The design of an asynchronous BCJR/MAP convolutional channel decoder.

The digital design alternative to the everyday synchronous circuit design paradigm is the asynchronous model. Asynchronous circuits are also known as handshaking circuits and they may prove to be a feasible design alternative in the modern digital Very Large Scale Integration (VLSI) design environment. Asynchronous circuits and systems offer the possibility of lower system power requirements, reduced noise, elimination of clock skew and many other benefits. Channel coding is a useful means of eliminating erroneous transmission due to the communication channel\u27s physical limits. Convolutional coding has come to the forefront of channel coding discussions due to the usefulness of turbo codes. The niche market for turbo codes have typically been in satellite communication. The usefulness of turbo codes are now expanding into the next generation of handheld communication products. It is probable that the turbo coding scheme will reside in the next cellular phone one purchases [1]. Turbo coding uses two BCJR decoders in its implementation. The BCJR decoding algorithm was named after its creators Bahl, Cocke, Jelinek, and Raviv (BCJR). The BCJR algorithm is sometimes known as a Maximum Priori Posteriori (MAP) algorithm. This means a very large part of the turbo coding research will encompass the BCJR/MAP decoder and its optimization for size, power and performance. An investigation into the design of a BCJR/MAP convolutional channel decoder will be introduced. This will encompass the use and synthesis of an asynchronous Hardware Definition Language (HDL) called Balsa. The design will be carried through to the gate implementation level. Proper gate level analysis will identify the key metrics that will determine the feasibility of an asynchronous design of that of the everyday clocked paradigm.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .P47. Source: Masters Abstracts International, Volume: 43-05, page: 1782. Adviser: Kemal Tepe. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Advanced Wireless LAN

The past two decades have witnessed starling advances in wireless LAN technologies that were stimulated by its increasing popularity in the home due to ease of installation, and in commercial complexes offering wireless access to their customers. This book presents some of the latest development status of wireless LAN, covering the topics on physical layer, MAC layer, QoS and systems. It provides an opportunity for both practitioners and researchers to explore the problems that arise in the rapidly developed technologies in wireless LAN

Compute-and-Forward Relay Networks with Asynchronous, Mobile, and Delay-Sensitive Users

We consider a wireless network consisting of multiple source nodes, a set of relays and a destination node. Suppose the sources transmit their messages simultaneously to the relays and the destination aims to decode all the messages. At the physical layer, a conventional approach would be for the relay to decode the individual message one at a time while treating rest of the messages as interference. Compute-and-forward is a novel strategy which attempts to turn the situation around by treating the interference as a constructive phenomenon. In compute-and-forward, each relay attempts to directly compute a combination of the transmitted messages and then forwards it to the destination. Upon receiving the combinations of messages from the relays, the destination can recover all the messages by solving the received equations. When identical lattice codes are employed at the sources, error correction to integer combination of messages is a viable option by exploiting the algebraic structure of lattice codes. Therefore, compute-and-forward with lattice codes enables the relay to manage interference and perform error correction concurrently. It is shown that compute-and-forward exhibits substantial improvement in the achievable rate compared with other state-of-the-art schemes for medium to high signal-to-noise ratio regime. Despite several results that show the excellent performance of compute-and-forward, there are still important challenges to overcome before we can utilize compute-and- forward in practice. Some important challenges include the assumptions of \perfect timing synchronization "and \quasi-static fading", since these assumptions rarely hold in realistic wireless channels. So far, there are no conclusive answers to whether compute-and-forward can still provide substantial gains even when these assumptions are removed. When lattice codewords are misaligned and mixed up, decoding integer combination of messages is not straightforward since the linearity of lattice codes is generally not invariant to time shift. When channel exhibits time selectivity, it brings challenges to compute-and-forward since the linearity of lattice codes does not suit the time varying nature of the channel. Another challenge comes from the emerging technologies for future 5G communication, e.g., autonomous driving and virtual reality, where low-latency communication with high reliability is necessary. In this regard, powerful short channel codes with reasonable encoding/decoding complexity are indispensable. Although there are fruitful results on designing short channel codes for point-to-point communication, studies on short code design specifically for compute-and-forward are rarely found. The objective of this dissertation is threefold. First, we study compute-and-forward with timing-asynchronous users. Second, we consider the problem of compute-and- forward over block-fading channels. Finally, the problem of compute-and-forward for low-latency communication is studied. Throughout the dissertation, the research methods and proposed remedies will center around the design of lattice codes in order to facilitate the use of compute-and-forward in the presence of these challenges

The SoftPHY Abstraction: from Packets to Symbols in Wireless Network Design

At ever-increasing rates, we are using wireless systems to communicatewith others and retrieve content of interest to us. Current wirelesstechnologies such as WiFi or Zigbee use forward error correction todrive bit error rates down when there are few interferingtransmissions. However, as more of us use wireless networks toretrieve increasingly rich content, interference increases inunpredictable ways. This results in errored bits, degradedthroughput, and eventually, an unusable network. We observe that thisis the result of higher layers working at the packet granularity,whereas they would benefit from a shift in perspective from wholepackets to individual symbols.From real-world experiments on a 31-node testbed of Zigbee andsoftware-defined radios, we find that often, not all of the bitsin corrupted packets share fate. Thus, today's wireless protocolsretransmit packets where only a small number of the constituent bitsin a packet are in error, wasting network resources. In thisdissertation, we will describe a physical layer that passesinformation about its confidence in each decoded symbol up to higherlayers. These SoftPHY hints have many applications, one ofwhich, more efficient link-layer retransmissions, we will describe indetail. PP-ARQ is a link-layer reliable retransmission protocolthat allows a receiver to compactly encode a request forretransmission of only the bits in a packet that are likely in error.Our experimental results show that PP-ARQ increases aggregate networkthroughput by a factor of approximately 2x under variousconditions. Finally, we will place our contributions in the contextof related work and discuss other uses of SoftPHY throughout thewireless networking stack

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems