On distributed antenna systems with limited feedback precoding: Opportunities and challenges

Downlink multi-user transmission techniques that allow several users to be served simultaneously in frequency and time have been recognized as a promising means to increase system capacity. However they require accurate channel state information (CSI) at the base station (BS) such that appropriate signal processing can be performed to separate multiple users in the space domain. In practice however, CSI cannot be perfectly known to the BS due to the capacity-limited feedback channel in FDD systems for instance. In this letter we investigate multiuser multiple-input single-output (MU-MISO) transmission with limited feedback. In particular we identify the useful information that the BS needs to deal with inter-user interference and a codebook design is derived accordingly. In addition, an adaptive threshold-based feedback approach is proposed, where only users with good feedback quality are allowed to do feedback. It is shown that the adaptive scheme can effectively improve the system performance given a fixed number of feedback bits.

Distributed antenna systems (DASs) are known to be effective to enhance coverage, spectral efficiency, and reliability in mobile communication systems. Because multiple antennas are physically separated in space, DASs benefit from both micro- and macro-diversity, which makes DASs significantly more robust compared to conventional co-located antenna systems in fading channels. However, when multiple antennas are not dispersed enough, there exists a certain degree of correlation in large-scale fading (shadowing), which degrades the macro-diversity gain. In practice, various measurements indicate a high degree of correlation of shadowing in DASs. However, most of the previous studies on DASs have not considered the correlated shadowing and its corresponding performance loss. Motivated by this limitation, we analyze the impact of the correlated shadowing to better evaluate DAS-based schemes with dual diversity transmitters. Assuming that shadowing correlation is an exponentially decreasing function of the inter-element separation, we derive the outage probability of DAS in composite Rayleigh-lognormal shadowing channels. Also, we present numerical and simulation results, which indicate there exists an optimal inter-separation between antennas that minimizes the outage rate to balance a trade-off between macro-diversity and path loss.

We propose the Optimum Power Allocation (OPA) scheme for Distributed Antenna Systems(DAS) in the time-varying Rayleigh fading channel. Recently, the OPA schemes which uses the Channel State Information (CSI) including a small scale (fast) fading have been proposed. However, the channel is changing vary fast over time due to small scale fading, therefore Bit Error Rate (BER) increases. Because of this reason, we derive the OPA for minimizing BER in DAS, which only uses a large scale fading to CSI and excepts a small scale fading. The simulation results show that the proposed OPA achieves better BER performance than conventional OPA considering a small scale fading in time-varying Rayleigh fading channel, and also has similar performance in Rayleigh flat-fading environment. The BER performance of proposed OPA which derived in Rayleigh fading channel is similar to minimum BER of Ricean fading channel which has small Line-of-Sight (LOS).

The provision for spectrally efficient multicast broadcast services (MBS) is one of the key functional requirements for next generation wireless communication systems. The challenge inherent to MBS is to ensure that all MBS users can be served, and one effective solution to this problem is to employ MIMO multicast transmit precoding. In previous works on MIMO multicast transmit precoding design, the authors either assumed (1) perfect transmitter-side channel state information (CSIT) or (2) special channel conditions that facilitate precoder design with imperfect CSIT. In this paper, we focus on transmit precoding design for MBS where the CSIT is obtained via limited feedback. In addition, we analyze the average minimum receive signal-noise-ratio (RxSNR) among the MBS users and study the order of growth with respect to the number of MBS users, the number of feedback bits, and the number of transmit antennas. Finally, we propose a threshold based feedback reduction scheme and study the tradeoff between feedback cost and performance loss.

This paper proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.

Comparison of downlink transmission strategies for distributed antenna systems with multi-antenna arrays

Opportunistic packet scheduling optimization problem in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) is investigated by this paper. Physical-layer channel state information (CSI) and wireless links' requirements for bit error rate are taken into account during problem formulation. The optimization aim is to minimize packet loss rate (PLR) subject to limited wireless resource, which is characterized by power, subcarrier, antenna et al. According to the characteristics of users' packet queues as well as CSI, the proposed algorithm adaptively allocates wireless resource to users to exploit as much spatial and multiuser diversity as possible. Simulation results show the proposed algorithm achieves the lowest PLR in all compared algorithms. Further more, the proposed circular DAS significantly outperforms co-located antenna systems (CAS) due to distributed antenna deployment.

Knowledge of the propagation channel makes it possible for multiple-input multiple-output (MIMO) links to achieve higher system capacities by means of beamforming on the stronger channel eigenvectors. Assigning dedicated low bit rate feedback links is an efficient and practical method of providing transmitters with the required channel state information. One of the key problems in limited feedback precoding is the design of finite indexed codebooks known a priori to both the transmitter and receiver.Previous design methods include signal-to-noise ratio maximization through Grassmannian line packing, distortion minimization via vector quantization and antenna selection algorithms. In this paper we propose a new method that utilizes the dominant right singular vector of the MIMO channel to construct quantized unitary precoders. We show that although sub-optimum, this method can produce full diversity order codebooks. Using Monte-Carlo simulations, we compare the performance of the this method with optimum Grassmannian precoders and also antenna selection and subset selection algorithms.

Limited feedback techniques, that employ codebook-based quantisation, are used in coordinated multi-point (CoMP) transmission to convey the channel direction information between the mobile user and each base station in the cooperative set and also among the cooperating base stations. However, unlike single-cell multiple-user multiple input multiple output (MU-MIMO) systems, CoMP systems face additional challenges in this codebook design. Among these challenges are the huge feedback overhead and the need for dynamic codebook size. These problems are solved when CoMP uses the so-called per-cell codebook, in which the channel to each cell is quantised separately. However, this results in a further problem, that is having random phase values between the quantised per-cell channels of the same user. In this study, the phase ambiguity problem is explained and its effect on performance degradation is quantified. A new quantisation technique is proposed where the quantisation of each of the real and the imaginary parts is performed independently using the same real codebook. This novel solution prevents the phase ambiguity problem instead of trying to mitigate its effects, in addition to simplifying the codebook design. The proposed quantisation technique outperforms two recently introduced solutions in the literature. The first solution is to quantise phase ambiguity as part of channel state information (CSI), whereas the other avoids phase ambiguity by an iterative selection procedure. Mathematical validations and simulations are provided to verify these findings.

A multihop regenerative relay system is considered. We explore adaptive power allocation (PA) under a total power constraint to minimize the system error probability when the perfect channel state information (CSI) is known at the transmitters. We propose adaptive PA schemes with limited feedback when the transmitters have no perfect knowledge of CSI. Simulation results demonstrate the good performance of the proposed PA scheme with perfect CSI. When no perfect CSI is available at the transmitters, the proposed PA schemes with a small number of feedback bits are able to achieve very close performance to the adaptive PA with perfect CSI.

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.

Due to the limitation of digital chains' number, the hybrid beamforming structure may be utilized for massive Multiple Input and Multiple Output (MIMO) systems, where the eNodeB may maintain a number of analog Tx beams. To achieve higher spectral efficiency, two analog beam interpolation methods are further proposed to enhance the accuracy of downlink analog beamforming, which based on amplitude or angle interpolation algorithm. In this paper, the typical channel state information(CSI) from user equipment (UE) is used to determine the candidate Transmitting (Tx) beams. Especially, by exploiting the spatial correlation of neighbor candidate beams, the best downlink analog Tx beam for each UE can be generated by interpolating the two strongest Tx beams from the Uplink Control Informat ion (UCI) report. Moreover, we develop a limited CSI feedback to alleviate the feedback load in traditional frequency division duplexing (FDD) Massive-MIMO systems, giving rise to better coverage and spectrum efficiency. Simulation results show that with the help of analog beam interpolation, the downlink spectrum efficiency can be significantly increased.

Very large multiple input multiple output (MIMO) system (referred to as massive MIMO) is currently investigated as a novel technology to enhance system throughput for future cellular systems. In this paper, we study a practical implementation of massive MIMO technology in LTE-Advanced systems. Firstly, two three dimensional(3D) dynamic beamforming schemes are proposed to realize full spatial resolution of the elevation as well as the traditional azimuth domain. These schemes are implemented through combining a couple of precoder matrix indictor (horizontal PMI and vertical PMI) during the channel state information (CSI) feedback stage. Furthermore, we also propose a limited feedback precoding algorithm named PMI-based regularized zero-forcing precoding (P-RZF) to achieve multi-user MIMO (MU-MIMO). System level simulation is performed to evaluate the proposed schemes.

Interference management is required for improving the performance of future mobile radio communication systems. In the downlink (DL) of cellular systems with base stations (BSs) located in the cell centers and equipped with omnidirectional antennas, transmission strategies such as matched filtering (MF) can't achieve good system performance, and zero forcing (ZF) with full channel state information (CSI) of the whole system imposes a high computational cost and requires a substantial amount of feedback. In this paper, an intelligent signal processing technique with moderate complexity, i.e., ZF joint transmission (JT) with significant CSI, based on distributed antenna systems (DASs) with BSs located at three vertices of each cell and equipped with sector antennas is proposed. From numerical results, it is shown that our proposal is a good combination of BS antenna layout and signal processing technique for interference management. The proposed intelligent signal processing technique can significantly benefit from the proposed smart BS antenna layout.

This study proposes an integrated framework of single-selection distributed antenna systems (DAS) and simultaneous wireless information and power transfer (SWIPT) with orthogonal multiple access (OMA), and analyzes the synergetic effects of SWIPT and DAS. To demonstrate the performance gains of the proposed SWIPT-enabled OMA with DAS, we jointly optimize power allocation for the OMA and power splitting (PS) ratio for energy harvesting of SWIPT with three different optimization goals: sum-rate maximization, user fairness, and energy efficiency. Although all of the above optimization problems are non-convex, the closed-form solutions of the problems maximizing the sum-rate and fairness are presented. Owing to difficult analytical tractability of the problem maximizing the energy efficiency, the problem is divided into three subproblems: power allocation for the base station (BS), power allocation for radio remote unit (RRU), and PS ratio assignment. Then, an iterative algorithm is presented to provide the energy-efficient SWIPT-enabled OMA with DAS. Theoretical findings are validated by numerical results, which show that the framework of applying DAS to SWIPT-OMA improves data rates and energy efficiency compared to SWIPT-OMA without DAS, while satisfying the minimum rate and harvested energy requirements.