Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI systems. We aimed to propose a BCI-based system with a visual servo module to operate a service robot. We recorded single-channel steady-state visual evoked potentials (SSVEP) as input signals for the BCI system of this study. The visual stimuli for inducing SSVEP were modulated at seven different frequencies with the sampled sinusoidal method. Correspondingly, this SSVEP-based BCI system can generate seven control commands for the operation of the service robot, which can provide three fundamental services: mobility, manipulation, and delivery. The visual servo module was established to reduce the burden of users and accelerate service procedures. To evaluate the performance of this system, subjects were recruited to participate in the experiments. All the participants succeed in operating the robot to provide the basic services. According to the experimental results, this SSVEP-based BCI system that incorporates the visual servo module can be effectively used to operate service robots with reduced number of channels and increased ability to perform multiple tasks.

This paper presents field trial results of innovative air interface technologies: sparse code multiple access (SCMA), filtered orthogonal frequency division multiplexing (f-OFDM), and massive multiple-input multiple-output (MIMO). To confirm the improvement of the promising technologies, the counterparts in long term evolution (LTE) are selected as the comparison baseline. For each technology, detailed parameter configurations and deployment scenarios are addressed at first, then precise test procedures are described, finally, extensive analysis are provided to testify the high spectral efficiency, flexibility and system capacity introduced by the new technologies. According to the trial results, SCMA can achieve 3 times connection number and throughput than SC-FDMA in uplink, and 90% throughput gains over OFDMA in downlink. Waveform f-OFDM is validated to support flexible resources allocation thus avoiding asynchronous transmission in both uplink and downlink. The large number of antennas in Massive MIMO accommodates more user and offers around 10 times throughput than single user scenario in downlink.

Ultrahigh data rate, massive connectivity, ultralow latency, and high reliability, as well as ultraflexible air interface design to support diversified usage scenarios, are the major targets of 5G radio access network design. To meet these targets, advanced new radio transmission technologies, including new waveform, new channel coding, non-orthogonal multiple access, as well as massive antenna techniques, have been proposed and studied worldwide in both academic and industries. Most of the gains claimed in the literature are from simulations or by small scale lab testing. Instead, this paper elaborates on the first hand results and analysis of the large-scale field trials carried out in China for a selection of key 5G technology components as well as some of their combinations, including filtered-orthogonal frequency division multiplexing (OFDM) (f-OFDM), polar code, sparse code multiple access, and massive multi-input multi-output (MIMO) for both uplink and downlink cellular networks. Testing results verify the feasibility and benefit of each technology contributing toward the diversified 5G targets, as well as the feasibility for these technologies to work jointly for higher spectrum efficiency, larger connectivity, and lower latency.

In this paper, a novel iterative soft joint detection algorithm for orthogonal frequency division multiplexing (OFDM) systems is proposed. Without the need of channel estimation, it directly generates the a posteriori probabilities (APPs) of data symbols by very efficient computation. With the increase of iterations, the proposed algorithm gradually achieves near-optimal performance, while its complexity is only linear in the number of subcarriers.

Generally, channel estimation is indispensable for signal detection in vertical Bell Laboratories layered space-time (V-BLAST) systems. However, channel estimation always cannot perfectly obtain the channel state information (CSI), and the inaccurate CSI may result in large performance degradation. In this paper, we propose a novel soft V-BLAST detector, called integrated maximum a posteriori (IMAP) detector. Unlike conventional detectors, the IMAP detector can avoid estimating the CSI and directly generate a posteriori probability of data symbols by efficient iterative computations. Moreover, we design a list-based fast searching algorithm to further reduce the complexity of the proposed detector in the cases of large numbers of transmit antennas or high order modulations. Computer simulation shows that the IMAP detector can approximately achieve the performance of the optimal detector using the perfect CSI.

In this paper, we investigate soft interference cancellation algorithms for vertical Bell Laboratories layered space- time (V-BLAST) systems. By using Gaussian-forcing technique and fast matrix computation, we first derive a simplified version of the conventional generalized probabilistic data association (GPDA) algorithm, called the fast soft interference cancellation (FSIC) algorithm, whose complexity is only cubic in the number of transmit/receive antennas. Then, based on the FSIC algorithm, we develop an enhanced soft interference cancellation (ESIC) algorithm by making use of the properties of MIMO channels. It is shown that with efficient iterative processing, the ESIC algorithm can achieve near-optimal performance in both uncoded systems and coded systems.

The maximum-likelihood (ML) detection is the optimal detection for vertical Bell Laboratories layered space-time (V-BLAST) systems if the channel is perfectly known at the receiver. Unfortunately, the perfect channel knowledge is always unavailable in practical systems. In this paper, we investigate the joint design of channel estimation and data detection for V- BLAST systems in spatially correlated channels, and develop an optimal joint channel estimation and data detection scheme under the ML criterion. To reduce its complexity, we also propose a simplified scheme based on list sphere decoding (LSD). Moreover, we analyze its relationship with the conventional optimal channel estimation and the conventional optimal detection, and evaluate its performance by computer simulation.

In this paper, a joint maximum a posteriori (MAP) detection algorithm is proposed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO- OFDM) systems. Without the need of estimating the channel state information (CSI), it can directly compute the a posteriori probability (APP) of the transmitted data and generate the detection results under the MAP criterion by efficient iterative processing. Based on the idea of list sphere decoding, we also present a fast searching algorithm to further reduce its complexity in the cases of large numbers of transmit antennas and high data rates. Computer simulation shows that even without the help of channel estimation, the proposed algorithm with a few iterations can approximately attain the performance of the optimal detector using the perfect CSI.

In this paper, we propose a novel soft-decision detector, called integrated maximum a posteriori (IMAP) detector, for vertical Bell Laboratories layered space-time (V-BLAST) systems. The proposed detector can avoid estimating channel state information (CSI) and directly generate a posteriori probability of data symbols via efficient iterative computations. Moreover, a list-based fast searching algorithm is developed to further reduce the complexity of this detector in the cases of large numbers of transmit antennas or high data rates. Computer simulation shows that the IMAP detector can approximately achieve the performance of the optimal detector using the perfect CSI.

In this paper, the minimum mean square error (MMSE) channel estimation for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is investigated. We first propose an optimal MMSE channel estimation algorithm, which can fully exploit the channel correlations over space, time, and frequency domains to estimate the channel frequency response. By the principle of maximum entropy, we then design an efficient robust MMSE channel estimation algorithm, which does not need to know spatial and time correlations and has a complexity only linear in the number of subcarriers. Moreover, computer simulation shows that the robust algorithm has no significant performance degradation in comparison with the optimal algorithm.

In this paper, a general minimum mean square error (MMSE) channel estimation algorithm is proposed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in spatially correlated multipath fading channels. It can make full use of the channel correlations in space, time, and frequency to estimate the channel state information for various systems, including pilot-symbol- assisted systems, pilot-embedded systems, and blind systems. To improve the computational efficiency of the proposed algorithm, we then derive a fast implementation algorithm with the complexity only linear in the number of subcarriers. Finally, we analyze the performance of the proposed algorithm for different systems in spatially independent and correlated channels.

In this paper, an iterative soft multiuser detection (MUD) algorithm is proposed for multiple-input multiple-output multi-carrier code-division multiple access (MIMO MC-CDMA) systems. Based on an equivalent system model, an efficient soft MUD algorithm is first derived by the central limit theorem, the property of the complex Gaussian distribution, and fast matrix computation formulas. By iteratively using the soft MUD algorithm with some updated probability information, we then obtain the proposed iterative soft MUD algorithm. Finally, it is shown that this algorithm achieves near-optimal performance with very efficient iterative computations.

A novel adaptive transmission scheme is proposed to improve the computational efficiency of conventional adaptive algorithms for multiple-input multiple-output orthogonal frequency-division multiplexing systems. By exploiting the stationary ergodicity of subchannel amplitudes, the proposed scheme can reduce the complexity of any existing adaptive algorithms to a considerably low level that is linear in the number of subchannels, while it causes little performance degradation.

A novel fast recursive minimum mean square error successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed. In this algorithm, the MMSE Alter matrices and the optimal detection order are successively computed from the previously obtained filter matrices according to simple recursive pseudoinverse formulas, so that the algorithmic complexity is reduced significantly, especially for the practical number of transmit/receive antennas.

In this paper, we present the soft version of the Generalized Probabilistic Data Association (GPDA) algorithm, which has two implementation modes: successive GPDA (S-GPDA) and parallel GPDA (P-GPDA), and apply it to V-BLAST architecture employing joint iterative detection and decoding. In addition, we investigate the soft interference cancellation with minimum-mean-square-error criterion (SIC-MMSE) algorithm, and prove that it is equivalent to P-GPDA with one inner-loop (detector) iteration. Simulation results are provided to verify this conclusion. From the simulation results, we can also see the following findings when sufficient innerloop/outer-loop (detector/decoder) iterations are employed: 1). the performance of P-GPDA is a little better than that of S-GPDA. 2). P-GPDA with one inner-loop iteration, which provides a good approximation to the maximum a posteriori (MAP) detection performance, has better performance than P-GPDA with more inner-loop iterations.

In this paper, we propose a novel low-complexity recursive minimum mean square error successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems. Different from the conventional V-BLAST detection algorithm, the proposed algorithm is capable of computing both the nulling vectors and the optimal detection order from the previous computational results via simple recursive methods, so that the algorithmic complexity is reduced significantly. Complexity and performance analyses show the proposed algorithm not only guarantees the optimal detection performance but is computationally more efficient than the existing algorithms.

Maximum-likelihood detection (MLD) algorithm is the optimal detection scheme for vertical Bell Laboratories layered space-time (V-BLAST) system. However, it is generally infeasible to implement MLD algorithm in practical systems due to its high computational complexity. In this paper, we utilize two parallel interference cancellation techniques including pre-cancellation algorithm and post-cancellation algorithm to simplify MLD algorithm to a computationally efficient suboptimal detection algorithm, herein called generalized parallel interference cancellation (GGPIC) algorithm. Numerical analysis shows GPIC algorithm can approximately achieve the MLD performance with considerably low and stable computational complexity. Since most steps of GPIC algorithm are parallel, it can be accelerated significantly via parallel processing methods. With near-optimal performance, stable and low complexity and fast processing speed, it is typically suitable for real-time high-speed systems.

The conventional detection algorithm for vertical Bell Laboratories layered space-time (V-BLAST) systems suffers from high computational complexity. In this paper, we develop a new fast recursive V-BLAST detection algorithm, called Greville-to-Inverse-Greville (GIG) algorithm. The core of this algorithm is a computationally efficient recursive pseudoinverse algorithm that can use the weight matrix of the first iteration to recursively compute all the other weight matrices. It can be considered as the inverse of the well-known Greville algorithm, and thus we call it Inverse Greville (IG) algorithm. By applying Greville algorithm to compute the weight matrix of the first iteration and IG algorithm to determine the others, we get the proposed GIG algorithm. Compared with the other existing fast algorithms, GIG algorithm has lower computational complexity and faster calculating speed for any number of transmit/receive antennas, even without any loss of performance.

In this paper, we propose double-orthogonal coded space-time-frequency spreading code-division multiple-access (DOC-STFS-CDMA) scheme for the downlink orthogonal frequency-division multiplexing (OFDM) systems employing multiple transmit and receive antennas. First, we introduce the so-called STFS-CDMA and the corresponding minimum mean-square error (MMSE) detection algorithms for three scenarios with different user information. Then, we propose a new class of spreading codes, called double-orthogonal code (DOC), to match the MMSE detection algorithms for STFS-CDMA system. With the special double orthogonality, DOC can effectively exploit space, time, and frequency diversity to enhance the performance of MMSE detectors, and provide the well-balanced signal-to-interference-and-noise ratio to different users. Finally, the performance gain of the proposed scheme over conventional schemes is shown by computer simulation.

In this paper, we present the ergodic capacity limits of time-varying multiple-input multiple-output (MIMO) channels in flat fading and frequency-selective fading environments. By using a nonstationary method, called generalized water-filling algorithm (GWFA), the transmitted power is optimally allocated over all the channel states in three dimensions including space, time and frequency. Therefore, the highest capacity (i.e., capacity limit) of time-varying MIMO channels is obtained. Compared with the conventional water-filling algorithm, GWFA can achieve higher capacity via more efficient computation.

In this paper, we present a new ultra-wideband (UWB) pulse design method for narrowband interference (NBI) suppression. With a short time duration, the obtained UWB pulses not only meet the Federal Communications Commission (FCC) spectral mask, but also dramatically suppress the mutual interference between UWB systems and multiple narrowband communication systems. This method successfully solves the coexistence problem between UWB systems and the existing narrowband systems without the need of reducing UWB pulse power spectral density (PSD) over the whole UWB frequency band. Therefore, the application range of UWB systems can be enlarged by increasing the transmitted UWB pulse power.

We investigate robust pilot-symbol-aided channel estimation and prediction for multiple-input multiple-output (MIMO) systems in fast fading environments. First, we derive the design criteria of the optimal pilot blocks for energy, power and bandwidth-limited systems, respectively. Then, in order to exploit channel time correlation sufficiently, we propose a robust minimum mean square error (MMSE) channel estimator. By simply adjusting some parameters, the MMSE estimator can work as an estimator and a predictor simultaneously. Finally, simulation results show the proposed MMSE estimator, with a little additional complexity, is considerably insensitive to channel statistics and significantly outperforms conventional estimators in fast fading environments.