectively at typical conditions. The proposed cells’ superior performance compared to the commercially available standard cells is also highlighted in terms of propagation delay, leakage, and dynamic power consumption. This shows a promising approach for foundries or other commercial entities to improve digital design performance to about half a technology node at no additional cost.

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a Deep Reinforcement Learning (DRL)-based approach for dynamic beamforming and power allocation in ISAC systems. The DRL agent interacts with the environment and learns optimal strategies through trial and error, guided by predefined rewards. Simulation results show that the DRL-based solution converges within 2000 episodes and achieves up to 80% of the spectral efficiency of a semidefinite relaxation (SDR) benchmark. More importantly, it offers a significant improvement in runtime performance, achieving decision times of around 20 ms compared to 4500 ms for the SDR method. Furthermore, compared with a Deep Q-Network (DQN) benchmark employing discrete beamforming, the proposed approach achieves approximately 30% higher sum-rate with comparable runtime. These results highlight the potential of DRL for enabling real-time, high-performance ISAC in dynamic scenarios.

In this paper, we consider a full-duplex (FD) Inte-grated Sensing and Communication (ISAC) system, in which the base station (BS) performs downlink and uplink communications with multiple users while simultaneously sensing multiple targets. In the scope of this work, we assume a narrowband and static scenario, aiming to focus on the beamforming and power allocation strategies. We propose a joint beamforming strategy for designing transmit and receive beamformer vectors at the BS. The optimization problem aims to maximize the communication sum-rate, which is critical for ensuring high-quality service to users, while also maintaining accurate sensing performance for detection tasks and adhering to maximum power constraints for efficient resource usage. The optimal receive beamformers are first derived using a closed-form Generalized Rayleigh Quotient (GRQ) solution, reducing the variables to be optimized. Then, the remaining problem is solved using floating-point Genetic Algorithms (GA). The numerical results show that the proposed GA-based solution demonstrates up to a 98% enhancement in sum-rate compared to a baseline half-duplex ISAC system and provides better performance than a benchmark algorithm from the literature. Additionally, it offers insights into sensing performance effects on beam patterns as well as communication-sensing trade-offs in multi-target scenarios.

High performance and energy efficiency are very crucial aspects in e.g. the field of edge computing where a tight power budget constrains the device operation. Different logic families were explored over the years to design standard cells with higher performance and/or lower power while keeping the noise immunity and the compatibility with design automation tools intact. Hybrid pass transistor logic with static CMOS output (HPSC) seems to be promising and is exploited in this paper to design low energy, high performance and toolchain-compatible standard cells without compromising on noise immunity and chip area. This paper presents a 2/3-input XOR cell, a 2/3-input XNOR cell, two variants of a half adder cell, a full adder cell and two variants of a 1-bit multiply-accumulate combinational cell based on a combination of HPSC and static CMOS logic in a commercial 65nm Low-Power CMOS technology. Post-layout simulations over all the process-voltage-temperature corners show a 4.7% - 35.7% lower energy-delay product with significant improvement in the propagation delay of the proposed cells.

Voice Activity Detection (VAD) is a technique used to identify the presence of human voice in an audio signal. It is implemented as an always-on component in most speech processing applications. As speech is absent most of the time, this component typically dominates the overall average power consumption of the system (excluding microphone). The widespread usage in speech applications and the need for ultra low power VAD have led to a plethora of algorithms and implementations in the hardware domain, necessitating a comprehensive study and analysis to understand (real-time) requirements, different design parameters, testing strategies, but also to identify design trends, challenges and guidelines for future implementations and testing of VAD devices. A scoping review was conducted to identify the articles for hardware implementations of VAD from January 2010 - December 2021, the results of which are presented in this article. The results highlight a big design space being used for VAD along with a lack of standard testing methodology and usage of application-dependent performance metrics. An increased usage of filter-based feature extractors along with neural-network-based classifiers is observed. Due to lack of standardisation, no other trends can be established from the results. A set of rules and guidelines are therefore provided to facilitate the future development and benchmarking of VADs.

Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing blockage types, is an initial step towards signal blockage avoidance. In this context, we investigate the feasibility of using human motion recognition as a surrogate task for blockage type recognition through a set of hypothesis validation experiments using both qualitative and quantitative analysis (visual inspection and hyperparameter tuning of deep learning (DL) models, respectively). A surrogate task is useful for DL model testing and/or pre-training, thereby requiring a low amount of data to be collected from the eventual JCAS environment. Therefore, we collect and use a small dataset from a 26 GHz cellular multi-user communication device with hybrid beamforming. The data is converted into Doppler Frequency Spectrum (DFS) and used for hypothesis validations. Our research shows that (i) the presence of domain shift between data used for learning and inference requires use of DL models that can successfully handle it, (ii) DFS input data dilution to increase dataset volume should be avoided, (iii) a small volume of input data is not enough for reasonable inference performance, (iv) higher sensing resolution, causing lower sensitivity, should be handled by doing more activities/gestures per frame and lowering sampling rate, and (v) a higher reported sampling rate to STFT during pre-processing may increase performance, but should always be tested on a per learning task basis.

We develop a systematic methodology to experimentally investigate the impact of interference from a non-listen-before-talk ultra-narrowband (UNB) signaling technique on Wi-Fi links. The methodology is based on a worst-case interference scenario, and consists of three investigating steps. This methodology is then applied to a measurement setup to practically study the case of 100 bps UNB signals interfering with an IEEE 802.11n transmission in the 2.4GHz band. Five different Wi-Fi devices are tested. The UNB signal is generated in two modulation schemes, the on-off-keying (OOK) and the Gaussian minimum-shift-keying. Both single and multiple simultaneous UNB interferers are considered. An analysis of the measurement results shows that three of the tested Wi-Fi devices cannot coexist with the considered non-listen-before-talk UNB communication system. The throughput performance analysis of the other tested devices shows that the OOK-modulated UNB signal has the least interfering impact, and the Wi-Fi pilot subcarriers are the most vulnerable to UNB interference. However, if a single UNB interferer avoids these subcarriers and employs the OOK-modulation scheme, then wireless coexistence is possible as long as the signal-to-interference ratio of Wi-Fi to UNB is greater than 30dB, given that a drop to 75% of the maximum Wi-Fi throughput is acceptable.

Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.

Approximate computing allows the introduction of inaccuracy in the computation for cost savings, such as energy consumption, chip-area, and latency. Targeting energy efficiency, approximate designs for multipliers, adders, and multiply-accumulate (MAC) have been extensively investigated in the past decade. However, accelerator designs for relatively bigger architectures have been of less attention yet.

Cross-correlation can be used in energy detection applications, such as spectrum analyzers, but also frequency shift keying (FSK) receivers, to improve noise suppression. To achieve higher signal-to-noise ratio (SNR), integration in time may be used, but could make it rather slow for communication purposes. In order to achieve better data-rates in low SNR conditions, we propose to use multiple chains instead of the traditional two chains. In this paper, we show an analysis of the SNR improvement and the power consumption penalty for BFSK modulation when using more chains. It shows that for low noise correlation between the chains, the improvement in sensitivity is proportional to the number of chains. Also, we develop a figure-of-merit to evaluate the optimum number of chains for different parameters of the receiver design. Furthermore, two examples from literature are analyzed. At their optimum number of chains, they both show ~6dB improvement in sensitivity with similar or even better figure-of- merit.

Carrier frequency offset (CFO) and time-varying fading channels are two problems that emerging ultra-narrowband solutions for Internet of Things and Wireless Sensor Networks need to tackle. Previously, an offset tolerant demodulator for Double Differential PSK (DDPSK) has been proposed to overcome CFO. To combat channel distortion, time or frequency diversity together with coding techniques can be utilized. However, these necessitate transmitting extra bits and leads to an increase in packet time. A longer packet time corresponds to longer on-time of the RF front-end and more power consumption. To avoid longer packet for the same symbol rate, a higher order modulation is required which considerably degrades BER performance. In this work, instead of higher order DDPSK, hybrid frequency/phase modulation is used. An offset tolerant demodulator for hybrid modulation is proposed which provides the same robustness against CFO as DDPSK. Besides, it provides higher order of modulation with less performance loss compared to higher order DDPSK. Simulation results show that a combination of BFSK and QPSK using the proposed demodulator can achieve almost 4 dB improvement at BER=0.001 in a Rayleigh channel compared to Double Differential 8PSK (DD8PSK) which can provide the same modulation order.

The autocorrelation demodulator (ACD) for DDPSK is an offset tolerant demodulator which has been introduced for applications where the signal experiences large Doppler shift. Moreover, emerging ultra-narrowband solutions for Internet of Things and Wireless Sensor Networks can exploit DDPSK to avoid the use of costly crystal or power hungry thermal compensators. However, to tolerate frequency offset the bandwidth of the lowpass or bandpass filter before the demodulator must increase which leads to a larger noise bandwidth and degrades BER performance. This work proposes a new method to overcome this problem. Instead of one path of ACD, samples at the output of the filter go through multiple paths with adjusted delay and interval for correlation in the ACD. The sum of the outputs of these paths provide the input to the detector with an increased SNR compared to conventional structure. Using the proposed method, the SNR per bit required for a certain BER remains independent of filter bandwidth if the target BER is less than 0.01.

The Balian-Low theorem (BLT) states the fundamental impossibility to design waveforms for L2(ℝ), which 1) form an orthogonal set, 2) are time-frequency localized, and 3) attain a critical waveform density such that they form an orthogonal basis. This article closes the gap between existing waveform designs and the BLT. The main contribution is the design of orthogonal, time-frequency localized, spectrum efficient waveforms for hexagonal lattices. The waveform design is adaptive by a single design parameter, which tradesoff time-frequency localization with the waveform density. As the orthogonalization procedure is based on employing the minimum number of most time-frequency localized waveforms (Hermite functions) it is argued that the results may be optimal in terms of combined spectrum efficiency and time-frequency localization. An example is provided for waveforms for a hexagonal lattice, which are quasi-orthogonal, time-frequency localized, and up to 99% of the critical waveform density. Although the designed waveforms are not strictly orthogonal, their cross-correlation can be made arbitrarily small. The robustness in doubly dispersive channels and the efficiency for multiuser scenarios are discussed and compared to conventional orthogonal frequency division multiplexing (OFDM).

Multi-carrier communication is associated with a high peak-to-average power ratio (PAPR). A new PAPR reduction method is proposed which is based on rotating the time-frequency representation of a transmit signal, prior to transmission. In general, a time-frequency rotation of a multi-carrier signal would change the signal basis, affecting the robustness of the transmit signal in fading channels. Exceptions are transmit signals constructed by (modulated) Hermite functions. The PAPR has been analyzed for transmit signals based on 64 Hermite functions. Allowing a rotation over 16 angles in time-frequency, the PAPR, which occurs with a probability of 10−3, is reduced by 3.8 dB at the cost of an increased computational complexity and a minor loss in spectral efficiency.

A new signal set, based on the Fourier and Hermite signal bases, is introduced. It combines properties of the Fourier basis signals with the perfect time-frequency localization of the Hermite functions. The signal set is characterized by both a high spectral efficiency and good time-frequency localization. Its robustness against time-frequency shifts is assessed and compared to Hermite and Fourier basis signals. The Fourier-Hermite signal set is particularly designed for communications in spectrum-scarce environments.

Extended Symbol OFDM (ES-OFDM) is applied in case of a multiple antenna receiver. The receiver architecture is based on the observation that OFDM constellation points can be determined by means of correlation. Summing correlations between multiple antennas leads to an interferometer receiver. This approach gives the freedom to choose which correlations are summed. Three antenna structures are explored: a Uniform Linear Array (ULA) and a sparse array where all correlations are summed and a sparse array where only a selection of correlations are summed. The sensitivity of the Bit Error Rate (BER) of an ES-OFDM communication link to an interfering source from different directions is studied. The ULA leads to a relatively wide BER main lobe, the range of angles around the Direction of Arrival of the ES-OFDM signal where the BER is high. Outside this range, the interfering source is suppressed to low BERs, in many cases beyond requirements. By using sparse arrays, the width of the BER main lobe can be traded against the BER levels outside the BER main lobe. This effect is shown for a sparse array where all possible correlations are summed. By summing only those correlations that lead to a uniform co-array, BER levels outside the BER main lobe are lower for an interferometer receiver compared to a traditional beamforming receiver.

A nonminimum-phase channel can always be decomposed into a minimum-phase part and an all-pass part. In our approach, called all-pass CMA, the dimensionality of the CMA algorithm has been reduced to improve blind equalization of a nonminimum-phase channel's all-pass part. The dimensionality reduction has been performed by parameterizing the CMA cost function in terms of the nonminimum-phase zero location of the all-pass part to be compensated. Currently, all-pass CMA can only compensate a single nonminimum-phase zero. However, compared to CMA, it typically provides a faster and more accurate compensation of this zero.

Synchronization and matched filtering of signals in time dispersive, frequency dispersive and time-frequency dispersive channels are addressed in this paper. The ‘eigenfunctions’ of these channels form the signal sets under investigation. While using channel-eigenfunctions is a first requirement for undistorted data transmission, a second necessity is to achieve good synchronization over the domains of time and frequency. The synchronization problem in time-frequency for non-stationary signals is discussed. A spiral correlation method is proposed to achieve synchronization and matched filtering in time-frequency. Spiral correlation, using the pattern of a sunflower, is simulated and evaluated. It is argued that partial spiral correlation can lead to a significant reduction in computational complexity necessary for synchronization. Generalizations and identities based on the fractional Fourier transform are provided which omit the need for fractional delay filters.

To reduce the energy consumption in wireless sensor network transceivers, we propose an approach which combines two tradeoffs. The first tradeoff is between the receiver sensitivity and transmitter output power. The second one is the duty cycle and data rate of the transceiver. The combined approach gives us the optimum choice of noise figure and data rate for a given application and transceiver architecture. Considering a typical transceiver architecture and perfectly synchronized system, we show that the energy consumption can indeed be reduced with this approach compared to choosing either data rate or noise figure arbitrarily. Moreover, in case of a wakeup receiver architecture and slot based MAC protocol, applying this method, we show that there is a different combination of optimum data rate and noise figure value for the wakeup receivers to minimize the wakeup energy.

Many systems interact with their physical environment. Design of such systems need a modelling and simulation tool which can deal with both the continuous and discrete aspects. However, most current tools are not adequately able to do so, as they implement both continuous and discrete time signals as consisting of separate values at a single global simulation clock. The consequence is that simulation, of a time delay for example, either yields inaccurate results or becomes inefficient.

Spectrum sensing is one of the key characteristics of a cognitive radio. Energy detection provides maximum flexibility by not relying on any prior knowledge, but suffers from an SNR-wall due to noise uncertainty. Crosscorrelation of the outputs of two receiver paths is a technique to reduce the noise level of the total receiver, and hence improves the SNR. The reduction of the noise is limited by correlated noise originating from shared components near the antenna. In this paper we explore the use of a separate antenna for each receiver for crosscorrelation spectrum sensing. One immediate advantage is that due to the removal of the splitter, which was necessary to interface the single antenna to two receivers, the SNR improves, significantly reducing the required measurement time. A lot of the noise correlation can be removed, leading to a lower residual noise floor. The noise at each antenna will still be partially correlated due to mutual coupling, spatial noise correlation and man-made noise. We show that some signal power can be lost in the sensing process due to partial decorrelation of the signal at the two antennas. Fortunately, this seems to be a problem only in highly mobile environments, which makes the use of two-antenna crosscorrelation spectrum sensing an interesting solution towards more reliable energy detection.

By extending OFDM symbols, acceptable BER performance can be achieved at low SNRs. Two alternative differential receiver architectures are presented, a receiver based on a FX correlator (Fourier transformation before correlation) and based on an XF correlator (correlation before Fourier transformation). To reduce the complexity and hence the power consumption of both the ADC and the first digital processing stage single- or two bit quantization is used. The receiver based on the XF correlator is more suited to exploit such coarse quantization. Two basic effects are visible if coarse quantization is used. First, the BER performance is reduced due to the introduction of quantization errors. Second, beyond certain SNR levels, the BER performance does not increase due to the correlation between quantization errors. Furthermore, oversampling increases BER performance considerably. For single bit quantization with oversampling, acceptable BERs (-3) can be achieved for a limited SNR range for symbol extension factors of 32 and 64. In case of two bit quantization without oversampling, the results are comparable with single bit quantization with two times oversampling. For two bit quantization in combination with two times oversampling, acceptable BERs are achieved for symbol extension factors 8, 16, 31 and 64.

Until a decade ago, the concept of phased array beam forming was mainly implemented with mechanical or analog solutions. Today, digital hardware has become powerful enough to perform the massive number of operations required for real-time digital beam forming. While more and more applications are using beam forming to improve the communication channel utilization both in space and frequency, many dedicated digital architectures are proposed for the processing. By using a reconfigurable architecture, the same hardware platform can be reused for different applications with different processing needs. In this paper, we present a reconfigurable Multi-processor System-on-Chip based solution for phased array processing that supports advanced tracking mechanisms to continuously receive signals with a mobile receiver. An adaptive beam former for DVB-S satellite reception is presented, that uses a Constant Modulus Algorithm to track satellites. The processing of a receiver with 64 antennas and 3 beams is mapped on a reconfigurable processor named Montium TP. The total implementation of such a receiver requires about 570 clock cycles on a single Montium TP, but can also be partitioned over multiple Montium TPs to support larger phased arrays.

A system that uses adaptive beamforming techniques for mobile DVB-S reception is proposed in this paper. The purpose is to enable DVB-S reception in moving vehicles. Phased arrays are able to electronically track the desired signal during dynamic behaviour of the vehicle the array is mounted on. The proposed system uses blind beamforming to adapt the array steering vector to changing signal (conditions and) directions. Movement of the vehicle, the phased array is mounted on, leads to modulus and phase deviations at the beamformer output. An extended version of the CMA algorithm is used to adapt the steering vector weights to compensate for those deviations. For simulation of the proposed system a model of vehicle dynamics is used to generate realistic antenna data. Simulation of the proposed system based on this antenna data shows appropriate corrections for modulus and phase deviations.

Reduction circuits are used to reduce rows of floating point values to single values. Binary floating point operators often have deep pipelines, which may cause hazards when many consecutive rows have to be reduced. We present an algorithm by which any number of consecutive rows of arbitrary lengths can be reduced by a pipelined commutative and associative binary operator in an efficient manner. The algorithm is simple to implement, has a low latency, produces results in-order, and requires only small buffers. Besides, it uses only a single pipeline for the involved operation. The complexity of the algorithm depends on the depth of the pipeline, not on the length of the input rows. In this paper we discuss an implementation of this algorithm and we prove its correctness.

Due to small sideband power leakage, filter bank multicarrier techniques are considered as interesting alternatives to traditional OFDMs for spectrum pooling Cognitive Radio. In this paper, we propose an oversampled filter bank multicarrier system for Cognitive Radio. The increased spacing between adjacent subcarriers in the oversampled filter bank multicarrier system largely reduce the intercarrier interference, the key limitation of the OFDM based Cognitive Radio. The proposed multicarrier system is compared with OFDM for BER performance and sideband power rejection. Design tradeoffs of the major parameters of the oversampled filter bank will be discussed. We also suggest a fast implementation of the proposed filter bank modulation based on generalized DFT filter bank model, followed by a computational complexity analysis.

Cognitive Radio has been proposed as a promising technology for solving today’s spectrum scarcity problem by means of dynamic spectrum access. The multiprocessor system-on-chip (MPSoC) reconfigurable platform is proposed as an enabling technology for cognitive radio. In this paper, we propose a design methodology based on task transaction level interface for the design of cognitive radio baseband on an MPSoC reconfigurable platform. The reconfiguration of a novel, low-complexity fast Fourier transform for orthogonal frequency-division multiplexing based Cognitive Radio is used as a design case to show the effectiveness of the methodology for modelling the dynamic behavior of Cognitive Radio and facilitating the platform implementation.

In this paper, a two-step methodology is introduced to analyse the mapping of cyclostationary feature detection (CFD) onto a multi-core processing platform. In the first step, the tasks to be executed by each core are determined in a structured way using techniques known from the design of array processors. In the second step, the implementation of tasks on a processing core is analysed. Using this methodology, it is shown that calculating a 127 times 127 discrete spectral correlation function requires approximately 140 mus on a tiled system on chip (SoC) with 4 Montium cores

Cognitive radio is a promising technology to utilize non-used parts of the spectrum that actually are assigned to licensed services. An adaptive OFDM based cognitive radio system has the capacity to nullify individual carriers to avoid interference to the licensed user. Therefore, there could be a considerably large number of zero-valued inputs/outputs for the IFFT/FFT in the OFDM transceiver. Due to the wasted operations on zero values, the standard FFT is no longer efficient. Based on this observation, we propose to use a computationally efficient IFFT/FFT as an option for OFDM based cognitive radio. Mapping this algorithm onto a reconfigurable architecture is discussed.

A model for determining the cross-correlation function of partially correlated noise is presented. In this model a strong interferer is included and represented by a periodic signal common to both channels of the correlator. A general expression for the correlation function is deduced and verified. The power spectrum of a calculated correlation function is compared with a simulation. The results presented in this paper form a base for the design of modern multibit correlators. These are part of future generation radio astronomy receivers which increasingly have to cope with man-made interfering signals.