In the generalized spatial modulation (GenSM) multiple input multiple output (MIMO) system, each block of data bits is mapped to a set of spatially multiplexed (SMX) symbols and an index of transmit antenna combination (TAC) of active antennas. The difficulty for the GenSM MIMO receiver is to detect the SMX symbols and TAC index simultaneously. Recently, we applied the conventional sphere decoding algorithm (SDA) successively to achieve the exact maximum likelihood detection (MLD) of GenSM MIMO signals. The SDA scheme suffers from variable computational complexity and leads to hardware detectors with variable throughput rate. Instead, fixed-complexity tree search algorithms, e.g., reduced fixed sphere decoding (rFSD), with nearly MLD performance are employed to facilitate hardware implementation. Here, we propose to apply the rFSD successively and design a hardware architecture for detecting GenSM MIMO signals under the scenario of 5 transmit antennas, 2 transmit radio frequency chains, 4 receive antennas, and 64-QAM SMX symbols. The VLSI implementation results under the TSMC 90nm CMOS technology reveal that our architecture requires 276.7K gates and provides detection throughput 1.613 Gbps, while operating at 322.6 MHz. Compared with other related architectures, our architecture provides higher detection throughput rate and is of better hardware efficiency.

Direction of Arrival (DoA) estimation is essential to adaptive beamforming widely used in many radar and wireless communication systems. Although many estimation algorithms have been investigated, most of them focus on the performance enhancement aspect but overlook the computing complexity or the hardware implementation issues. In this paper, a low-complexity yet effective DoA estimation algorithm and the corresponding hardware accelerator chip design are presented. The proposed algorithm features a combination of signal sub-space projection and parallel matching pursuit techniques, i.e., applying signal projection first before performing matching pursuit from a codebook. This measure helps minimize the interference from noise sub-space and makes the matching process free of extra orthogonalization computations. The computing complexity can thus be reduced significantly. In addition, estimations of all signal sources can be performed in parallel without going through a successive update process. To facilitate an efficient hardware implementation, the computing scheme of the estimation algorithm is also optimized. The most critical part of the algorithm, i.e., calculating the projection matrix, is largely simplified and neatly accomplished by using QR decomposition. In addition, the proposed scheme supports parallel matches of all signal sources from a beamforming codebook to improve the processing throughput. The algorithm complexity analysis shows that the proposed scheme outperforms other well-known estimation algorithms significantly under various system configurations. The performance simulation results further reveal that, subject to a beamforming codebook with a 5° angular resolution, the Root Mean Square (RMS) error of angle estimations is only 0.76° when Signal to Noise Ratio (SNR) = 20 dB. The estimation accuracy outpaces other matching pursuit based approaches and is close to that of the classic Estimation of Signal Parameters Via Rotational Invariance Techniques (ESPRIT) scheme but requires only one fifth of its computing complexity. In developing the hardware accelerator design, pipelined Coordinate Rotation Digital Computer (CORDIC) processors consisting of simple adders and shifters are employed to implement the basic trigonometric operations needed in QR decomposition. A systolic array architecture is developed as the computing kernel for QR decomposition. Other computing modules are also realized using various linear systolic arrays and chained together seamlessly to maximize the computing throughput. A Taiwan Semiconductor Manufacturing Company (TSMC) 40 nm CMOS process was chosen as the implementation technology. The gate count of the chip design is 454.4k, featuring a core size of 0.76 ${\mathrm{mm}}^2$ , and can operate up to 333 MHz. This suggests that one DoA estimation, with up to three signal sources, can be performed every 2.38 μs.

This paper presents a low complexity chip design supporting dual-mode beamforming, i.e. sampling matrix inversion (SMI) and the minimum variance distortionless response (MVDR), for wireless Multiple-Input Multiple-Output (MIMO) communication systems. The auto-correlation matrix inversion is the critical computing kernel shared by the two beamforming schemes. To alleviate the computing complexity, the auto-correlation matrix is approximated by a Toeplitz counterpart, which can be decomposed efficiently by applying the Cholesky decomposition and the Schur algorithm. This leads to an.......... to.......... complexity reduction, where N is the matrix size, while preserving computing parallelism for the hardware design. In addition, a diagonal loading technique is employed to mitigate the stability problem when the matrix is ill-conditioned. Simulation results indicate that no performance loss is observed due to the algorithm simplification measures. A systolic array based mapping procedure converts the two beamforming algorithms to a unified hardware accelerator design with 80% shared circuitry. Complex-valued divisions are achieved by adopting a hardware efficient coordinate rotation digital computer (CORDIC) scheme. In chip implementation, a TSMC 90nm UTM process technology is used and the design specs largely follow the requirements of IEEE 802.11ac standard. The core size of the chip design is 0.68 mm... The measurement results show that the chip can operate up to 200MHz with a power consumption of 49.03mW. It can complete the computation of a new beamforming vector (of size 8) every 0.64us and exhibits the highest throughput among the 6 compared designs.

In this paper, we proposed two novel low power multiplier designs based on improved column bypassing schemes. The power saving comes from bypassing signals along those adder columns in the array multiplier corresponding to zero bits in the multiplicand. Spurious signal switching activities can then be eliminated when bypassing occurs. The proposed designs successfully resolve the adverse DC power consumption problem in previous research due to troublesome tri-state input buffers. Our designs also implement the bypassing logic cleverly via C²MOS circuitry and eliminate the costly (both circuit and power-wise) multiplexers. The circuit overheads of the proposed designs can be as low as 10% compared with 54% in M. C. Wen et al. (2005). Simulations results also indicate previous work may fail to gain any power saving (and actually deteriorate power consumption) when Vdd is higher than 1.6V. Our designs, nonetheless, achieve power saving consistently in different working conditions and the best saving can be as much as 29%

In this paper, a low complexity channel estimator for MIMO OFDM systems is proposed. A frequency domain filtering scheme based on the concept of temporal windowing for noise power reduction outside the window is developed to enhance the performance of the simple least square estimator. This approach can eliminate the extra FFT/IFFT operations required in the conventional time domain scheme. A temporal Hamming window is adopted to further reduce the computing complexity of the frequency domain filtering. Simulation results indicate the proposed scheme can significantly improve both the NMSE and BER performance of the LS channel estimator. Compared with the time domain most significant tap (MST) scheme, the proposed scheme has comparable performance but requires much less computing complexity. The proposed scheme also has the advantage in hardware implementation over the MST scheme