A. Bourdoux

Selective Spanning with Fast Enumeration: A Near Maximum-Likelihood MIMO Detector Designed for Parallel Programmable Baseband Architectures

ML and near-ML MIMO detectors have attracted a lot of interest in recent years. However, almost all of the reported implementations are delivered in ASIC or FPGA. Our contribution is to co-optimize the near-ML MIMO detector algorithm and implementation for parallel programmable base-band architectures, such as DSPs with VLIW, SIMD or vector processing features. Although for hardware the architecture can be tuned to fit algorithms, for programmable platforms the algorithm must be elaborately designed to fit the given architecture, so that efficient resource-utilizations can be achieved. By thoroughly analyzing and exploiting the interaction between algorithms and architectures, we propose the SSFE (selective spanning with fast enumeration) as an architecture-friendly near-ML MIMO detector. The SSFE has a distributed and greedy algorithmic structure that brings a completely deterministic and regular dataflow. The SSFE has been evaluated for coded OFDM transmissions over 802.11n channels and 3GPP channels. Under the same performance constraints, the complexity of the SSFE is significantly lower than the K-Best, the most popular detector implemented in hardware. More importantly, SSFE can be easily parallelized and efficiently mapped on programmable baseband architectures. With TI TMS320C6416, the SSFE delivers 37.4 - 125.3 Mbps throughput for 4x4 64 QAM transmissions. To the best of our knowledge, this is the first reported near-ML MIMO detector explicitly designed for parallel programmable architectures and demonstrated on a real-life platform.

Low-Complexity EM-based Joint Acquisition of the Carrier Frequency Offset and IQ Imbalance

New air interfaces are currently being developed to meet the high spectral efficiency requirements of the emerging wireless communication systems. In this context, OFDM is considered as a promising air interface candidate for both indoor and outdoor communications. Besides spectral efficiency and power consumption, the production cost of the transceiver should also be optimized. Direct-conversion radio frequency receivers are appealing because they avoid costly intermediate frequency hardware. However, they imply analog IQ separation, introducing a phase and amplitude mismatch between the I and Q branches. A communication system based on OFDM is sensitive to synchronization errors, such as CFO, and to front- end non-idealities, such as IQ imbalance. The goal of this paper is to use the iterative EM algorithm to acquire jointly the CFO and the IQ imbalance. The solution relies on a standard compliant repetitive preamble and does not require the knowledge of the propagation channel. Based on a second order approximation of the likelihood function, the complexity of the EM algorithm is significantly reduced. The algorithm is shown to perform extremely well: the estimates of the CFO and of the IQ imbalance converge to their ML estimate after less than 3 iterations. It outperforms state-of-the-art solutions significantly and suffers from a lower computational complexity. While the CFO estimate is robust against variations of the SNR, the IQ imbalance estimate accuracy is reduced at values of the SNR below 10 dB and above 35 dB.

Low-Complexity EM-based Joint CFO and IQ imbalance Acquisition

New air interfaces are currently being developed to meet the high spectral efficiency requirements of the emerging wireless communication systems. In this context, OFDM is considered as a promising air interface candidate for both indoor and outdoor communications. Besides spectral efficiency and power consumption, the production cost of the transceiver should also be optimized. Direct-conversion radio frequency receivers are appealing because they avoid costly intermediate frequency hardware. However, they imply analog IQ separation, introducing a phase and amplitude mismatch between the I and Q branches. A communication system based on OFDM is sensitive to synchronization errors, such as CFO, and to front-end non-idealities, such as IQ imbalance. The goal of this paper is to use the iterative EM algorithm to acquire jointly the CFO and the IQ imbalance. The solution relies on a repetitive preamble and does not require the knowledge of the propagation channel. Based on a second order approximation of the likelihood function, the complexity of the EM algorithm is significantly reduced. The algorithm is shown to perform extremely well: the estimates of the CFO and of the IQ imbalance converge to their ML estimate after less than 3 iterations. While the CFO estimate is robust against variations of the SNR, the IQ imbalance estimate accuracy is reduced at values of the SNR below 10 dB and above 35 dB.

Low-Complexity Frequency Domain Equalization Receiver for Continuous Phase Modulation

A new approach for frequency-domain equalization of continuous phase modulated (CPM) signals is presented. In contrast with state-of-the-art receivers, we separate channel equalization on the one hand and CPM demodulation on the other. This separation enables us to calculate independently of the CPM scheme a new low-complexity zero-forcing channel equalizer. We also present a new high-performance minimum mean square error (MMSE) channel equalizer for any CPM scheme and a method to lower its complexity for a popular class of CPM schemes. Simulations show that our new MMSE equalizer significantly outperforms state-of-the-art linear receivers in a 60 GHz multipath environment.

Power-aware evaluation flowfor digital decimation filter architectures for high-speed ADCS

The raising cost of the latest technology nodes as well as the design cost associated has motivated an increasing push for flexible radio implementations. In this context, Sigma-Delta (ΣΔ) ADCs have emerged as a promising alternative to direct conversion. In this work a novel wireless receiver architecture based on an RF bandpass ΣΔ is considered. One of the key blocks of this architecture is the digital decimation filter which needs to run at very high speed. In order to offer competitive power consumption, the implementation of this decimation filter needs to be thoroughly optimized. Considering that many implementation options are possible, this paper presents an early evaluation flow, which still considers relevant implementation details to aid designers in selecting the most optimal implementation option. The flow is shown for a design of a 9-bits ADC targeting 40nm CMOS technology. The power consumption of the optimal implementation option is shown to be below 12.6 mW.

Applying frequency domain equalization to precoded CPM

We show how to apply frequency domain equalization (FDE) to precoded continuous phase modulation (CPM) systems. It is well known that differential precoding can be applied to the specific, popular class of CPM schemes with modulation index h = 1/2Q, where Q is any integer. This precoding halves the bit error rate (BER) compared to nonprecoded CPM without any overhead or complexity increase. We apply FDE to a block-based precoded CPM system. Therefore, we show that in addition to a cyclic prefix, two subblocks of data-dependent symbols have to be inserted in each block to cope with the memory in the CPM signal and to enable correct decoding by the receiver. We explain how to calculate these subblocks. Simulation results in a 60 GHz environment confirm that the BER is halved by precoding, and that this precoding is compatible with FDE using our new technique.

The Quality-Energy Scalable OFDMA Modulation for Low Power Transmitter and VLIW Processor Based Implementation

The improvement of spectral efficiency comes at the cost of exponential increment of signal processing complexity [1]. Hence, the energy-efficiency of baseband has recently turned out to be the bottleneck when deploying advanced air interfaces such as that in 4G. We advocate the scalable baseband design as a system level technique to aggressively optimize the average computation-load and associated energy-consumption. The key technique is to dynamically scale the baseband processing itself to the user requirement, the environment, the platform, etc. In this paper, we present the scalable design and VLIW processor based implementation of the OFDMA modulator, which is one of the most energy consuming parts of OFDMA and MIMO- OFDMA transmitters (in IEEE 802.16e , 3GPP LTE, etc.). Our work enables the OFDMA modulator to scale the modulation- accuracy and computation-load, so that the OFDMA modulator can dynamically reconfigure and work with minimal number of operations, whereas the required modulation-accuracy is still firmly guaranteed. Our work brings significant reductions in the average computation-load and associated energy-dissipation on real-life programmable platforms. Specifically, when the user is working with 16QAM and 1/2 coding rate (Turbo Coding) in a half-loaded 8-user system, the proposed scheme reduces 84% of the cycle-count and the associated energy-consumption on TI TMS320C6713, whereas the resulted Relative Constellation Error (RCE) is still lOdB better than the required RCE in IEEE 802.16e specifications.

Single-Carrier FDMA versus Cyclic-Prefix CDMA

In order to meet the data rate and quality-of-service (QoS) requirements of the future cellular systems, new air interfaces are currently under development. In this paper, we compare two air interfaces of particular interest for the uplink: cyclic-prefix code-division multiple access (CP-CDMA) proposed in the literature as an evolution of direct-sequence code-division multiple access (DS-CDMA) because it enables the low complexity equalization of the multipath channel in the frequency domain, and single-carrier frequency-division multiple access (SC-FDMA), recently proposed in the long term evolution of the 3GPP standard because it enables the easy separation of the users in the frequency domain. We demonstrate analytically that SC-FDMA is a special case of CP-CDMA, in which the CDMA codes have been optimized to minimize the symbol estimation mean square error (MSE) under a constraint of received power. Numerical results show that SC-FDMA outperforms significantly CP-CDMA at high user loads. The transmit power necessary to fulfill the received power constraint is higher in case of SC-FDMA than in case of CP-CDMA when the carrier sub-sets are allocated randomly to the users, and lower when the carrier sub-sets are allocated in an optimized way.

PMCW waveform and MIMO technique for a 79 GHz CMOS automotive radar

Automotive radars in the 77-81 GHz band will be widely deployed in the coming years. This paper provides a comparison of the bi-phase modulated continuous wave (PMCW) and linear frequency-modulated continuous wave (FMCW) waveforms for these radars. The comparison covers performance, implementation and other non technical aspects. Multiple Input Multiple Output (MIMO) radars require perfectly orthogonal waveforms on the different transmit antennas, preferably transmitting simultaneously for fast illumination. In this paper, we propose two techniques: Outer code and Range domain, to enable MIMO processing on the PMCW radars. The proposed MIMO techniques are verified with both simulation and lab experiments, on a fully integrated deep-submicron CMOS integrated circuit designed for a 79 GHz PMCW radar. Our analysis shows that, although not widely used in the automotive industry, PMCW radars are advantageous for low cost, high volume single-chip production and excellent performance.

Symbol Based Search Space Constraining for Complexity/Performance Scalable Near ML Detection in Spatial Multiplexing MIMO OFDM Systems

For both outdoor and indoor wireless systems there is an increasing demand of high spectral efficiency at a very low cost and power consumption. In this context, MIMO wireless system adopting spatial multiplexing offer a way of increasing the spectral efficiency of the system. In order to fully exploit this capacity non linear MIMO detectors such as Maximum Likelihood detectors are required. However, when high order modulation schemes are applied, the complexity of this kind of detector becomes prohibitive for a practical implementation. As a solution to this problem, low complexity Maximum Likelihood detectors such as sphere detectors are appealing as a low complexity solution for high spectral efficiency transmission. Although sphere decoding provides a lower complexity solution than a classical ML detector, its complexity still remains unpredictable and exponentially dependent on channel propagation conditions. This variability in complexity makes the implementation of sphere decoders not practical. In this paper, a new approach for constraining the ML search space is proposed which provides a predictable upper bound for complexity, hence facilitating its implementation. Moreover, the new approach for computing the constrained search space significantly reduces the complexity of the detection while offering scalability in terms of performance and complexity. Simulation results in a cellular system demonstrate the scalability of our detector and the performance/complexity trade-off that it enables.