Partial Channel State Information Feedback Research Articles

Decomposition of uplink multi-user OTFS channel with partial CSI feedback

This work studies system design methods for the uplink multi-user orthogonal time–frequency space (OTFS) channel which forms a virtual multiple-input multiple-output (MIMO) communication system. For such system, the received signal contains interferences in the space, frequency, and time domain at the same time. To reduce the computational complexity, this work proposes to decompose the original large MIMO channel into parallel small sub-channels in the following order: first to decompose in the space domain, then to decompose in the time domain, thereby reducing the computational complexity. To help achieve channel decomposition, the proposed method requires the transmitter to perform precoding that needs channel state information (CSI) feedback. However, the proposed method only needs partial CSI feedback including delay and Doppler shift, so the feedback burden is small. Simulation results of the bit error rate (BER) performance verify that the proposed channel decomposition method is effective.

Partial and statistical CSI based precoding for reduced‐CP OTFS with inter‐slot‐interference pre‐cancellation

This work studies the precoding methods for the reduced cycle-prefix (CP) orthogonal time frequency space (OTFS) modulation to simplify the receiver design. First, a system model is established which shows that there are both intra-slot-interference and inter-slot-interference in the reduced-CP OTFS system. Then, the precoding operation is proposed to pre-cancel the inter-slot-interference in the reduced-CP OTFS system so that the bit error rate (BER) performance can be greatly improved. According to how much channel state information (CSI) needs to be fed back, precoding methods for two different cases are proposed: the partial CSI feedback case and the partial and statistical CSI feedback case. Among them, the partial and statistical CSI based precoding method has very small feedback burden. Simulation results of the BER performance of the proposed precoding methods for reduced-CP OTFS are reported and compared under different system parameters.

Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

Deep Learning-Based Power Control Scheme With Partial Channel Information in Overlay Device-to-Device Communication Systems

In the overlay device-to-device (D2D) communication systems, transmit power control is critical to better manage interference, so that the sum rate is maximized. Such power control for sum-rate optimization is NP-hard, which is typically tackled by iterative algorithms such as weighted minimum mean square error (WMMSE) method. However, the iterative power control schemes inherently incur high complexity and excessive latency. To overcome the limitations, we propose a deep learning-based power control scheme with reduced complexity and latency, where partial and outdated channel state information (CSI) is considered. Using a deep neural network (DNN)-based approach, we formulate an optimization problem to maximize the spectral efficiency under the constraints of user fairness and energy efficiency, where the DNN-based method is based on unsupervised learning with no label data generation process. In addition, a CSI reporting method based on the channel-to-interference power ratio is proposed for partial CSI feedback, which considerably reduces the feedback overhead. Through simulations, we show the results of the spectral efficiency, energy efficiency, and fairness performance for various topographical sizes and channel correlation coefficients. Also, it is shown that the proposed scheme achieves better spectral efficiency and energy efficiency than the WMMSE scheme even when it uses a small amount of CSI feedback.

Delay-Sensitive Area Spectral Efficiency: A Performance Metric for Delay-Constrained Green Networks

This paper introduces a new metric, referred to as delay-sensitive area spectral efficiency (DASE), to analyze the performance of delay-constrained wireless networks. We study DASE in different point-to-point, spectrum sharing, interference, and Poisson-point process-based dense network configurations and with different levels of channel state information (CSI) at the transmitters. Also, we determine the optimal rates/powers optimizing DASE. Finally, we use some recent results on finite block-length codes to analyze the effect of the codewords length on the network DASE. As demonstrated, DASE is a useful metric for analyzing delay-sensitive green networks. Moreover, adaptive power allocation and partial CSI feedback are powerful tools for performance improvement in green networks.

Optimal Design of Random Unitary Beamforming for Energy Efficiency in MIMO Broadcast Channels

Random unitary beamforming (RUB) achieves multiuser diversity gain over multiple-input multiple-output broadcast channels with partial channel state information (CSI). RUB can asymptotically achieve the same growth of rate $M\log \log K$ as the optimal dirty paper coding or sub-optimal zero-forcing beamforming, which need full CSI at the transmitter. In this paper, we propose energy efficient RUB systems that select the optimal number of streams and transmission power to maximize energy efficiency (EE). In contrast to other beamforming schemes that use full CSI, optimizing RUB in terms of EE is a difficult task due to the CSI constraints. To solve this problem, we first select the EE-optimal number of streams and transmission power by using the statistical characteristics of RUB, which have been studied in previous works. Next, based on partial CSI feedback from users, we propose the method whereby the transmitter of RUB can adaptively control the transmission power. Simulation results demonstrate that our proposed systems can improve the EE performance of RUB and that the analytical results derived in this paper are accurate.

Robust power control for femtocell networks under outage-based QoS constraints

This paper studies a robust uplink power control scheme with imperfect channel state information (CSI) in two-tier femtocell network systems. Aiming at enhancing the system’s robustness, we formulate an outage-based robust optimization problem. Specifically, we focus on two scenarios including partial CSI feedback and no CSI feedback. In the former scenario, channel gain error vectors obey multivariate Gaussian distributions. We propose a novel transformation method, which is based on Q-radial distribution, to obtain the closed-form expressions. In the latter scenario, channel gain includes path loss attenuation and Rayleigh fading. For the complex channel models, a novel method is given to extract closed-form approximations. Then, two iterative algorithms are provided. Also, admission control algorithm is applied. Finally, simulation results are given to show the validity of our proposed algorithms.

Minimization of CSI Feedback Dimension for Interference Alignment in MIMO Interference Multicast Networks

It is well-known that interference alignment (IA) can achieve substantial theoretical gains in multiple-input multiple-output (MIMO) networks. However, the conventional works usually assume the full channel state information (CSI) is available at the transmitter side, which would create a overwhelming CSI feedback overhead for practical frequency-division duplexing (FDD) wireless systems. To implement IA in practice, it is highly desirable to reduce the amount of required CSI feedback overhead. In this paper, we consider IA in MIMO interference multicast networks under partial CSI feedback, and we attempt to minimize the CSI feedback cost subject to IA feasibility constraints with a given degree of freedom (DoF) requirements. First, we propose a CSI feedback profile to embrace two important CSI feedback reduction strategies and we use the metric of CSI feedback dimension to quantify the associated CSI feedback cost. We then formulate the IA conditions under partial CSI feedback in MIMO interference multicast networks and we derive new IA feasibility conditions under the proposed partial CSI feedback framework. Based on these results, we consider the CSI feedback dimension minimization subject to the IA feasibility constraints with a given DoF requirements in MIMO interference multicast networks, which is formulated as a combinatorial optimization problem. Based on the specific problem structure, we derive an asymptotically optimal solution for a category of network topologies and we further obtain closed-form tradeoff results between DoFs and the CSI feedback cost for MIMO interference multicast networks.

Interference Alignment With Partial CSI Feedback in MIMO Cellular Networks

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. However, most existing IA designs require full channel state information (CSI) at the transmitters, which would lead to significant CSI signaling overhead. There are two techniques, namely CSI quantization and CSI feedback filtering, to reduce the CSI feedback overhead. In this paper, we consider IA processing with CSI feedback filtering in MIMO cellular networks. We introduce a novel metric, namely the feedback dimension, to quantify the first order CSI feedback cost associated with the CSI feedback filtering. The CSI feedback filtering poses several important challenges in IA processing. First, there is a hidden partial CSI knowledge constraint in IA precoder design which cannot be handled using conventional IA design methodology. Furthermore, existing results on the feasibility conditions of IA cannot be applied due to the partial CSI knowledge. Finally, it is very challenging to find out how much CSI feedback is actually needed to support IA processing. We shall address the above challenges and propose a new IA feasibility condition under partial CSIT knowledge in MIMO cellular networks. Based on this, we consider the CSI feedback profile design subject to the degrees of freedom requirements, and we derive closed-form trade-off results between the CSI feedback cost and IA performance in MIMO cellular networks.

CSI Feedback Reduction for MIMO Interference Alignment

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.

Multi-user diversity with two-step channel state information feedback

This study proposes the performance of a scheduling scheme using two-step quantised channel state information (CSI) feedback. Considering fixed and random access networks, the results are obtained under both short- and long-term power constraints and for both single- and multi-layer transmission techniques. Further, the authors evaluate the asymptotic performance of the proposed scheme and show that, while the users scheduling is based on quantised CSI feedback in the first round, the second round of partial CSI feedback can be provided by either channel quantisation or hybrid automatic repeat request feedback, leading to the same forward channel average rate. In comparison with the case of no CSI feedback, the results indicate substantial average rate increment with limited number of feedback bits. Also, the average rate increases via successive partial CSI feedback when compared with one-step CSI feedback schemes.

Partially Non-orthogonal Block Diagonalization-Based Precoding in Downlink Multiuser MIMO with Limited Channel State Information Feedback

This paper investigates a precoding method in downlink multiuser multiple-input multiple-output (MIMO) transmission with multiple base station (BS) cooperation, where each user device basically feeds back the instantaneous channel state information (CSI) to only the nearest BS, but the users near the cell edge additionally feedback the instantaneous CSI to the second nearest BS among the cooperating BSs. Our precoding method is categorized as a form of multi-cell processing (MCP) [5], in which the transmission information to a user is shared by the cooperating BSs in order to utilize fully the degrees of freedom of the spatial channel, and is based on block diagonalization of the channel matrix. However, since some elements of the channel matrix are unknown, we allow partially non-orthogonal transmission. More specifically, we allow inter-user interference to users with limited instantaneous CSI feedback from the channel where the instantaneous CSIs of those users are not obtained at the BSs. The other sources of inter-user interference are set to zero based on the block diagonalization of the channel matrix. The proposed method more efficiently utilizes the degrees of freedom of the spatial channel compared to the case with full orthogonal transmission at the cost of increased inter-user interference. Simulation results show the effectiveness of the proposed method compared to the conventional approaches, which can accommodate the partial CSI feedback scenario, from the viewpoints of the required transmission power and achievable throughput.

On the Capacity and Design of Limited Feedback Multiuser MIMO Uplinks

The theory of multiple-input-multiple-output (MIMO) technology has been well developed to increase fading channel capacity over single-input-single-output (SISO) systems. This capacity gain can often be leveraged by utilizing channel state information at the transmitter and the receiver. Users make use of this channel state information for transmit signal adaptation. In this correspondence, we derive the capacity region for the MIMO multiple access channel (MIMO MAC) when partial channel state information is available at the transmitters, where we assume a synchronous MIMO multiuser uplink. The partial channel state information feedback has a cardinality constraint and is fed back from the basestation to the users using a limited rate feedback channel. Using this feedback information, we propose a finite codebook design method to maximize the sum rate. In this correspondence, the codebook is a set of transmit signal covariance matrices. We also derive the capacity region and codebook design methods in the case that the covariance matrix is rank one (i.e., beam- forming). This is motivated by the fact that beamforming is optimal in certain conditions. The simulation results show that when the number of feedback bits increases, the capacity also increases. Even with a small number of feedback bits, the performance of the proposed system is close to an optimal solution with the full feedback.

Power allocation and code construction for space–time turbo trellis codes with partial CSI feedback

Conventional space–time turbo trellis coding (STTuTC) schemes allocate transmit power equally to the available antennas. This is not optimal if channel state information (CSI) is available at the transmitter. A STTuTC scheme is considered with partial CSI knowledge that optimally allocates power to the transmit antennas. This scheme is referred to as STTuTC with dynamic transmit power allocation (STTuTC/DTPA). The optimum four-state constituent code and power allocation are presented. Simulation results show that the STTuTC/DTPA scheme with the new code outperforms conventional STTuTC schemes.