Multipacket reception in the presence of in-band full-duplex communication

In-Band Full-DupleX (IB-FDX) is defined as the ability for nodes to transmit and receive signals simultaneously on the same channel. Conventional digital wireless networks do not implement it, since a node’s own transmission signal causes interference to the signal it is trying to receive. However, recent studies attempt to overcome this obstacle, since it can potentially double the spectral efficiency of current wireless networks. Different mechanisms exist today that are able to reduce a significant part of the Self- Interference (SI), although specially tuned Medium Access Control (MAC) protocols are required to optimize its use. One of IB-FDX’s biggest problems is that the nodes’ interference range is extended, meaning the unusable space for other transmissions and receptions is broader. This dissertation proposes using MultiPacket Reception (MPR) to address this issue and adapts an already existing Single-Carrier with Frequency-Domain Equalization (SC-FDE) receiver to IB-FDX. The performance analysis suggests that MPR and IB-FDX have a strong synergy and are able to achieve higher data rates, when used together. Using analytical models, the optimal transmission patterns and transmission power were identified, which maximize the channel capacity with the minimal energy consumption. This was used to define a new MAC protocol, named Full-duplex Multipacket reception Medium Access Control (FM-MAC). FM-MAC was designed for a single-hop cellular infrastructure, where the Access Point (AP) and the terminals implement both IB-FDX and MPR. It divides the coverage range of the AP into a closer Full-DupleX (FDX) zone and a farther Half-DupleX (HDX) zone and adds a tunable fairness mechanism to avoid terminal starvation. Simulation results show that this protocol provides efficient support for both HDX and FDX terminals, maximizing its capacity when more FDX terminals are used

Novel Complex Adaptive Signal Processing Techniques Employing Optimally Derived Time-varying Convergence Factors With Applicatio

In digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive system coefficients at each iteration. In addition, the real and imaginary components of the complex signal and complex adaptive filter coefficients are treated as separate entities, and are independently updated. As a result, the developed methods efficiently utilize the degrees of freedom of the adaptive system, thereby exhibiting improved convergence characteristics, even in dynamic environments. In wireless communications, acceptable co-channel, adjacent channel, and image interference rejection is often one of the most critical requirements for a receiver. In this regard, the fixed-point complex Independent Component Analysis (ICA) algorithm, called Complex FastICA, has been previously applied to realize digital blind interference suppression in stationary or slow fading environments. However, under dynamic flat fading channel conditions frequently encountered in practice, the performance of the Complex FastICA is significantly degraded. In this dissertation, novel complex block adaptive ICA algorithms employing optimal convergence factors are presented, which exhibit superior convergence speed and accuracy in time-varying flat fading channels, as compared to the Complex FastICA algorithm. The proposed algorithms are called Complex IA-ICA, Complex OBA-ICA, and Complex CBC-ICA. For adaptive filtering applications, the Complex Least Mean Square algorithm (Complex LMS) has been widely used in both block and sequential form, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence and dependence on the choice of the convergence factor. In this research, novel block and sequential based algorithms for complex adaptive digital filtering are presented, which overcome the inherent limitations of the existing Complex LMS. The block adaptive algorithms are called Complex OBA-LMS and Complex OBAI-LMS, and their sequential versions are named Complex HA-LMS and Complex IA-LMS, respectively. The performance of the developed techniques is tested in various adaptive filtering applications, such as channel estimation, and adaptive beamforming. The combination of Orthogonal Frequency Division Multiplexing (OFDM) and the Multiple-Input-Multiple-Output (MIMO) technique is being increasingly employed for broadband wireless systems operating in frequency selective channels. However, MIMO-OFDM systems are extremely sensitive to Intercarrier Interference (ICI), caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver. This results in crosstalk between the various OFDM subcarriers resulting in severe deterioration in performance. In order to mitigate this problem, the previously proposed Complex OBA-ICA algorithm is employed to recover user signals in the presence of ICI and channel induced mixing. The effectiveness of the Complex OBA-ICA method in performing ICI mitigation and signal separation is tested for various values of CFO, rate of channel variation, and Signal to Noise Ratio (SNR)

Time diversity solutions to cope with lost packets

A dissertation submitted to Departamento de Engenharia Electrotécnica of Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engenharia Electrotécnica e de ComputadoresModern broadband wireless systems require high throughputs and can also have very high Quality-of-Service (QoS) requirements, namely small error rates and short delays. A high spectral efficiency is needed to meet these requirements. Lost packets, either due to errors or collisions, are usually discarded and need to be retransmitted, leading to performance degradation. An alternative to simple retransmission that can improve both power and spectral efficiency is to combine the signals associated to different transmission attempts. This thesis analyses two time diversity approaches to cope with lost packets that are relatively similar at physical layer but handle different packet loss causes. The first is a lowcomplexity Diversity-Combining (DC) Automatic Repeat reQuest (ARQ) scheme employed in a Time Division Multiple Access (TDMA) architecture, adapted for channels dedicated to a single user. The second is a Network-assisted Diversity Multiple Access (NDMA) scheme, which is a multi-packet detection approach able to separate multiple mobile terminals transmitting simultaneously in one slot using temporal diversity. This thesis combines these techniques with Single Carrier with Frequency Division Equalizer (SC-FDE) systems, which are widely recognized as the best candidates for the uplink of future broadband wireless systems. It proposes a new NDMA scheme capable of handling more Mobile Terminals (MTs) than the user separation capacity of the receiver. This thesis also proposes a set of analytical tools that can be used to analyse and optimize the use of these two systems. These tools are then employed to compare both approaches in terms of error rate, throughput and delay performances, and taking the implementation complexity into consideration. Finally, it is shown that both approaches represent viable solutions for future broadband wireless communications complementing each other.Fundação para a Ciência e Tecnologia - PhD grant(SFRH/BD/41515/2007); CTS multi-annual funding project PEst-OE/EEI/UI0066/2011, IT pluri-annual funding project PEst-OE/EEI/LA0008/2011, U-BOAT project PTDC/EEATEL/ 67066/2006, MPSat project PTDC/EEA-TEL/099074/2008 and OPPORTUNISTICCR project PTDC/EEA-TEL/115981/200

Exploiting independence for co-channel interference cancellation and symbol detection in multiuser digital communications

In the blind equalization of multi-input multi-output (MIMO) fi-nite impulse response communication channels, co-channel inter-ference (CCI) is typically cancelled by exploiting the properties of digital modulations, such as their finite alphabet (FA). This contri-bution takes advantage of the mutual independence of the users’ signals through the application of independent component ana-lysis (ICA). We demonstrate that ICA-based CCI suppression re-markably improves an FA-based approach. In addition, proposed ICA-assisted minimum mean square error receivers are shown to enhance the conventional detection capabilities of MIMO equal-ization methods relying on channel identification. The particular structure of the MIMO model yields a simplified detection scheme with improved performance at a reduced computational cost. 1

Root-MUSIC-based methods for blind network-assisted diversity multiple access

Packet collisions in wireless networks degrade the throughput and impede the system performance. The collided packets are typically corrupted and get discarded. Channelization methods avoid collisions through fixed assignment of communication resources to the system users, but they do not take into account the randomness of packet arrivals. Statistical multiplexing optimally adapts the allocation of resources to the instantaneous traffic demands of the users. However, it is only possible in the downlink wherein the data streams are managed by one station. Random-access methods mimic statistical multiplexing by dynamically assigning resources to users. A slot is wasted if the channel incurs a collision, and the collided packets have to be retransmitted. First, we present a cross-layer design for providing multiple access to a shared wireless link. While retransmissions are controlled by the medium access control (MAC) layer, this creates sufficient diversity to recover the collided packets in the physical (PHY) layer. Both the number and identities of the involved transmitters in a collision are unknown to the receiver. The signal separation is done blindly using root-MUSIC-like algorithms. We solve the collision resolution problem in four network-operation modes: synchronous blocking mode, synchronous non-blocking mode, asynchronous blocking mode and asynchronous non-blocking mode. Second, we evaluate the decoding performance of the algorithms in block-fading channels with additive white Gaussian noise. We analytically demonstrate the effect of signal-to-noise ratio and the number of retransmissions on the signal separation capability of the proposed methods for a given number of collided packets. Third, we evaluate the network throughput and mean packet queueing delay for the proposed collision resolution algorithms analytically and numerically. We derive conditions for stability of the queueing network as function of the mean packet arrival rates.Electrical and Computer Engineerin

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig