Sum-rate maximizing beamforming in multicell systems with limited feedback

In Multi-user MIMO (MU-MIMO) downlink system, suitable user selection schemes can improve spatial diversity gain. In most of previous studies, it is always assumed that the base station (BS) knows full channel state information (CSI) of each user, which does not consider the reality. However, there are only limited feedback bits in real system. Besides, user fairness is often ignored in most of current user selection schemes. To discuss the user fairness and limited feedback, in this paper, the user selection scheme with limited feedback bits is proposed. The BS utilizes codebook precoding transmitting strategy with LTE codebook. Furthermore, this paper analyzes the influence of the number of feedback bits and the number of users on user fairness and system sum capacity. Simulation results show that in order to achieve better user fairness, we can use fewer bits for feedback CSI when the number of user is small, and more feedback bits when the number of users is large.

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.

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.

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.

In this paper, we propose a novel bit allocation strategy in coordinated multi-cell systems with limited feedback. Multi-cell cooperation technique is an effective way to improve the system throughput and especially the performance of the cell edge users. In a Coordinated Multi-Point (CoMP) system, the rate loss caused by quantization error of Channel State Information (CSI) is minimized considering two aspects of the channels. Firstly, as the physical conditions and constraints of different users are not identical, appropriately allocating feedback bits to users based on their channels can achieve higher throughput. Secondly, in a Coordinated Beamforming/Coordinated Scheduling (CB/CS) system, each user has a serving channel to the serving base station and an interfering channel to the coordinated base station. The user needs to partition its available bits between the two channels. Thus, we study a global bit allocation strategy that can minimize the rate loss from that of the full CSI circumstance. From our simulation results, we prove that the proposed bit allocation strategy achieves higher per-cell sum-rate compared with the equal bit allocation strategy.

The traditional limited feedback scheme can use imperfect channel state information to achieve interference alignment (IA) in a fully connected situation. But these solutions could no longer be used directly in heterogeneous networks where partial connectivity is considered. To solve this problem, this article proposes an IA scheme based on limited feedback with differentiation for the two‐tiered heterogeneous network. First, users who are affected by different types and numbers of interference and corresponding base stations are divided into different units according to the partial connectivity of heterogeneous networks. Then, we design interference alignment algorithm for each unit separately. Next, considering the imperfect channel state information, we derive the leakage of interference caused by the limited feedback, and give expressions for the upper bound of sum rate loss in various units. Finally, dynamic feedback bit allocation is carried out to each unit with the goal of minimizing the throughput loss in the limited feedback system. The simulation results show that the proposed IA scheme can eliminate interference well and efficiency of limited feedback can be improved through dynamic feedback bit allocation, so that system can obtain higher sum rate than other schemes.

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) at the base station (BS) is crucial to achieve beamforming and multiplexing gains in multiple-input multiple-output (MIMO) systems. State-of-the-art limited feedback schemes require feedback overhead that scales linearly with the number of BS antennas, which is prohibitive for 5G massive MIMO. This paper proposes novel limited feedback algorithms that lift this burden by exploiting the inherent sparsity in double-directional MIMO channel representation using overcomplete dictionaries. These dictionaries are associated with angle of arrival and angle of departure that specifically account for antenna directivity patterns at both ends of the link. The proposed algorithms achieve satisfactory channel estimation accuracy using a small number of feedback bits, even when the number of transmit antennas at the BS is large-making them ideal for 5G massive MIMO. Judicious simulations reveal that they outperform a number of popular feedback schemes and underscore the importance of using angle dictionaries matching the given antenna directivity patterns, as opposed to uniform dictionaries. The proposed algorithms are lightweight in terms of computation, especially on the user equipment side, making them ideal for actual deployment in 5G systems.

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

We present a generalized singular value decomposition (GSVD) based multi-user (MU) multi-stream multiple-input multiple-output (MIMO) precoding scheme with limited feedback. Our precoding scheme maximizes the signal to leakage and noise ratio (SLNR), so that it does not require any iterations to obtain precoding matrices. However, in MU-MIMO system, the precoding matrix should be computed at transmit side, because the precoding matrix of a user affects those of other users. Accordingly, full channel state information (CSI) for all users is required at the transmitter, which prevents MU-MIMO precoding implementable. To overcome this problem, an efficient limited feedback algorithm optimized to the GSVD based MU-MIMO precoding is proposed. Here, by extracting the parameters to be utilized in GSVD based precoding from the full CSI, the amount of feedback information is reduced. Furthermore, by analyzing their statistical properties, the parameters are efficiently quantized according to their probability densities. Finally, we develop a modified GSVD based precoding considering the channel quantization errors due to the limited feedback.

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.

We study strategies for enhanced secrecy using cooperative jamming in secure communication systems with limited rate feedback. A Gaussian multiple-input multiple-output (MIMO) wiretap channel with a jamming helper is considered. The transmitter and helper both require channel state information (CSI), which is quantized at the receiver and fed back through two sum-rate-limited feedback channels. The quantization errors result in reduced beamforming gain from the transmitter, as well as interference leakage from the helper. First, under the assumption that the eavesdropper's CSI is completely unknown, we derive a lower bound on the average main channel rate and find the feedback bit allocation that maximizes the jamming power under a constraint on the bound. For the case where statistical CSI for the eavesdropper's channel is available, we derive a lower bound on the average secrecy rate, and we optimize the bound to find a suitable bit allocation and the transmit powers allocated to the transmitter and helper. For the case where the transmitter and helper have the same number of antennas, we obtain a closed-form solution for the optimal bit allocation. Simulations verify the theoretical analysis and demonstrate the significant performance gain that results with intelligent feedback bit allocation and power control.

SummaryIn this paper, coordinated limited feedback zero‐forcing beamforming multicell system in time‐varying channel is studied. In time‐varying channels, the actual rate loss arising from error in channel quantization of both interuser and intercell inferences is quantified in this work. Using the actual degradation of rate loss, a limited feedback bit allocation is proposed to reduce interuser and intercell interference simultaneously with feedback update period as one of the parameters. Furthermore, the scaling law of bits required to maintain a constant rate loss is formulated in varying channel conditions for a given feedback update period. Simulation results demonstrated the practical feedback requirement in multicell systems in the presence of both intercell and interuser interference over conventional schemes to maintain a constant rate offset. The key finding from the proposed feedback allocation is that practically much higher allocation of feedback bits and feedback scaling are required in time‐varying channels to reduce throughput degradation.

We consider a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation where some of the instantaneous channel state information (CSI) is not available at the BSs. The proposed precoding method is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow a partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSI of that user is not obtained at the BSs. The other inter-user interferences are set to zero based on the block diagonalization of the channel matrix. The proposed method efficiently uses the degree of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference to the users with limited instantaneous CSI feedback. Simulation results show that the proposed method increases the capacity for a given transmission power or decreases the transmission power for a given capacity requirement compared to the case with full orthogonal transmission.