Rapid Industrial Prototyping and SoC Design of 3G/4G Wireless Systems Using an HLS Methodology

Many very-high-complexity signal processing algorithms are required in future wireless systems, giving tremendous challenges to real-time implementations. In this paper, we present our industrial rapid prototyping experiences on 3G/4G wireless systems using advanced signal processing algorithms in MIMO-CDMA and MIMO-OFDM systems. Core system design issues are studied and advanced receiver algorithms suitable for implementation are proposed for synchronization, MIMO equalization, and detection. We then present VLSI-oriented complexity reduction schemes and demonstrate how to interact these high-complexity algorithms with an HLS-based methodology for extensive design space exploration. This is achieved by abstracting the main effort from hardware iterations to the algorithmic C/C++ fixed-point design. We also analyze the advantages and limitations of the methodology. Our industrial design experience demonstrates that it is possible to enable an extensive architectural analysis in a short-time frame using HLS methodology, which significantly shortens the time to market for wireless systems.National Science Foundatio

Flexible N-Way MIMO Detector on GPU

This paper proposes a flexible Multiple-Input Multiple-Output (MIMO) detector on graphics processing units (GPU). MIMO detection is a key technology in broadband wireless system such as LTE,WiMAX, and 802.11n. Existing detectors either use costly sorting for better performance or sacrifice sorting for higher throughput. To achieve good performance with high thoughput, our detector runs multiple search passes in parallel, where each search pass detects the transmit stream with a different permuted detection order. We show that this flexible detector, including QR decomposition preprocessing, outperforms existing GPU MIMO detectors while maintaining good bit error rate (BER) performance. In addition, this detector can achieve different tradeoffs between throughput and accuracy by changing the number of parallel search passes.National Science Foundation (NSF

On Development of Some Soft Computing Based Multiuser Detection Techniques for SDMA–OFDM Wireless Communication System

Space Division Multiple Access(SDMA) based technique as a subclass of Multiple Input Multiple Output (MIMO) systems achieves high spectral efficiency through bandwidth reuse by multiple users. On the other hand, Orthogonal Frequency Division Multiplexing (OFDM) mitigates the impairments of the propagation channel. The combination of SDMA and OFDM has emerged as a most competitive technology for future wireless communication system. In the SDMA uplink, multiple users communicate simultaneously with a multiple antenna Base Station (BS) sharing the same frequency band by exploring their unique user specific-special spatial signature. Different Multiuser Detection (MUD) schemes have been proposed at the BS receiver to identify users correctly by mitigating the multiuser interference. However, most of the classical MUDs fail to separate the users signals in the over load scenario, where the number of users exceed the number of receiving antennas. On the other hand, due to exhaustive search mechanism, the optimal Maximum Likelihood (ML) detector is limited by high computational complexity, which increases exponentially with increasing number of simultaneous users. Hence, cost function minimization based Minimum Error Rate (MER) detectors are preferred, which basically minimize the probability of error by iteratively updating receiver’s weights using adaptive algorithms such as Steepest Descent (SD), Conjugate Gradient (CG) etc. The first part of research proposes Optimization Techniques (OTs) aided MER detectors to overcome the shortfalls of the CG based MER detectors. Popular metaheuristic search algorithms like Adaptive Genetic Algorithm (AGA), Adaptive Differential Evolution Algorithm (ADEA) and Invasive Weed Optimization (IWO), which rely on an intelligent search of a large but finite solution space using statistical methods, have been applied for finding the optimal weight vectors for MER MUD. Further, it is observed in an overload SDMA–OFDM system that the channel output phasor constellation often becomes linearly non-separable. With increasing the number of users, the receiver weight optimization task turns out to be more difficult due to the exponentially increased number of dimensions of the weight matrix. As a result, MUD becomes a challenging multidimensional optimization problem. Therefore, signal classification requires a nonlinear solution. Considering this, the second part of research work suggests Artificial Neural Network (ANN) based MUDs on thestandard Multilayer Perceptron (MLP) and Radial Basis Function (RBF) frameworks fo

Uncoded space-time labelling diversity : data rate & reliability enhancements and application to real-world satellite broadcasting.

Doctoral Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF

Low-Complexity Algorithms for Channel Estimation in Optimised Pilot-Assisted Wireless OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has recently become a dominant transmission technology considered for the next generation fixed and mobile broadband wireless communication systems. OFDM has an advantage of lessening the severe effects of the frequency-selective (multipath) fading due to the band splitting into relatively flat fading subchannels, and allows for low-complexity transceiver implementation based on the fast Fourier transform algorithms. Combining OFDM modulation with multilevel frequency-domain symbol mapping (e.g., QAM) and spatial multiplexing (SM) over the multiple-input multiple-output (MIMO) channels, can theoretically achieve near Shannon capacity of the communication link. However, the high-rate and spectrumefficient system implementation requires coherent detection at the receiving end that is possible only when accurate channel state information (CSI) is available. Since in practice, the response of the wireless channel is unknown and is subject to random variation with time, the receiver typically employs a channel estimator for CSI acquisition. The channel response information retrieved by the estimator is then used by the data detector and can also be fed back to the transmitter by means of in-band or out-of-band signalling, so the latter could adapt power loading, modulation and coding parameters according to the channel conditions. Thus, design of an accurate and robust channel estimator is a crucial requirement for reliable communication through the channel, which is selective in time and frequency. In a MIMO configuration, a separate channel estimator has to be associated with each transmit/receive antenna pair, making the estimation algorithm complexity a primary concern. Pilot-assisted methods, relying on the insertion of reference symbols in certain frequencies and time slots, have been found attractive for identification of the doubly-selective radio channels from both the complexity and performance standpoint. In this dissertation, a family of the reduced-complexity estimators for the single and multiple-antenna OFDM systems is developed. The estimators are based on the transform-domain processing and have the same order of computational complexity, irrespective of the number of pilot subcarriers and their positioning. The common estimator structure represents a cascade of successive small-dimension filtering modules. The number of modules, as well as their order inside the cascade, is determined by the class of the estimator (one or two-dimensional) and availability of the channel statistics (correlation and signal-to-noise power ratio). For fine precision estimation in the multipath channels with statistics not known a priori, we propose recursive design of the filtering modules. Simulation results show that in the steady state, performance of the recursive estimators approaches that of their theoretical counterparts, which are optimal in the minimum mean square error (MMSE) sense. In contrast to the majority of the channel estimators developed so far, our modular-type architectures are suitable for the reconfigurable OFDM transceivers where the actual channel conditions influence the decision of what class of filtering algorithm to use, and how to allot pilot subcarrier positions in the band. In the pilot-assisted transmissions, channel estimation and detection are performed separately from each other over the distinct subcarrier sets. The estimator output is used only to construct the detector transform, but not as the detector input. Since performance of both channel estimation and detection depends on the signal-to-noise power vi ratio (SNR) at the corresponding subcarriers, there is a dilemma of the optimal power allocation between the data and the pilot symbols as these are conflicting requirements under the total transmit power constraint. The problem is exacerbated by the variety of channel estimators. Each kind of estimation algorithm is characterised by its own SNR gain, which in general can vary depending on the channel correlation. In this dissertation, we optimise pilot-data power allocation for the case of developed low-complexity one and two-dimensional MMSE channel estimators. The resultant contribution is manifested by the closed-form analytical expressions of the upper bound (suboptimal approximate value) on the optimal pilot-to-data power ratio (PDR) as a function of a number of design parameters (number of subcarriers, number of pilots, number of transmit antennas, effective order of the channel model, maximum Doppler shift, SNR, etc.). The resultant PDR equations can be applied to the MIMO-OFDM systems with arbitrary arrangement of the pilot subcarriers, operating in an arbitrary multipath fading channel. These properties and relatively simple functional representation of the derived analytical PDR expressions are designated to alleviate the challenging task of on-the-fly optimisation of the adaptive SM-MIMO-OFDM system, which is capable of adjusting transmit signal configuration (e.g., block length, number of pilot subcarriers or antennas) according to the established channel conditions

Novel LDPC coding and decoding strategies: design, analysis, and algorithms

In this digital era, modern communication systems play an essential part in nearly every aspect of life, with examples ranging from mobile networks and satellite communications to Internet and data transfer. Unfortunately, all communication systems in a practical setting are noisy, which indicates that we can either improve the physical characteristics of the channel or find a possible systematical solution, i.e. error control coding. The history of error control coding dates back to 1948 when Claude Shannon published his celebrated work “A Mathematical Theory of Communication”, which built a framework for channel coding, source coding and information theory. For the first time, we saw evidence for the existence of channel codes, which enable reliable communication as long as the information rate of the code does not surpass the so-called channel capacity. Nevertheless, in the following 60 years none of the codes have been proven closely to approach the theoretical bound until the arrival of turbo codes and the renaissance of LDPC codes. As a strong contender of turbo codes, the advantages of LDPC codes include parallel implementation of decoding algorithms and, more crucially, graphical construction of codes. However, there are also some drawbacks to LDPC codes, e.g. significant performance degradation due to the presence of short cycles or very high decoding latency. In this thesis, we will focus on the practical realisation of finite-length LDPC codes and devise algorithms to tackle those issues. Firstly, rate-compatible (RC) LDPC codes with short/moderate block lengths are investigated on the basis of optimising the graphical structure of the tanner graph (TG), in order to achieve a variety of code rates (0.1 < R < 0.9) by only using a single encoder-decoder pair. As is widely recognised in the literature, the presence of short cycles considerably reduces the overall performance of LDPC codes which significantly limits their application in communication systems. To reduce the impact of short cycles effectively for different code rates, algorithms for counting short cycles and a graph-related metric called Extrinsic Message Degree (EMD) are applied with the development of the proposed puncturing and extension techniques. A complete set of simulations are carried out to demonstrate that the proposed RC designs can largely minimise the performance loss caused by puncturing or extension. Secondly, at the decoding end, we study novel decoding strategies which compensate for the negative effect of short cycles by reweighting part of the extrinsic messages exchanged between the nodes of a TG. The proposed reweighted belief propagation (BP) algorithms aim to implement efficient decoding, i.e. accurate signal reconstruction and low decoding latency, for LDPC codes via various design methods. A variable factor appearance probability belief propagation (VFAP-BP) algorithm is proposed along with an improved version called a locally-optimized reweighted (LOW)-BP algorithm, both of which can be employed to enhance decoding performance significantly for regular and irregular LDPC codes. More importantly, the optimisation of reweighting parameters only takes place in an offline stage so that no additional computational complexity is required during the real-time decoding process. Lastly, two iterative detection and decoding (IDD) receivers are presented for multiple-input multiple-output (MIMO) systems operating in a spatial multiplexing configuration. QR decomposition (QRD)-type IDD receivers utilise the proposed multiple-feedback (MF)-QRD or variable-M (VM)-QRD detection algorithm with a standard BP decoding algorithm, while knowledge-aided (KA)-type receivers are equipped with a simple soft parallel interference cancellation (PIC) detector and the proposed reweighted BP decoders. In the uncoded scenario, the proposed MF-QRD and VM-QRD algorithms are shown to approach optimal performance, yet require a reduced computational complexity. In the LDPC-coded scenario, simulation results have illustrated that the proposed QRD-type IDD receivers can offer near-optimal performance after a small number of detection/decoding iterations and the proposed KA-type IDD receivers significantly outperform receivers using alternative decoding algorithms, while requiring similar decoding complexity

A Study on High Performance Gbps MIMO Wireless System

九州工業大学博士学位論文 学位記番号:情工博甲第294号　学位授与年月日:平成26年12月25日1 Introduction||2 Wireless System Overview||3 RC4 Encryption Architectures||4 MIMO Detection Algorithm and Architecture||5 LDPC Decoder Architecture||6 Conclusion and Future Wor

Cooperative Partial Detection for MIMO Relay Networks

This paper was submitted by the author prior to final official version. For official version please see http://hdl.handle.net/1911/64372Cooperative communication has recently re-emerged as a possible paradigm shift to realize the promises of the ever increasing wireless communication market; how- ever, there have been few, if any, studies to translate theoretical results into feasi- ble schemes with their particular practical challenges. The multiple-input multiple- output (MIMO) technique is another method that has been recently employed in different standards and protocols, often as an optional scenario, to further improve the reliability and data rate of different wireless communication applications. In this work, we look into possible methods and algorithms for combining these two tech- niques to take advantage of the benefits of both. In this thesis, we will consider methods that consider the limitations of practical solutions, which, to the best of our knowledge, are the first time to be considered in this context. We will present complexity reduction techniques for MIMO systems in cooperative systems. Furthermore, we will present architectures for flexible and configurable MIMO detectors. These architectures could support a range of data rates, modulation orders and numbers of antennas, and therefore, are crucial in the different nodes of cooperative systems. The breadth-first search employed in our realization presents a large opportunity to exploit the parallelism of the FPGA in order to achieve high data rates. Algorithmic modifications to address potential sequential bottlenecks in the traditional bread-first search-based SD are highlighted in the thesis. We will present a novel Cooperative Partial Detection (CPD) approach in MIMO relay channels, where instead of applying the conventional full detection in the relay, the relay performs a partial detection and forwards the detected parts of the message to the destination. We will demonstrate how this approach leads to controlling the complexity in the relay and helping it choose how much it is willing to cooperate based on its available resources. We will discuss the complexity implications of this method, and more importantly, present hardware verification and over-the-air experimentation of CPD using the Wireless Open-access Research Platform (WARP).NSF grants EIA-0321266, CCF-0541363, CNS-0551692, CNS-0619767, EECS-0925942, and CNS-0923479, Nokia, Xilinx, Nokia Siemens Networks, Texas Instruments, and Azimuth Systems

Doctor of Philosophy

dissertationThe continuous growth of wireless communication use has largely exhausted the limited spectrum available. Methods to improve spectral efficiency are in high demand and will continue to be for the foreseeable future. Several technologies have the potential to make large improvements to spectral efficiency and the total capacity of networks including massive multiple-input multiple-output (MIMO), cognitive radio, and spatial-multiplexing MIMO. Of these, spatial-multiplexing MIMO has the largest near-term potential as it has already been adopted in the WiFi, WiMAX, and LTE standards. Although transmitting independent MIMO streams is cheap and easy, with a mere linear increase in cost with streams, receiving MIMO is difficult since the optimal methods have exponentially increasing cost and power consumption. Suboptimal MIMO detectors such as K-Best have a drastically reduced complexity compared to optimal methods but still have an undesirable exponentially increasing cost with data-rate. The Markov Chain Monte Carlo (MCMC) detector has been proposed as a near-optimal method with polynomial cost, but it has a history of unusual performance issues which have hindered its adoption. In this dissertation, we introduce a revised derivation of the bitwise MCMC MIMO detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling terms. Another common problem with MCMC algorithms is an unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations is used on a slowly converging example, the output LLRs can be unstable and overconfident, therefore, we develop a method to identify rare, slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in bit-error-rates. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximum-a-posteriori (MAP) performance even with challenging, realistic, highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8x8 256 QAM. Further, the new excited MCMC (X-MCMC) detector is demonstrated on an 8-antenna MIMO testbed with the 802.11ac WiFi protocol, confirming its high performance. Finally, a VLSI implementation of the X-MCMC detector is presented which retains the near-optimal performance of the floating-point algorithm while having one of the lowest complexities found in the near-optimal MIMO detector literature

Energy Efficient VLSI Circuits for MIMO-WLAN

Mobile communication - anytime, anywhere access to data and communication services - has been continuously increasing since the operation of the first wireless communication link by Guglielmo Marconi. The demand for higher data rates, despite the limited bandwidth, led to the development of multiple-input multiple-output (MIMO) communication which is often combined with orthogonal frequency division multiplexing (OFDM). Together, these two techniques achieve a high bandwidth efficiency. Unfortunately, techniques such as MIMO-OFDM significantly increase the signal processing complexity of transceivers. While fast improvements in the integrated circuit (IC) technology enabled to implement more signal processing complexity per chip, large efforts had and have to be done for novel algorithms as well as for efficient very large scaled integration (VLSI) architectures in order to meet today's and tomorrow's requirements for mobile wireless communication systems. In this thesis, we will present architectures and VLSI implementations of complete physical (PHY) layer application specific integrated circuits (ASICs) under the constraints imposed by an industrial wireless communication standard. Contrary to many other publications, we do not elaborate individual components of a MIMO-OFDM communication system stand-alone, but in the context of the complete PHY layer ASIC. We will investigate the performance of several MIMO detectors and the corresponding preprocessing circuits, being integrated into the entire PHY layer ASIC, in terms of achievable error-rate, power consumption, and area requirement. Finally, we will assemble the results from the proposed PHY layer implementations in order to enhance the energy efficiency of a transceiver. To this end, we propose a cross-layer optimization of PHY layer and medium access control (MAC) layer