Phase selection design for reconfigurable intelligent surfaces (RISs) is a significant research challenge, as a closed-form optimal solution for a multi-user (MU) system is believed to be intractable. While existing methods achieve strong near-optimal performance, they typically entail high computational complexity. In this work, we take a different approach and propose a practical method that achieves competitive performance while substantially reducing computational complexity. To do so, we consider a RIS divided into subsurfaces. Each subsurface is designed specifically for one user, who is served on their own frequency band. The other subsurfaces (those not designed for this user) provide additional uncontrolled scattering. We derive the exact closed-form expression for the mean signal-to-noise ratio (SNR) for the proposed subsurface design (SD) when all channels experience correlated Ricean fading. We simplify this to find the mean SNR for line-of-sight (LoS) channels and channels experiencing correlated Rayleigh fading. An iterative SD (ISD) process is proposed, where subsurfaces are designed sequentially, and the phases that are already set are used to enhance the design of the remaining subsurfaces. This is extended to a converged ISD (CISD), where the ISD process is repeated multiple times until the SNR increases by less than a specified tolerance. The ISD and CISD both provide a performance improvement over SD, which increases as the number of RIS elements increases. The SD is significantly simpler than the lowest complexity MU method we know of, and despite each user having less bandwidth, the SD outperforms the existing method in some key scenarios. The SD is more robust to strongly LoS channels and clustered users, as it does not rely on spatial multiplexing like other MU methods. Combined with the complexity reduction, this makes the SD an attractive phase selection method.

A key challenge facing reconfigurable intelligent surfaces (RISs) is channel state information acquisition. Passive RISs cannot generate pilot signals or process data, making rapid temporal changes in the channel problematic. Additionally, the impact of spatial changes in RIS channels has not been thoroughly investigated. Therefore, in this work, we use second order statistics to investigate the spatio-temporal behaviour of a single-user (SU) RIS system. Assuming a line-of-sight (LoS) RIS to base station (BS) link, we derive an exact expression for the level crossing rate (LCR) of the RIS link (user equipment (UE)-RIS-BS path) and propose a numerically stable approximation for the LCR of the global UE-BS channel. Each LCR expression attained is then utilised to find the corresponding average fade duration (AFD). The temporal signal-to-noise ratio (SNR) correlation is also derived assuming an LoS RIS-BS link. Assuming a Ricean RIS-BS link, expressions for the spatial correlation matrix of the global channel and the mean SNR loss due to channel ageing are derived. All of the analyses are verified by simulation, and the impact of key system parameters is investigated. We show that the use of an RIS does not significantly amplify changes in the channel.

We analyse the level crossing rate (LCR) of an uplink single-user (SU) reconfigurable intelligent surface (RIS) aided system. It is assumed that the RIS to base station (RIS-BS) channel is deployed as line-of-sight (LoS), and the user (UE)-RIS and UE-BS channels are correlated Rayleigh. For the optimal RIS reflection matrix, we derive a novel and exact analytical LCR expression for when the direct (UE-BS) channel is blocked, i.e. the RIS-only channel. Also, the existing exact expression for the direct-only channel (equivalent to classical maximal-ratio-combining (MRC)) suffers from extreme numerical precision problems when the BS has many elements. Therefore, we propose a new stable and accurate approximation to the LCR of the direct channel. The approximation is based on replacing any small similar eigenvalues of the channel correlation matrix by their average. We show that increasing the number of elements at the RIS or BS and decreasing channel correlation makes the LCR drop more rapidly for thresholds away from the mean SNR. Crucially, we find that RIS systems do not significantly amplify temporal variations in the channel. This is particularly beneficial for RIS systems considering the difficulty in acquiring channel state information (CSI).

In distributed radio localisation systems based on time-of-flight measurements accuracy of time synchronisation between radio nodes places limits on the achievable performance. In this work, we evaluate feasibility of using pulse-per-second reference signals provided by off-the-shelf timing GNSS modules as a means for wireless synchronisation of distributed softwaredefines radio nodes. Our experimental results indicate that it is possible to achieve sub-nanosecond level synchronisation required for accurate time-of-flight estimation. This, however, requires post-processing and firmware modifications in order to remove the influence of instability of the timing reference signals.

In this paper, we analyse the performance of a reconfigurable intelligent surface (RIS) aided system where the RIS is divided into subsurfaces. Each subsurface is designed specifically for one user, who is served on their own frequency band. The other subsurfaces (those not designed for this user) provide additional uncontrolled scattering. A new subsurface RIS design is developed based on the optimal single-user design for a pure line-of-sight (LoS) base station (BS) to RIS channel. This is also extended to arbitrary BS-RIS channels. For our method, exact closed form solutions for the mean SNR and a mean rate upper bound are derived for the BS-RIS LoS scenario. For each user, the designed subsurface performs optimally in LoS conditions and is remarkably robust to non-LoS conditions. The system design drives down complexity to extremely low levels, reducing RIS design and receiver processing complexity and reducing the channel estimation requirements. We also quantify the complexity-performance trade-off for the new design relative to multi-user approaches.

We evaluate the performance of a low-power narrowband system utilising higher-order sectorisation (HOS) at the base stations (BS) and directional antennas (DA) at fixed user equipment (UE). We use a specific rotational frequency reuse scheme (RFR) for directional UEs and extend it for HOS (3, 6 and 12 sectors) by considering antenna orientations for interference avoidance. We propose two novel schemes for directional UEs with a cluster size less than one, named double frequency reuse (DFR) and triple frequency reuse (TFR). These schemes are tested and compared to conventional frequency reuse (CFR) through simulations in a realistic geographic terrain. The simulation results indicates that the proposed schemes are helpful to achieve higher capacity while using different BS and UE antenna beamwidths. It is shown that scheme RFR provides a performance enhancement compared to CFR only with wider UE antenna beamwidths. The implementation of DFR achieves reduced outage probability and offers a large increase in average spectral efficiency per cell (ASE) of up to 84.8-81.6% in 6 and 12 sectored system. The scheme TFR provides a maximum ASE of up to 149-153% in 6 and 12 sectored system. However, in a six-sectored system, TFR only meets the coverage criteria with a narrow beamwidth antenna of 20°.

Millimeter-wave channel measurements for a meeting room, lecture room and open plan office floor were used to analyse and model the spatial consistency of channel clusters. Particularly, how the angles of arrival and delays of extracted multi-path components in each cluster varied with small changes in receiver location. We extended the KPowerMeans algorithm, for classifying captured multi-path components into clusters from the delay/angular domains, to include the location domain to allow the spatial consistency of the clusters between locations to be analysed. The observed spatial consistency in measurements was then used to validate whether the 3GPP spatial consistency procedure (3GPP SC-I) reflects real world spatial consistency. The 3GPP spatial consistency procedure for ensuring spatial consistency of cluster parameters during mobile user simulations was then applied for each environment, allowing for comparison between the ‘predicted’ cluster parameters and the measured cluster parameters. The 3GPP model/procedure showed a good fit for all environments but highlighted the impact of the environment on the amount of accuracy of the procedure.

In this paper, we present a design and first proof-of-concept test results of a distributed radio-localization system for tracking of moving targets with a swarm of drones. In particular, we develop and test a software framework that uses bistatic ranges to follow a target in-flight. We conduct full-system flight test with up to two drones in the air, three simulated drones, and a laptop equipped with a GPS module as the target. The system is shown to dynamically track the target as it moved, with both real and simulated drones following it in a specified formation.

Most robots currently being sold or developed are either stylized with white material or have a metallic appearance. In this research we used the shooter bias paradigm and several questionnaires to investigate if people automatically identify robots as being racialized, such that we might say that some robots are 'White' while others are 'Asian', or 'Black'. To do so, we conducted an extended replication of the classic social psychological shooter bias paradigm using robot stimuli to explore whether effects known from human-human intergroup experiments would generalize to robots that were racialized as Black and White. Reaction-time based measures revealed that participants demonstrated 'shooter-bias' toward both Black people and robot racialized as Black. Participants were also willing to attribute a race to the robots depending on their racialization and demonstrated a high degree of inter-subject agreement when it came to these attributions.

We investigate multiple input multiple output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems that operate with insufficient cyclic prefix (CP). Using a CP shorter than the channel delay spread can enable a significant improvement in bandwidth utilization or range extension for OFDM networks at the cost of increased intersymbol interference (ISI) and inter-carrier interference (ICI). We first analyze the effect of ICI and ISI on the received signal. A bi-directional M-algorithm (BDMA) is then proposed for high performance trellis-based equalization to construct an iterative interference mitigation and detection process. Simulations show that, after only 2 iterations, the bit error rate (BER) of the proposed equalization scheme can converge to that of a sufficient-CP system even when the channel delay spread is 6 times longer than the insufficient CP.

We investigate orthogonal frequency-division multiplexing (OFDM) systems that operate with limited or insufficient cyclic prefix (CP). Using a shorter CP can enable a significant reduction in bandwidth usage or enable range extension for OFDM networks. The cost is increased inter-symbol interference (ISI) and inter-carrier interference (ICI). Here, the effect of ICI and ISI on the received signal is analyzed. We then propose a two-stage detector for single input multiple output (SIMO) systems with two receive antennas. Taking advantage of the spatial diversity, the first detection stage uses a low complexity zero-forcing (ZF) or minimum mean square error (MMSE) method to estimate the transmitted data for aiding the second stage trellis based detector. The proposed schemes provide satisfactory performance for delay spreads up to 3.5 times the CP.

In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction in the code tree simultaneously. It is shown that the proposed BFA can achieve more than twice the decoding throughput compared to the conventional unidirectional Fano algorithm (UFA) and there is higher throughput improvement at low signal-to-noise ratio (SNR). This new BFA decoding technique is applied in the parallel convolutional decoding architecture in very high throughput systems, such as the WirelessHD system. Due to the variability in the decoding delays of the parallel codewords, a scheduler is introduced in the parallel Fano decoding architecture which can dynamically allocate the idle decoders to assist with decoding the other parallel codewords in a bidirectional manner. It is shown that the proposed parallel Fano decoding with scheduling can dramatically increase the decoding throughput compared to the parallel Fano decoding without scheduling, and its computational complexity is much lower than that of parallel Viterbi decoding, especially at high SNR. The performance of the parallel Fano decoding with different scheduling schemes is also compared and analyzed in detail in the paper.

The bidirectional Fano algorithm (BFA) can achieve at least two times decoding throughput compared to the conventional unidirectional Fano algorithm (UFA). In this paper, bidirectional Fano decoding is examined from the queuing theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model the BFA decoder with a finite input buffer. The relationship between the input data rate, the input buffer size and the clock speed of the BFA decoder is established. The DTMC based modelling can be used in designing a high throughput parallel BFA decoding system. It is shown that there is a trade-off between the number of BFA decoders and the input buffer size, and an optimal input buffer size can be chosen to minimize the hardware complexity for a target decoding throughput in designing a high throughput parallel BFA decoding system.

Recently, we introduced a bidirectional Fano algorithm (BFA) [10] which can achieve much higher decoding throughput compared to the regular unidirectional Fano algorithm (UFA), especially at low signal-to-noise-ratio (SNR). However, the decoding throughput improvement of the conventional BFA with respect to the UFA reduces as the SNR increases and converges to 100% at high SNR. In this paper, two parameters in the BFA, which are known as the number of merged states (NMS) and the threshold increment value Δ, are exploited to improve the decoding throughout of the conventional BFA. The improved BFA can achieve much higher decoding throughput compared to the UFA and the conventional BFA, especially at high SNR. For example at Eb/N0=5dB, the throughput improvement achieved by the improved BFA is about 280% compared to the UFA and about 80% compared to the conventional BFA, and its computational complexity is only 4% of the Viterbi algorithm.

Various techniques, such as bidirectional search, have been employed in sequential decoding to reduce the decoding delay. In this paper, a bidirectional Fano algorithm (BFA) is proposed, in which a forward decoder (FD) and a backward decoder (BD) search in the opposite direction simultaneously. It is shown that the proposed BFA can reduce the average decoding delay by at least 50% compared to the unidirectional Fano algorithm (UFA). Due to the reduction in the variability of the computational effort by using bidirectional search, there is even higher decoding throughput improvement at low signal-to-noise-ratio (SNR). For example at Eb/No=3dB, there is 300% throughput improvement by using the BFA decoding compared to the conventional UFA decoding. The proposed BFA decoding technique can be employed in very high throughput wireless communication systems with low hardware complexity and power consumption.

One promising approach to peak to average power ratio (PAPR) reduction for orthogonal frequency division multiple access (OFDMA) is the use of reserved tones, which are modulated so as to reduce the PAPR. One problem with this approach is the computationally efficient determination of reserved tone signals that best reduce the PAPR. In this paper we describe a new algorithm for selection of the reserved tone signals. The algorithm results from a differentiable approximation of the PAPR optimisation criterion. We derive a new joint minimisation method capable of reducing multiple peaks simultaneously. It is shown how this can be utilized in a low complexity gradient update algorithm. Simulations show superior performance (lower resultant PAPR) compared to other methods, where system parameters have been motivated by the proposed long term evolution of the 3GPP cellular standard.

Multiuser detection (MUD) strategies have the potential to significantly increase the capacity of wireless communications systems, but for these to be useful they must also be practical for implementation in VLSI circuits. In particular, while the maximum-likelihood (ML) solution is optimal in bit error rate, it cannot be directly implemented due to its exponential computational complexity. Lattice decoders, such as the sphere search, exhibit near-optimal ML performance with reduced complexity, but their application is still limited by computational requirements. Here, a number of optimisations are presented, designed to reduce the computational cost of the sphere search, in the context of VLSI implementation. We also propose parallel implementation strategies for such a detector. This is then combined with a single-pass tree search approach that can be designed to not significantly impair error-rate performance. While the design is targeted towards a MUD application, the concepts may also be applied to a multiple-input multiple-output (MIMO) system, or similar applications.

Finding the nearest point in a lattice is an NP-hard problem for which we desire a simplified architecture. Furthermore, output of confidence measures or soft information is desirable for decoding/detection problems, yet generating such information further complicates the task. This paper presents an efficient and highly parallelisable architecture for a list sphere detector that is particularly suited to ASIC implementation. Various degrees of parallelism are explored and compared. This architecture finds application in multiuser and MIMO detectors, providing soft estimates of the transmitted symbols.

Adaptive iterative receivers which combine multiuser decision-feedback detection with maximum a posteriori (MAP) decoding and soft feedback are presented for synchronous coded direct sequence-code-division multiple access. Both successive and parallel demodulation of users are considered. Optimal filters are derived using both minimum mean squared error and least squares (LS) criteria. The latter assumes short (repeated) spreading codes and that the users to be demodulated simultaneously transmit training sequences. The LS criterion does not require prior knowledge or estimates of spreading codes and channels. Simulation results show that the adaptive receiver can perform significantly better than the standard (soft) interference canceller, since the adaptive algorithm attempts to measure and exploit the second-order statistics between the input and output of the MAP decoder. With limited training, successive feedback and decoding performs significantly better than parallel feedback. The effect of code rate on performance is examined, and reduced-rank versions of the adaptive LS algorithms, which can reduce training overhead, are also presented.

Multiuser decision-feedback detectors (DFDs) for direct-sequence code-division multiple access, based on the minimum mean-squared error (MMSE) performance criterion, are described. Both successive and parallel feedback (interference cancellation) with hard decisions are considered. An iterative DFD is presented, which consists of cascaded DFDs, each performing successive cancellation. The two-stage DFD achieves the single-user bound in the absence of error propagation, and performs significantly better than an MMSE DFD with parallel feedback. The filter structures are generalized to include finite impulse response feedforward and feedback matrix filters, which account for asynchronous users and intersymbol interference. The effect of error propagation is illustrated through simulation. Both uncoded and coded performance results are presented. Although error propagation can significantly degrade performance, the DFDs still offer a significant performance gain relative to linear MMSE detection.

An adaptive receiver which combines multiuser parallel decision feedback with MAP decoding in an iterative manner is presented for synchronous coded DS-CDMA. Performance with various convolutional code rates is considered. optimal filters are derived using both minimum mean squared error (MMSE) and least squares (LS) criteria. When used with short (repeated) spreading codes, the adaptive LS receiver requires only a training sequence and timing for estimation of all filter coefficients. The adaptive receiver performs significantly better than the standard (soft) interference canceller, since the adaptive algorithm attempts to measure and exploit the joint statistics of the output of the MAP decoder with the input symbols. Numerical results are presented which illustrate how performance depends on the rate of the convolutional code, assuming similar decoding complexity. These results show that low code rates perform best at moderate loads and low received power. Higher code rates can achieve very low packet error rates for an overloaded system (number of users greater than the bandwidth expansion).

The general theory of adaptive self reconfigurable interference suppression schemes is applied to several specific practical problems mainly suppression of m-level m amplitude-shift keying, m phase-shift keying (PSK), and m quadrature amplitude modulation signals. This is a practical situation when a code-division multiple-access (CDMA) network is overlaid with standard microwave systems. Another example is a multirate CDMA network where a limited number of high bit rate CDMA signals are allowed to use much higher power level due to lower processing gain. The algorithm is well suited for a modular software radio concept, which we believe, will be more and more accepted in future wireless communications. Further modifications of the schemes necessary for these applications are described, and numerous results are presented to illustrate performance improvements. A general interpretation of these techniques based on so-called multilayer least mean squares (LMS) algorithm is introduced and discussed. The algorithm is based on estimating fast changing interfering signal parameters by using parallel structures, which are fast but complex. At the same time, estimation of slow-varying signal parameters over a large range is accomplished by using an LMS algorithm that is simple but slower. In this way, suppression of the interference occupying the same bandwidth as the CDMA signal is possible with reasonable implementation complexity. For this case, a BER0 dB for binary PSK interference, J/S>17 dB for 8-PSK, J/S>27 dB for 32-PSK, and J/S>40 dB for 128-PSK.

Multiuser decision-feedback detectors (DFDs) for DS-CDMA are presented using the minimum mean squared error (MMSE) criterion. Both successive and parallel feedback (interference cancellation) are considered. Short spreading codes are assumed, eliminating the need for remodulation. A multi-stage DFD is presented which consists of cascaded DFDs each performing successive cancellation. In the absence of error propagation, a two-stage structure achieves the single-user bound for all users, and is shown to be equivalent to an MMSE DFD with parallel feedback. The effect of error propagation is illustrated through simulation. Both uncoded and coded performance results are presented. These results show that the DFDs can offer a significant performance gain relative to linear MMSE detection.

A review of adaptive detection techniques for direct-sequence code division multiple access (CDMA) signals is given. The goal is to improve CDMA system performance and capacity by reducing interference between users. The techniques considered are implementations of multiuser receivers, for which background material is given. Adaptive algorithms improve the feasibility of such receivers. Three main forms of receivers are considered. The minimum mean square error (MMSE) receiver is described and its performance illustrated. Numerous adaptive algorithms can be used to implement the MMSE receiver, including blind techniques, which eliminate the need for training sequences. The adaptive decorrelator can be used to eliminate interference from known interferers, though it is prone to noise enhancement. Multistage and successive interference cancellation techniques reduce interference by cancellation of one detected signal from another. Practical problems and some open research topics are mentioned. These typically relate to the convergence rate and tracking performance of the adaptive algorithm.

Issues relating to the choice of adaptive algorithms for use in single user minimum mean square error (MMSE) direct sequence (DS) code division multiple access (CDMA) receivers are discussed. Such receivers have the potential to dramatically increase capacity of CDMA systems compared to systems with conventional correlator receivers, without the high complexity associated with many proposed multi-user receivers. To be practical for many applications, the convergence rate of the adaptive algorithms must be improved, while maintaining low residual mean-squared error (MSE), robustness to noise and realisable complexity. The slow convergence of the least mean squares (LMS) algorithm is highlighted for this application. Recursive least squares (RLS) algorithms are shown to give much quicker convergence, but have much higher sensitivity to thermal noise. Algorithms normally termed "Fast-RLS", are shown to more complex than the conventional RLS algorithm when used in a CDMA receiver.