This paper proposes a halved-phase only multipleinput- multiple-output (HPO-MIMO) whose base station (BS) acquires pi-periodic phases of complex envelope signals by phase detectors. Because phases are directly extracted from the received radio frequency (RF) signals, the RF receivers of HPO-MIMO are simplified significantly, and enjoy much lower power consumption and fabrication cost in comparison to their correspondences in MIMO. However, existing MIMO baseband algorithms will not work in HPO-MIMO because the latter losses magnitude information completely and its phase observations suffer from pi-ambiguities. Practical baseband algorithms are developed for HPO-MIMO by firstly categorizing the multiuser detection (MUD) and channel estimation (CE) problems as generalized linear mixing ones under pi-periodic phase measurements, and then resolving the latter by a modified generalized approximate message passing (GAMP). In GAMP, the mean-variance messages involving phase observations are numerically updated by the importance sampling technique. Scale-and-polarity ambiguities among the CE and MUD results are removed based on the measurements from a conventional RF chain. Simulation results validate the effectiveness of the proposed algorithms, and compare HPO-MIMO with existing MIMO schemes thoroughly.

Cooperative localization (CL) based on the angle-of-arrival (AOA) measurements is a promising positioning technique for the wireless sensor network (WSN). This is because CL reaches high localization precision and robustness by exploiting the relative ranging measurements among the agents. In addition, the AOA interpretation involves no propagation parameters, and its acquisition does not require strict time-synchronization among the WSN nodes. In this paper, we firstly categorize the AOA-CL problem as a generalized linear-mixing problem under the phase-only measurements, and then resolve it by our developed phase-only generalized approximate message passing (POG-AMP) algorithm. The POG-AMP localizer is warm-started by developing an incremental localizer assisting by anchor-connectivity and region-boundary constraints. It calls for sparse matrix-vector multiplications (MVMs) as its most complex operations, and can be implemented in a distributed manner. Therefore, it has low computational complexity, and is suitable for WSN hardware implementation. Simulation results validate the state-of-the-art performance and cooperation gains of the POG-AMP localizer.

Cooperative localizer is a potential positioning technique for vehicular ad-hoc networks (VANETs). However, it would suffer from the non-line-of-sight (NLOS) problems widely existing in VANETs. This letter proposes a geographical information enhanced cooperative localizer (GIE-CL) for VANET with time-of-arrival (TOA) measurements. It iterates between NLOS identification and extended generalized approximate message passing (EGAMP) motivated cooperative positioning. A region sampling method is developed to identify NLOS measurements based on geographical information and current vehicle position estimations. Subsequently, the detected NLOS measurements are removed and the EGAMP localizer is activated to re-estimate the vehicle positions. The above-mentioned iteration will be terminated until convergence is reached. Initial positions are provided by Global Navigation Satellite System (GNSS). Simulation results show that GIE-CL can handle the NLOS problem, and approach to its performance upper bound provided by the case with known NLOS/LOS link-type information. Compared to EGAMP localizer, the positioning accuracy of GIE-CL is improved by eight times when the allowed localization error is less than 5 m.

This letter proposes a universal cooperative localizer (UCL) for wireless sensor network (WSN) with varied types of ranging measurements including time-of-arrival, radio signal strength, angle-of-arrival, and Doppler frequency. By representing the WSN node positions as complex numbers, the localization problem is converted as a generalized linear mixing problem, and then resolved by generalized approximate message passing with the importance sampling mechanism. The computational complexity of UCL is two orders lower than that of the belief propagation localizer. Simulation results validate that UCL outperforms most mainstream localizers, and cooperation significantly enhances the positioning accuracy.

This paper proposes a multiuser magnitude-only (MO-)MIMO, whose base station acquires quantized magnitudes of the complex baseband signals through envelop detectors and low-resolution ADCs. Consequently, MO-MIMO enjoys much lower circuit power and cost in comparison with the conventional MIMO. Because the phase information is unavailable, all the existing MIMO baseband algorithms cannot be applied into MO-MIMO. Therefore, two types of channel estimators and multiuser detectors are constructed by first categorizing the channel estimation and multiuser detection problems as a quantized phase retrieval (PR) problem, and then solving the latter by developing two methods under the framework of generalized approximate message passing (GAMP). The first method directly applies GAMP to solve the quantized PR problem by exploiting the probability relationships between the quantized magnitude measurements and unknown complex signals. The second method iterates between the missing phase estimation and signal recovery, where the latter calls for GAMP to handle a linear mixing problem with quantized observations. The developed estimators and detectors call for matrix-vector multiplications and nonlinear function calculations as the most complex operations, handle the nonlinear quantization loss specially, and exploit the signal prior probability distributions. Finally, their effectiveness is validated experimentally.

This paper proposes a MIMO system where the base station (BS) acquires quantized phase-only (PO) measurements of the complex baseband signal by our introduced stage-wised phase quantizer. PO-MIMO requires only one-bit ADCs for data sampling, so it successfully overcomes the ADC bottleneck that appears when the signal bandwidth is extremely wide. We construct a PO generalized approximate message passing (POG-AMP) algorithm for solving the linear mixing problem with quantized or unquantized phase measurements. POG-AMP has low computational complexity, exploits the signal prior statistical distribution, and handles the nonlinear distortions exerted on the measurements (e.g., losing magnitude and quantization). Then, POG-AMP is successfully applied to construct practical channel estimator and multiuser detector for PO-MIMO. Numerical results show that the POG-AMP estimator (POG-AMPE) and POG-AMP detector (POG-AMPD) are robust to the phase-quantization loss. POG-AMPE acquires high-quality channel side information at the receiver (CSIR), and POG-AMPD is robust to the CSIR errors when the BS antennas are massive enough. By introducing moderately more BS antennas, PO-MIMO with phase measurements even performs similarly to MIMO with full measurements containing both magnitude and phase. In order to maximize the transmit energy-efficiency, the lengths of the channel training sequences should be gradually increased with the increase of the channel coherence time. Antenna correlations at the BS degrade the convergence and bit-error rate performances of POG-AMPD, but can be handled by the analog spatial filtering technique.

This paper proposes a massive multiple-input multiple-output (MIMO) with low-precision in-phase quadrature-phase (IQ) (de-)modulators and low-resolution analog-to-digital convertors, and develops uplink baseband algorithms for the proposed MIMO system. The IQ imbalance parameters are recovered by a two-stage method. At the first stage, the base station (BS) antennas acquire phase-shifted versions of their imbalance parameters after receiving some predefined training sequence emitted by a specific transceiver that is near to the BS and shares flat-fading channels between itself and the BS. At the second stage, the phase shifts from the first stage are estimated by the specific transceiver through receiving a common sequence transmitted by the BS antennas alternatively. In order to jointly handle the IQ imbalance and nonlinear quantization loss, the IQ-imbalanced multiuser (MU)-MIMO with single-antenna users is first converted into an IQ-balanced MU-MIMO with dual-antenna users. Then, two kinds of channel estimator and multiuser detectors are constructed for the equivalent IQ-balanced system based on the spectral projected gradient method and vectorized message passing de-quantization algorithm. Our numerical results validate the effectiveness of the above baseband algorithms, and show that the proposed massive MIMO is more energy-efficient than the conventional one when the channel coherence time is long enough.

This paper proposes a multiuser MIMO system with both full measurements and one-bit magnitude observations, which are acquired by several linear inphase-and-quadrature (IQ) structured radio frequency (RF) chains and massive one-bit envelope chains, respectively. The total circuit power and cost are not increased significantly, since the added one-bit envelope chains have low power and low cost. Channel side information on the one-bit envelope chains is acquired by sharing the IQ-structured RF chains and executing a channel calibration operation. Two multiuser detectors are constructed based on the semidefinite relaxation (SDR) and approximate message passing (AMP). The one-bit magnitudes are interpreted as inequality constraints in the SDR detector, and exploited in a Bayesian manner by the AMP detector. Simulation results show that the one-bit magnitude measurements bring about high MIMO multiplexing and diversity gains, and decrease the transmission power. With the increase of the channel coherence time, more one-bit envelope chains are prone to be equipped, and one-bit magnitude-aided MIMO becomes more and more spectral-and-energy-efficient than the conventional MIMO.

This correspondence researches the multiuser detection (MUD) in a new massive MIMO system, where the base station (BS) acquires the quantized amplitude-only (AO) measurements of the complex baseband signals through the envelop detection and low-resolution analog-to-digital convertor (ADC) sampling. Cumbersome frequency mixers are removed and low-resolution ADCs can easily reach the Giga-sample-per-second (GSPS) rate, so massive AO-MIMO has low power and low cost, and is especially suitable for millimeter-wave (mmWave) communication. A practical multiuser detector is constructed based on the Wirtinger-flow (WF) gradient method for phase retrieval (PR), and the message passing de-quantization (MPDQ) algorithm for the quantized compressed sensing (CS). It iterates between the missing phase estimation and signal reconstruction, and involves only matrix-vector multiplication as its complex operation. Simulation results validate the effectiveness of the proposed detector, and show that massive AO-MIMO can even perform similarly as the classical massive MIMO by adopting relatively more BS antennas.