Moritz Borgmann

VLSI implementation of MIMO detection using the sphere decoding algorithm

Multiple-input multiple-output (MIMO) techniques are a key enabling technology for high-rate wireless communications. This paper discusses two ASIC implementations of MIMO sphere decoders. The first ASIC attains maximum-likelihood performance with an average throughput of 73 Mb/s at a signal-to-noise ratio (SNR) of 20 dB; the second ASIC shows only a negligible bit-error-rate degradation and achieves a throughput of 170 Mb/s at the same SNR. The three key contributing factors to high throughput and low complexity are: depth-first tree traversal with radius reduction, implemented in a one-node-per-cycle architecture, the use of the /spl lscr//sup /spl infin//-instead of /spl lscr//sup 2/-norm, and, finally, the efficient implementation of the enumeration approach recently proposed in . The resulting ASICs currently rank among the fastest reported MIMO detector implementations.

Impact of the propagation environment on the performance of space-frequency coded MIMO-OFDM

Previous work on space-frequency coded multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) has been restricted to idealistic propagation conditions. In this paper, using a broadband MIMO channel model taking into account Ricean K-factor, transmit and receive angle spread, and antenna spacing, we study the impact of the propagation environment on the performance of space-frequency coded MIMO-OFDM. For a given space-frequency code, we quantify the achievable diversity order and coding gain as a function of the propagation parameters. We find that while the presence of spatial receive correlation affects all space-frequency codes equally, spatial fading correlation at the transmit array can result in widely varying performance losses. High-rate space-frequency codes such as spatial multiplexing are typically significantly more affected by transmit correlation than low-rate codes such as space-frequency block codes. We show that in the MIMO Ricean case the presence of frequency-selectivity typically results in improved performance compared to the frequency-flat case.

Space-frequency coded MIMO-OFDM with variable multiplexing-diversity tradeoff

Space-frequency coded orthogonal frequency division multiplexing (OFDM) is capable of realizing both spatial and frequency-diversity gains in multipath multiple-input multiple-output (MIMO) fading channels. This naturally leads to the question of variable allocation of the channels degrees of freedom to multiplexing and diversity transmission modes. In this paper, we provide a systematic method for the design of space-frequency codes with variable multiplexing-diversity tradeoffs. Simulation results illustrate the performance of the proposed codes.

Interpolation-based QR decomposition in MIMO-OFDM systems

The extension of multiple-input multiple-output (MIMO) sphere decoding from the narrowband case to wideband systems based on orthogonal frequency division multiplexing (OFDM) requires the computation of a QR decomposition for each of the data-carrying OFDM tones. Since the number of data-carrying tones ranges from 48 (as in the IEEE 802.11a/g standards) to 6817 (as in the DVB-T standard), the corresponding computational complexity will in general be significant. This paper presents two algorithms for interpolation-based QR decomposition in MIMO-OFDM systems. An in-depth computational complexity analysis shows that the proposed algorithms, for a sufficiently high number of data-carrying tones and small channel order exhibit significantly smaller complexity than brute-force per-tone QR decomposition.

Interpolation-based efficient matrix inversion for MIMO-OFDM receivers

The use of orthogonal frequency-division multiplexing (OFDM) drastically simplifies receiver design in multiple-input multiple-output (MIMO) wireless systems. Nevertheless, MIMO-OFDM receivers are computationally very demanding since processing is performed on a tone by tone basis with the number of data-carrying tones ranging from 48 (as in the IEEE 802.11a/g standards) to 6817 (as in the DVB-T standard). In this paper, we present a new class of algorithms for computationally efficient channel inversion in MIMO-OFDM zero-forcing receivers. The basic idea of the proposed approach is based on the fact that even though the inverse of a polynomial matrix is generally not polynomial, the adjoint and the determinant will be polynomial, which allows efficient inversion of the individual matrices through interpolation. We perform an in-depth complexity analysis of the new class of interpolation-based inversion algorithms. For the system parameters employed in the IEEE 802.16a standard, we demonstrate computational cost savings of up to 80 % over brute-force per-tone matrix inversion.

On the capacity of noncoherent wideband MIMO-OFDM systems

We study the capacity behavior of full-band MIMO-OFDM systems in the absence of channel state information both at the transmitter and the receiver. Based on capacity lower and upper bounds, we quantify the transmission rate penalty due to channel uncertainty as a function of the number of transmit antennas, the number of resolvable taps in the channel, the power delay profile, and the bandwidth. Our analysis reveals that for a given bandwidth and transmit power there is an optimum, capacity-maximizing, number of transmit antennas. Numerical results show that using a large number of transmit antennas in systems employing bandwidths of several GHz (such as in ultrawideband systems) is detrimental from a capacity point of view. Finally, we evaluate the capacity performance of space-frequency unitary codebooks recently introduced in M. Borgmann and H. Bolcskei, (2005)

Semicoherent PPM for wideband communications

We quantify the impact of coherence on pulse position modulation (PPM) over wideband fading channels by computing achievable rates and an upper bound on uncoded symbol error probability. We study the influence of channel estimation accuracy on the optimum diversity order and furthermore find that a near-optimum receiver typically needs to estimate a few channel taps only