Multiuser Scheduling for Cognitive MIMO with Channel Estimation Errors and Feedback Delay

A multiuser scheduling multiple-input multiple-output (MIMO) cognitive radio network (CRN) with space-time block coding (STBC) is considered in this paper, where one secondary base station (BS) communicates with one secondary user (SU) selected from K candidates. The joint impact of imperfect channel state information (CSI) in BS → SUs and BS → PU due to channel estimation errors and feedback delay on the outage performance is firstly investigated. We obtain the exact outage probability expressions for the considered network under the peak interference power IP at PU and maximum transmit power Pm at BS which cover perfect/imperfect CSI scenarios in BS → SUs and BS → PU. In addition, asymptotic expressions of outage probability in high SNR region are also derived from which we obtain several important insights into the system design. For example, only with perfect CSIs in BS → SUs, i.e., without channel estimation errors and feedback delay, the multiuser diversity can be exploited. Finally, simulation results confirm the correctness of our analysis.

Generalized Selection Combining for Cognitive Relay Networks over Nakagami-m Fading

We consider transmit antenna selection with receive generalized selection combining (TAS/GSC) for cognitive decodeand-forward (DF) relaying in Nakagami-m fading channels. In an effort to assess the performance, the probability density function and the cumulative distribution function of the endto-end SNR are derived using the moment generating function, from which new exact closed-form expressions for the outage probability and the symbol error rate are derived. We then derive a new closed-form expression for the ergodic capacity. More importantly, by deriving the asymptotic expressions for the outage probability and the symbol error rate, as well as the high SNR approximations of the ergodic capacity, we establish new design insights under the two distinct constraint scenarios: 1) proportional interference power constraint, and 2) fixed interference power constraint. Several pivotal conclusions are reached. For the first scenario, the full diversity order of the
outage probability and the symbol error rate is achieved, and the high SNR slope of the ergodic capacity is 1/2. For the second scenario, the diversity order of the outage probability and the symbol error rate is zero with error floors, and the high SNR slope of the ergodic capacity is zero with capacity ceiling.

Fourier Rotman Lens Enabled Directional Modulation Transmitter

A 10 GHz Fourier Rotman lens enabled dynamic directional modulation (DM) transmitter is experimentally evaluated. Bit error rate (BER) performance is obtained via real-time data transmission. It is shown that Fourier Rotman DM functionality enhances system security performance in terms of narrower decodable low BER region and higher BER values associated with BER sidelobes especially under high signal to noise ratio (SNR) scenarios. This enhancement is achieved by controlled corruption of constellation diagrams in IQ space by orthogonal injection of interference. Furthermore, the paper gives the first report of a functional dual-beam DM transmitter, which has the capability of simultaneously projecting two independent data streams into two different spatial directions while simultaneously scrambling the information signals along all other directions.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

A mixed cost-function adaptive algorithm for ADSL time-domain equalization

We propose a mixed cost-function adaptive initialization algorithm for the time domain equalizer in a discrete multitone (DMT)-based asymmetric digital subscriber line. Using our approach, a higher convergence rate than that of the commonly used least-mean square algorithm is obtained, whilst attaining bit rates close to the optimum maximum shortening SNR and the upper bound SNR. Furthermore, our proposed method outperforms the minimum mean-squared error design for a range of time domain equalizer (TEQ) filter lengths. The improved performance outweighs the small increase in computational complexity required. A block variant of our proposed algorithm is also presented to overcome the increased latency imposed on the feedback path of the adaptive system.

Hierarchical Demand Response for Peak Reduction using Dantzig‐Wolfe Decomposition

Demand Response (DR) algorithms manipulate the energy consumption schedules of controllable loads so as to satisfy grid objectives. Implementation of DR algorithms using a centralised agent can be problematic for scalability reasons, and there are issues related to the privacy of data and robustness to communication failures. Thus it is desirable to use a scalable decentralised algorithm for the implementation of DR. In this paper, a hierarchical DR scheme is proposed for Peak Minimisation (PM) based on Dantzig-Wolfe Decomposition (DWD). In addition, a Time Weighted Maximisation option is included in the cost function which improves the Quality of Service for devices seeking to receive their desired energy sooner rather than later. The paper also demonstrates how the DWD algorithm can be implemented more efficiently through the calculation of the upper and lower cost bounds after each DWD iteration.

An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion

This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement.

Speech Enhancement from Additive Noise and Channel Distortion - a Corpus-Based Approach

This paper presents a new approach to single-channel speech enhancement involving both noise and channel distortion (i.e., convolutional noise). The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise. Third, we present an iterative algorithm for improved speech estimates. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. Index Terms: corpus-based speech model, longest matching segment, speech enhancement, speech recognition

Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

Cooperative Beamforming and User Selection for Improving the Security of Relay-aided Systems

A relay network in which a source wishes to convey a confidential message to a legitimate destination with the assistance of trusted relays is considered. In particular, cooperative beamforming and user selection techniques are applied to protect the confidential message. The secrecy rate (SR) and secrecy outage probability (SOP) of the network are investigated first, and a tight upper bound for the SR and an exact formula for the SOP are derived. Next, asymptotic approximations for the SR and SOP in the high signal-to-noise ratio (SNR) regime are derived for two different schemes: i) cooperative beamforming and ii) multiuser selection. Further, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), which can be used to evaluate security level of a cooperative relaying network, is investigated. It is shown that the ACDG of cooperative beamforming is equal to the conventional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG of the multiuser scenario is equal to that of traditional single-input multiple-output networks.

Statistical Eigenmode Transmission for the MU-MIMO Downlink in Rician Fading

In this paper, we study the achievable ergodic sum-rate of multiuser multiple-input multiple-output downlink systems in Rician fading channels. We first derive a lower bound on the average signal-to-leakage-and-noise ratio by using the Mullen’s inequality, and then use it to analyze the effect of channel mean information on the achievable ergodic sum-rate. A novel statistical-eigenmode space-division multiple-access (SESDMA) downlink transmission scheme is then proposed. For this scheme, we derive an exact analytical closed-form expression for the achievable ergodic rate and present tractable tight upper and lower bounds. Based on our analysis, we gain valuable insights into the system parameters, such as the number of transmit antennas, the signal-to-noise ratio (SNR) and Rician K-factor on the system sum-rate. Results show that the sum-rate converges to a saturation value in the high SNR regime and tends to a lower limit for the low Rician K-factor case. In addition, we compare the achievable ergodic sum-rate between SE-SDMA and zeroforcing beamforming with perfect channel state information at the base station. Our results reveal that the rate gap tends to zero in the high Rician K-factor regime. Finally, numerical results are presented to validate our analysis.

Repeatability and reliability of new air and water permeability tests for assessing the durability of high-performance concretes

This paper reports on the accuracy of new test methods developed to measure the air and water permeability of high-performance concretes (HPCs). Five representative HPC and one normal concrete (NC) mixtures were tested to estimate both repeatability and reliability of the proposed methods. Repeatability acceptance was adjudged using values of signal-noise ratio (SNR) and discrimination ratio (DR), and reliability was investigated by comparing against standard laboratory-based test methods (i.e., the RILEM gas permeability test and BS EN water penetration test). With SNR and DR values satisfying recommended criteria, it was concluded that test repeatability error has no significant influence on results. In addition, the research confirmed strong positive relationships between the proposed test methods and existing standard permeability assessment techniques. Based on these findings, the proposed test methods show strong potential to become recognized as international methods for determining the permeability of HPCs.

An effective key generation system using improved channel reciprocity

In physical layer security systems there is a clear need to exploit the radio link characteristics to automatically generate an encryption key between two end points. The success of the key generation depends on the channel reciprocity, which is impacted by the non-simultaneous measurements and the white nature of the noise. In this paper, an OFDM subcarriers' channel responses based key generation system with enhanced channel reciprocity is proposed. By theoretically modelling the OFDM subcarriers' channel responses, the channel reciprocity is modelled and analyzed. A low pass filter is accordingly designed to improve the channel reciprocity by suppressing the noise. This feature is essential in low SNR environments in order to reduce the risk of the failure of the information reconciliation phase during key generation. The simulation results show that the low pass filter improves the channel reciprocity, decreases the key disagreement, and effectively increases the success of the key generation.

I/Q imbalance in AF dual-hop relaying: Performance analysis in Nakagami-m fading

We analyze the performance of amplify-and-forward dual-hop relaying systems in the presence of in-phase and quadrature-phase imbalance (IQI) at the relay node. In particular, an exact analytical expression for and tight lower bounds on the outage probability are derived over independent, non-identically distributed Nakagami-m fading channels. Moreover, tractable upper and lower bounds on the ergodic capacity are presented at arbitrary signal-to-noise ratios (SNRs). Some special cases of practical interest (e.g., Rayleigh and Nakagami-0.5 fading) are also studied. An asymptotic analysis is performed in the high SNR regime, where we observe that IQI results in a ceiling effect on the signal-to-interference-plus-noise ratio (SINR), which depends only on the level of I/Q impairments, i.e., the joint image rejection ratio. Finally, the optimal I/Q amplitude and phase mismatch parameters are provided for maximizing the SINR ceiling, thus improving the system performance. An interesting observation is that, under a fixed total phase mismatch constraint, it is optimal to have the same level of transmitter (TX) and receiver (RX) phase mismatch at the relay node, while the optimal values for the TX and RX amplitude mismatch should be inversely proportional to each other.

Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.