Hemanth Sampath

Generalized linear precoder and decoder design for MIMO channels using the weighted MMSE criterion

We address the problem of designing jointly optimum linear precoder and decoder for a MIMO channel possibly with delay-spread, using a weighted minimum mean-squared error (MMSE) criterion subject to a transmit power constraint. We show that the optimum linear precoder and decoder diagonalize the MIMO channel into eigen subchannels, for any set of error weights. Furthermore, we derive the optimum linear precoder and decoder as functions of the error weights and consider specialized designs based on specific choices of error weights. We show how to obtain: (1) the maximum information rate design; (2) QoS-based design (we show how to achieve any set of relative SNRs across the subchannels); and (3) the (unweighted) MMSE and equal-error design for fixed rate systems.

Optimal designs for space-time linear precoders and decoders

We introduce a new paradigm for the design of transmitter space-time coding that we refer to as linear precoding. It leads to simple closed-form solutions for transmission over frequency-selective multiple-input multiple-output (MIMO) channels, which are scalable with respect to the number of antennas, size of the coding block, and transmit average/peak power. The scheme operates as a block transmission system in which vectors of symbols are encoded and modulated through a linear mapping operating jointly in the space and time dimension. The specific designs target minimization of the symbol mean square error and the approximate maximization of the minimum distance between symbol hypotheses, under average and peak power constraints. The solutions are shown to convert the MIMO channel with memory into a set of parallel flat fading subchannels, regardless of the design criterion, while appropriate power/bits loading on the subchannels is the specific signature of the different designs. The proposed designs are compared in terms of various performance measures such as information rate, BER, and symbol mean square error.

A fourth-generation MIMO-OFDM broadband wireless system: design, performance, and field trial results

Increasing demand for high-performance 4G broadband wireless is enabled by the use of multiple antennas at both base station and subscriber ends. Multiple antenna technologies enable high capacities suited for Internet and multimedia services, and also dramatically increase range and reliability. In this article we describe a multiple-input multiple-output OFDM wireless communication system, lab test results, and field test results obtained in San Jose, California. These are the first MIMO system field tests to establish the performance of MIMO communication systems. Increased capacity, coverage, and reliability are clearly evident from the test results presented in this article.

Linear precoding for space-time coded systems with known fading correlations

We design an optimal linear precoder for a space-time coded system assuming knowledge of only the transmit antenna fading correlations. Assuming a flat fading channel and a maximum-likelihood receiver, we show that the linear precoder transmits power on the eigenmodes of the transmit antenna correlation matrix. The power allocation on the eigenmodes is a form of waterpouring policy. Simulation results are presented to show performance improvement on a space-time coded system.

Joint transmit and receive optimization for high data rate wireless communication using multiple antennas

We propose a joint transmit and receive optimization scheme for the multi-input multi-output (MIMO) spatial multiplexing system (also known as BLAST) in a narrowband wireless channel. The optimum solution is found using the minimum mean square error criterion subject to average transmitter power constraint. The transmit and receive filters are shown to decouple the MIMO channel into parallel sub-chanhels. The optimum power allocation policy on these sub-channels is derived.

Emerging technologies for WLAN

New technologies continue to be introduced for WLAN applications at a robust pace. We review the value proposition for some of the key features of 802.11ac such as larger bandwidth, higher order modulation, and MIMO and MUMIMO transmission modes, explaining how each feature translates to improved user experience. We present channel measurements and prototype performance data to demonstrate the gains of MIMO and MU-MIMO in an indoor environment. Next, we discuss the value proposition of some key features of 802.11ah. Measurement data of 802.11ah performance is provided, showing how it is a compelling technology for the growing Internet of Things market. We conclude with a preview of emerging technologies that promise to improve user experience - 802.11ai for fast network acquisition and 802.11ax for high-efficiency networking in dense indoor and outdoor networks.

Pre-equalization for MIMO wireless channels with delay spread

We consider a downlink finite impulse response (FIR) multi-input multi-output (MIMO) wireless channel with L taps. A is shown that such a channel can be pre-equalized with an FIR MIMO transmit filter with only L taps, if the angle spread due to the different multipaths is sufficiently large at the transmitter. The filter taps are derived for the cases where the transmitter has perfect and partial channel knowledge, respectively. Finally, we present a pre-filter structure which converts the available frequency diversity into spatial diversity. The resulting spatial diversity can then be exploited using conventional receivers designed for frequency-flat fading channels.

Performance analysis of linear precoding based on field trials results of MIMO-OFDM system

We use field trial results obtained from a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system with two transmitter and three receiver antennas (2/spl times/3), to first validate the properties of the transmit correlation matrix in a macro-cellular environment. We find that approximately 20% of the locations have well-defined transmit correlation matrices. Furthermore, the eigenvectors of the transmit correlation matrix vary slowly over distance with 60% of the locations having eigenvector variation of less than 1 dB over a distance of 20 m. Next, we quantify the performance of the optimal statistical linear precoding (OSLP) , and statistical one-dimensional (1-D) eigenbeamforming (SEB) based on transmit correlation matrices, and the 1-D eigenbeamforming (EB)-based on perfect channel knowledge at the transmitter. We find that the OSLP and SEB schemes obtain array gain over the Alamouti scheme at lower signal-to-noise ratio (SNR) with a median gain of 2.0 (1.5) dB at the 1.0-(3.5) km cell-radii. However, the SEB scheme (unlike the OSLP scheme) looses diversity order at higher SNR that leads to a performance loss. The EB scheme provides the best performance over the Alamouti scheme, at the expense of increased feedback requirements.

Adaptive modulation for multiple antenna systems

We present a simple adaptive modulation scheme for multiple antenna systems. Assuming a linear MMSE receiver and a QoS requirement, we show how to use the signal to interference and noise ratio (SINR) information at the receiver output to optimize the data rate for each transmit antenna. Next, we present an iterative adaptive modulation scheme that provides better throughput when compared to the previous scheme, but takes a hit in implementation complexity. We assume perfect channel knowledge at the receiver and the existence of some minimal feedback link to the transmit antennas.

Field Results on MIMO Performance in UMB Systems

The paper presents MIMO field performance results observed using a ultra mobile broadband (UMB) testbed network. We evaluate metrics such as antenna correlations and channel condition number to characterize the MIMO channel. Results show that low condition numbers, which are beneficial to MIMO, are prevalent for a majority of the coverage area in our network. We demonstrate that the use of MIMO provides gains of the order of 20-40% over SIMO transmissions. These gains are made possible by the use of cross-polarized transmit antennas and advanced UMB features that allow dynamic MIMO vs. SIMO transmission selection based on channel conditions. These results are obtained in a truly mobile, wireless wide-area deployment, which makes them unique. Our results point to the viability and value of MIMO in future mobile wireless networks.