Desmond C. McLernon

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

A Joint TOA/AOA Constrained Minimization Method for Locating Wireless Devices in Non-Line-of-Sight Environment

Non-line-of-sight (NLOS) propagation degrades the performance of wireless location systems. Thus, developing algorithms that are robust to NLOS is of great importance. This paper introduces a new location technique that utilizes time of arrival (TOA) and angle of arrival (AOA) measurements. In the proposed method, we assume the signal from the mobile station reaches each base station via one dominant scatterer. By including the scatterers coordinates as unknowns in a TOA/AOA-based cost function and imposing some equality and inequality constrains, the location of the mobile station (MS) is shown to significantly improve. The performance of the proposed algorithm is assessed and compared with that of existing algorithms through extensive simulations.

Exact Evaluation of Ergodic Capacity for Multihop Variable-Gain Relay Networks: A Unified Framework for Generalized Fading Channels

The closed-form expressions of the probability density function (pdf) and the moment-generating function for the exact end-to-end signal-to-noise ratio (SNR) of variable-gain relay networks are unknown for generalized fading models; thus, the exact evaluation for the ergodic capacity of these systems is cumbersome. In this paper, we develop a new unified framework for the exact ergodic capacity of multihop networks equipped with variable-gain relays, assuming that channel-state information (CSI) is available only at the receiving terminals. The resulting expression (which is based on the exact end-to-end SNR) is in the form of a single truncated infinite series and is valid for an arbitrary number of hops and for various fading models that are typically encountered in realistic scenarios. Furthermore, we show that the exact ergodic capacity of dual-hop variable-gain relay networks can be written in terms of the ergodic capacities of two equivalent single-input-single-output (SISO) channels and one single-input-multiple-output channel (SIMO), the closed-form expressions of which are already available for many commonly used fading/shadowing models. Finally, we show that there is an exact match between the Monte Carlo simulation and theoretical results over a whole range of per-hop average SNR.

SVD-based joint azimuth/elevation estimation with automatic pairing

In this paper, a joint azimuth/elevation estimator with automatic pairing is developed. Two-dimensional (2-D) angle of arrival (AOA) estimation is useful in space processing systems and wireless location systems that employ AOA technology. The estimator makes use of a special setup of the received signal at an L-shaped antenna array element organized especially for the estimation process. The estimator is based on applying the singular value decomposition (SVD) algorithm to a cross-correlation matrix that is constructed from both arrays of the L-shaped structure. The proposed method avoids the computational burden of the complex pair-matching procedure. Simulations of the proposed method are shown to assess its performance.

PHY Layer Security Based on Protected Zone and Artificial Noise

We address physical layer security in multiple- input-multiple-output (MISO) communications in the presence of an unknown passive eavesdropper. Beamforming and artificial noise broadcasting are chosen to increase communications security. We first study the effect of a close eavesdropper on security and then we define a “Protected Zone” in the transmitters vicinity. We present an optimisation strategy that intelligently sets the transmission power and the size of the protected zone to probabilistically achieve secrecy at a specified target secrecy rate. The results show that this strategy can achieve a high probability of secrecy by efficiently prioritising the use of the available resources.

Deep learning approach for Network Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

Optimized training and basis expansion model parameters for doubly-selective channel estimation

We address the problem of estimating doubly-selective channels using pilot clusters that are time-division multiplexed with the data. The pilot clusters consist of zero-padded pilot symbols in order to decouple channel estimation from data detection. Channel estimation is carried out using the basis expansion model (BEM-)based method, where different BEMs are investigated, and the exact MMSE method which requires full knowledge of the channel statistics. For a fixed number of pilot symbols, we attempt to optimize the power and placement of the pilot symbols at the transmitter side used in transmission, and the number of BEM coefficients used in channel estimation, in the sense of minimizing the total mean-square estimation error (MSE) that includes modelling error. Simulation results confirm that for a wide range of SNR and Doppler spread values, this optimization greatly reduces the MSE and the bit-error rate, and that modelling error, which was ignored in existing work on training design, should be taken into account. The effects of uncertainty in the channel statistics are also studied.

Frame/Training Sequence Synchronization and DC-Offset Removal for (Data-Dependent) Superimposed Training Based Channel Estimation

Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-bearing signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

Cochannel interference estimation for M-ary PSK modulated signals

Although the signal-to-interference power ratio (SIR) based criterion for diversity reception has been highlighted in the literature as the most effective method for reducing the effects of cochannel interference, it is often not clear how to measure this parameter in a practical context. This work addresses the problem of finding a way to determine the level of interference (due to frequency reuse) from neighbouring cells in a mobile telephone system, with M-ary PSK modulated signals and fast fading channel. The proposed parameter measures the quality of the received signal corrupted by additive white noise and cochannel interference, for any number of interferers. It is also suitable for high bit rate transmission, as in indoor mobile radio systems, since the proposed method requires sampling the received signal at just one sample/symbol.

Designing stable extrapolators for explicit depth extrapolation of 2D and 3D wavefields using projections onto convex sets

We have developed a robust algorithm for designing explicit depth extrapolation operators using the projections-onto-convex-sets (POCS) method. The operators are optimal in the sense that they satisfy all required extrapolation design characteristics. In addition, we propose a simple modification of the POCS algorithm (modified POCS, or MPOCS) that further enhances the stability of extrapolated wavefields and reduces the number of iterations required to design such operators to approximately 2% of that required for the basic POCS design algorithm. Various synthetic tests show that 25-coefficient 1D extrapolation operators, which have 13 unique coefficients, can accommodate dip angles up to 70°. We migrated the SEG/EAGE salt model data with the operators and compare our results with images obtained via extrapolators based on modified Taylor series and with wavefield extrapolation techniques such as phase shift plus interpolation (PSPI) and split-step Fourier. The MPOCS algorithm provides practically stable...