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Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequency-adaptive e qualizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems. [ABSTRACT FROM AUTHOR]

The nonlinearities of power amplifiers combined with non-contiguous transmissions found in modern, frequency-agile, wireless standards create undesirable spurious emissions through the nearby spectrum of data carriers. Digital predistortion (DPD) is an effective way of combating spurious emission violations without the need for a significant power reduction in the transmitter leading to better power efficiency and network coverage. In this paper, an iterative, multi sub-band version of the sub-band DPD, proposed earlier by the authors, is presented. The DPD learning is iterated over intermodulation distortion (IMD) sub-bands until a satisfactory performance is achieved for each of them. A sequential DPD learning procedure is also presented to reduce the hardware complexity when higher order nonlinearities are incorporated in the DPD learning. Improvements in the convergence speed of the adaptive DPD learning are also achieved via incorporating a variable learning rate and interpolation of previously trained DPD coefficients. A WARPLab implementation of the proposed DPD is also shown with excellent suppression of the targeted spurious emissions. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) wireless is an enabling technology for high spectral efficiency and has been adopted in many modern wireless communication standards, such as 3GPP-LTE and IEEE 802.11n. However, (optimal) maximum a-posteriori (MAP) detection suffers from excessively high computational complexity, which prevents its deployment in practical systems. Hence, many algorithms have been proposed in the literature that trade-off performance versus detection complexity. In this paper, we propose a flexible N-Way MIMO detector that achieves excellent error-rate performance and high throughput on graphics processing units (GPUs). The proposed detector includes the required QR decomposition step and a tree-search detector, which exploits the massive parallelism available in GPUs. The proposed algorithm performs multiple tree searches in parallel, which leads to excellent error-rate performance at low computational complexity on different GPU architectures, such as Nvidia Fermi and Kepler. We highlight the flexibility of the proposed detector and demonstrate that it achieves higher throughput than existing GPU-based MIMO detectors while achieving the same or better error-rate performance. [ABSTRACT FROM AUTHOR]

Low-density parity-check (LDPC) codes and convolutional Turbo codes are two of the most powerful error correcting codes that are widely used in modern communication systems. In a multi-mode baseband receiver, both LDPC and Turbo decoders may be required. However, the different decoding approaches for LDPC and Turbo codes usually lead to different hardware architectures. In this paper we propose a unified message passing algorithm for LDPC and Turbo codes and introduce a flexible soft-input soft-output (SISO) module to handle LDPC/Turbo decoding. We employ the trellis-based maximum a posteriori (MAP) algorithm as a bridge between LDPC and Turbo codes decoding. We view the LDPC code as a concatenation of n super-codes where each super-code has a simpler trellis structure so that the MAP algorithm can be easily applied to it. We propose a flexible functional unit (FFU) for MAP processing of LDPC and Turbo codes with a low hardware overhead (about 15% area and timing overhead). Based on the FFU, we propose an area-efficient flexible SISO decoder architecture to support LDPC/Turbo codes decoding. Multiple such SISO modules can be embedded into a parallel decoder for higher decoding throughput. As a case study, a flexible LDPC/Turbo decoder has been synthesized on a TSMC 90 nm CMOS technology with a core area of 3.2 mm. The decoder can support IEEE 802.16e LDPC codes, IEEE 802.11n LDPC codes, and 3GPP LTE Turbo codes. Running at 500 MHz clock frequency, the decoder can sustain up to 600 Mbps LDPC decoding or 450 Mbps Turbo decoding. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGA-like performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms. [ABSTRACT FROM AUTHOR]