Multiuser diversity with limited feedback

In cellular systems, deploying a large number of antennas at the base station (BS), also called massive multiple-input multiple-output (MIMO), can offer a huge improvement in system throughput. This performance gain achieved through coherent beamforming highly depends on having accurate channel state information (CSI) at the BS. Initially, massive MIMO was considered promising only for time division duplexing (TDD) systems because the downlink training overhead in frequency division duplexing (FDD) systems could be large. To reduce the overhead in FDD systems, we propose efficient eigenspace training and precoding. In the downlink training, the prebeamforming matrix is designed based on the spatial fading correlation to probe the channel and minimize the mean squared error (MSE) in channel estimation. In data transmission, the precoding matrix is designed based on instantaneous CSI to manage the inter-user interference. Our algorithms achieve significant savings in the downlink training because the overhead is associated with the dimension of prebeamforming matrix and no longer limited by the number of BS antennas. Compared with joint spatial division and multiplexing, our algorithms offer much lower MSE and, under some conditions, higher sum rates.

Multiple transmit antennas can be employed in a downlink channel to serve multiple users under the assumption of perfect channel state information (CSI) of all users at the base station. In practice, however, the channel knowledge available at the base station is not perfect. For instance, in case of a time division duplex (TDD) system, the transmit CSI is the estimated uplink channel. On the other hand, in a frequency division duplex (FDD) system, feedback from the users is required to obtain transmit CSI. In this case, we assume the transmit CSI consists of a quantized version of the normalized estimated channel vector and the statistics of the channel norm of each user. In this work we consider the transceiver design of the downlink of a multiuser MISO system based on the minimum mean square error (MMSE) criterion with imperfect CSI as discussed above.

Massive MIMO is a powerful multiuser/multiantenna technology that exploits a very large number of antennas at the base station side and the knowledge of the channel matrix between base station antennas and multiple users in order to achieve large beamforming and multiplexing gain. Classical massive MIMO exploits Time-Division Duplexing (TDD) and channel reciprocity, such that the channel matrix can be learned at the base station from the incoming uplink pilot signals sent by the users. However, the large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where channel reciprocity does not hold and explicit downlink probing and uplink CSI feedback are required in order to achieve some spatial multiplexing gain. Unfortunately, the overhead incurred by explicit probing and feedback is very large in massive MIMO, since the channels are high-dimensional random vectors. In this paper, we present a new approach to achieve very competitive tradeoff between spatial multiplexing gain and probing/feedback overhead in FDD massive MIMO. Our approach is based on two novel concepts: 1) an efficient and mathematically rigorous technique to extrapolate the channel covariance matrix from the uplink to the downlink, such that the second order statistics of each downlink channel can be accurately learned for free from uplink pilots; 2) a novel “sparsifying precoding” approach, that introduces sparsity in the channel in a controlled form, such that for any assigned overhead (i.e., downlink pilot dimension) it is possible to set an optimal sparsity level for which the “effective” channels after sparsification can be estimated at the base station with low mean-square error. We compare our method with that of the state-of-the-art compressed sensing (CS) based method. Our results show that the proposed method is much more robust than compressed sensing methods, since it is able to “shape the channel sparsity” as desired, instead of being at the mercy of nature (i.e., at the mercy of the natural sparsity induced by the nronaaation environment).

Orthogonal frequency division multiplexing (OFDM) is a practical technique for communicating over broadband channels in multi-path fading environments. It is well known that varying the power allocation from frequency tone-to-frequency tone, commonly called power loading, can improve capacity or error rate performance. Unfortunately, the implementation of power loading is complicated by the fact that the transmitter must have knowledge of the forwardlink channel. This assumption is often unrealistic, particularly in systems using frequency division duplexing (FDD). In this paper we present a limited feedback approach for power loading OFDM symbols. In this approach, the receiver, which is assumed to have full forwardlink channel knowledge, designs a power loading vector and conveys it to the transmitter over a limited rate feedback channel. The technique uses a codebook of power loading vectors known to both the transmitter and receiver. We design limited feedback schemes for error rate optimized and capacity optimized power loading. We show how to design quantizers using iterative optimization techniques from the theory of vector quantization for the case of error rate selection. In the capacity case, we characterize optimal feedback schemes for asymptotic signal-to-noise ratio (SNR) scenarios and present a simple method called multi-mode power loading that uses a codebook that switches subcarriers off and on. Simulation results show that the limited feedback techniques provide performance close to perfect channel knowledge power loading.

Joint linear beamforming and scheduling are performed in a system where limited feedback is present at the transmitter side. The feedback conveyed by each user to the base station consists of channel direction information (CDI) based on a predetermined codebook and a scalar metric with channel quality information (CQI) used to perform user scheduling. In this paper, we present a design framework for scalar feedback in MIMO broadcast channels with limited feedback. An approximation on the sum rate is provided for the proposed family of metrics, which is validated through simulations. For a given number of active users and average SNR conditions, the base station is able to update certain transmission parameters in order to maximize the sum-rate function. On the other hand, the proposed sum-rate function provides a means of simple comparison between transmission schemes and scalar feedback techniques. Particularly, the sum rate of SDMA and time division multiple access (TDMA) is compared in the following extreme regimes: large number of users, high SNR, and low SNR. Simulations are provided to illustrate the performance of various scalar feedback techniques based on the proposed design framework.

The frequency synchronization issue of multiuser in the uplink of an orthogonal frequency-division multiple-access (OFDMA) system is investigated in this letter. We develop a subspace based blind carrier frequency offsets (CFOs) estimation algorithm for the base station (BS) equipped with uniform linear array (ULA). Due to the adoption of ULA at the BS and the narrowband signal assumption, CFOs of different users can be separated by their spatial information. Thus, unlike existing blind alternatives, which are subject to some specific carrier assignment schemes (CAS) or rely on null subcarriers, our proposed method can support generalized carrier assignment scheme (GCAS) and fully loaded systems, i.e., all subcarriers are available to users. Consequently, the dynamic channel assignment is available and the bandwidth efficiency is higher. Moreover, the closed-form directions of arrival (DOAs) of all active users are also obtained, which can be used in downlink beamforming in frequency division duplex (FDD) systems. Performance of the proposed scheme is evaluated by computer Monte Carlo simulations.

Channel reciprocity is limited in frequency division duplex (FDD) system, and thus those channel reciprocity-based secret key extraction methods which are widely adopted in time division duplex (TDD) systems can hardly be implemented in FDD systems. In this paper, a novel secret key extraction method in OFDM-FDD system is proposed to achieve physical-layer security, by which the channel frequency response (CFR) in the uplink at the same time is estimated by the communication pair with the aid of a specially designed forwarding strategy based on confidential probing signal, and then the secret key is extracted from the CFR estimates. The effectiveness of the proposed method is verified by numerical simulations.

In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to optimize the beamforming matrices. In frequency division duplex (FDD) systems, full channel reciprocity does not hold, and CSI acquisition generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed the end-to-end design of pilot transmission, feedback, and CSI estimation via deep learning. In this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots. The proposed method is based on a novel deep learning architecture – HyperRNN – that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance, and that it reduces requirements in terms of pilot lengths.

Accurate channel state information (CSI) at base station (BS) is the key to achieve full multiplexing and diversity gain in traditional multiple-input multiple-output (MIMO) systems. In frequency-division-duplex (FDD) systems, it is obtained through downlink training and uplink feedback. However, the training and feedback overhead in massive MIMO systems is unacceptable for the large number of antennas equipped at BS. To reduce the CSI feedback overhead and improve CSI quantization accuracy, we propose a limited feedback scheme for spatio-temporal correlated massive MIMO channel. The proposed scheme jointly performs Karhunen-Loève transform (KLT) and polar-cap quantization on CSI. For the compactly deployed antennas at BS, there exists spatial correlation in massive MIMO channel. When the user equipment (UE) moves slowly, adjacent channels feature temporal correlation. The high dimensional and spatial correlated CSI is compressed through KLT. Then the compressed CSI is normalized to be isotropic to perform polar-cap quantization. Simulation results show that the proposed scheme is capable of improving the sum-rate performance with limited feedback bits for massive MIMO systems.

In this paper, we consider practical multiuser multiple-input multiple-output (MU-MIMO) systems where each user has multiple receive antennas and limited channel feedback is available at the base station (BS). The joint combiner and precoding scheme for downlink transmission is proposed, in which we consider the joint use of the receive antenna combiner at each user and precoding at the BS. In the proposed scheme, each user independently chooses the codeword as its preferred-beam vector from the predefined codebook, which maximizes the effective channel gain after the receive antenna combiner. Utilizing the received feedback from users, the BS can easily construct the precoding matrix by zero-forcing beamforming (ZFBF) criterion. In addition, a user selection algorithm is provided to select served users, when the number of users exceeds the number of transmit antennas. Numerical results show that the proposed scheme improves the sum-rate performance of MU-MIMO downlink systems.

Cell-free system where a group of base stations (BSs) cooperatively serves users has received much attention as a promising technology for the future wireless systems. In order to maximize the cooperation gain in the cell-free systems, acquisition of downlink channel state information (CSI) at the BSs is crucial. While this task is relatively easy for the time division duplexing (TDD) systems due to the channel reciprocity, it is not easy for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. This issue is even more pronounced in the cell-free systems since the user needs to feed back the CSIs of multiple BSs. In this paper, we propose a novel feedback reduction technique for the FDD-based cell-free systems. Key feature of the proposed technique is to choose a few dominating paths and then feed back the path gain information (PGI) of the chosen paths. By exploiting the property that the angles of departure (AoDs) are quite similar in the uplink and downlink channels (this property is referred to as angle reciprocity), the BSs obtain the AoDs directly from the uplink pilot signal. From the extensive simulations, we observe that the proposed technique can achieve more than 60% reduction in feedback overhead over the conventional CSI feedback scheme.

ABSTRACTIn this paper, several multi‐user scheduling algorithms with limited channel feedback are proposed for multi‐antenna downland channels. With little channel quality information (CQI) and channel direction information (CDI) at multi‐antenna base station, multi‐user scheduling algorithms are carefully designed to maximise the potential system throughput while reducing the feedback load. We first propose a scheduling algorithm that requires a quantised CDI and a threshold CQI. Then we propose a scheduling that needs limited feedback of a threshold CDI and a perfect CQI. Both scheduling algorithms are studied in terms of the achievable throughput and the feedback load. Analytical results show that the proposed scheduling with limited channel feedback has a small performance degradation compared with the scheduling with unlimited channel feedback when the thresholds for user scheduling are judiciously designed. Moreover, under a per user total bit rate constraint of the feedback channel, we propose a scheduling with the quantisation of both CQI and CDI. It is demonstrated that allocating 1 bit to the CQI with an optimal threshold and the remaining bits to the CDI achieves the near optimal sum‐rate performance. Copyright © 2011 John Wiley & Sons, Ltd.

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.

Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.

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.