AoD-Adaptive Channel Feedback in FDD Massive MIMO Systems with Multiple-Antenna Users

Channel feedback is essential in frequency division duplexing (FDD) massive\nmultiple-input multiple-output (MIMO) systems. Unfortunately, previous work on\nmultiuser MIMO has shown that the codebook size for channel feedback should\nscale exponentially with the number of base station (BS) antennas, which is\ngreatly increased in massive MIMO systems. To reduce the codebook size and\nfeedback overhead, we propose an angle-of-departure (AoD)-adaptive subspace\ncodebook for channel feedback in FDD massive MIMO systems. Our key insight is\nto leverage the observation that path AoDs vary more slowly than the path\ngains. Within the angle coherence time, by utilizing the constant AoD\ninformation, the proposed AoD-adaptive subspace codebook is able to quantize\nthe channel vector in a more accurate way. We also provide performance analysis\nof the proposed codebook in the large-dimensional regime, where we prove that\nto limit the capacity degradation within an acceptable level, the required\nnumber of feedback bits only scales linearly with the number of resolvable\n(path) AoDs, which is much smaller than the number of BS antennas. Moreover, we\ncompare quantized channel feedback using the proposed AoD-adaptive subspace\ncodebook with analog channel feedback. Extensive simulations that verify the\nanalytical results are provided.\n

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

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.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) multiuser massive MIMO systems. In this paper, we propose an information-theoretic metric to design the pilot for downlink channel estimation in FDD multiuser massive MIMO systems. By exploiting the low-rank nature of the channel covariance matrix, we first derive the minimum number of pilot symbols required to ensure perfect channel recovery, which is much less than the number of antennas at the base station. Further, under a general channel model that the channel vector of each user follows a Gaussian mixture distribution, the pilot symbols are designed by maximizing the weighted sum of the Shannon mutual information between the measurements of the users and their corresponding channel vectors on the complex Grassmannian manifold. Simulation results demonstrate the effectiveness of the proposed information-theoretic pilot design for the downlink channel estimation in FDD massive MIMO systems.

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.

In this paper, we consider the problem of channel feedback in massive multiple-input-multiple-output (MIMO) systems. For the downlink scenario, we present a detailed comparison between the performance of the quantized and the analog channel feedback schemes for the case of having multiple antenna users. Both schemes’ performance is evaluated by deriving an upper bound on the rate gap between the rate of the system with perfect channel state information (CSI) and with imperfect CSI for both feedback schemes. We compare the two schemes, namely, quantized channel feedback and analog channel feedback, under the same resources allocated for channel feedback for a fair comparison. Moreover, we consider two different downlink transmission schemes; the first one does not consider power allocation across the streams and the second one does power allocation (water-filling) across the streams. Our results show that the analog feedback scheme performs better in the low signal to noise (SNR) region when performing power allocation across the multiple data streams. However, the quantized channel feedback scheme performs better at the high SNR region, where the quantized CSI can provide a better approximation of the actual CSI. Finally, simulation results are presented to verify our theoretical analysis and demonstrate our conclusions.

Chapter 7 - Channel feedback for mmWave massive MIMO

In frequency-division duplexing (FDD) massive multiple-input-multiple-output (MIMO) systems, channel estimation about reducing the training and feedback overhead has always been a hot research area. The previous studies have shown that the method of utilizing channel sparsity in discrete Fourier transform (DFT) domain and angle reciprocity works well in particular channel environment. In this paper, we propose an improved method based on pilot reuse for general channel environment. The proposed method is applied to uplink/downlink channel estimation in FDD massive MIMO systems. Based on practical characteristics of the channel in massive MIMO systems, we modify the original method and propose an improved algorithm in assigning the pilot resources dynamically. The numerical results corroborate that compared to the original method, the improved method is more suitable and performs better in general channel environment. Meanwhile, the proposed algorithm can save the pilot resources and reduce the feedback overhead efficiently.

Massive multiple input multiple output (MIMO) transmission and coordinated multipoint transmission are candidate technologies for increasing data throughput in evolving 5G standards. Frequency division duplex (FDD) is likely to remain predominant in large parts of the spectrum below 6 GHz for future 5G systems. Therefore, it is important to estimate the downlink FDD channels from a very large number of antennas, while avoiding an excessive downlink reference signal overhead. We here propose and investigate a three part solution. First, massive MIMO downlinks use a fixed grid of beams. For each user, only a subset of beams will then be relevant, and require estimation. Second, sets of coded reference signal sequences, with cyclic patterns over time, are used. Third, each terminal estimates its most relevant channels. We here propose and compare a linear mean square estimation and a Kalman estimation. Both utilize frequency and antenna correlation, and the later also utilizes temporal correlation. In extensive simulations, this scheme provides channel estimates that lead to an insignificant beamforming performance degradation as compared to full channel knowledge. The cyclic pattern of coded reference signals is found to be important for reliable channel estimation, without having to adjust the reference signals to specific users.

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.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next generation wireless communication systems and for green communications. There are two operation modes: time-division duplex (TDD) and frequency-division duplex (FDD) for massive MIMO systems, and the debate on the two modes is still discussed in the literature. The originator of massive MIMO systems, Marzetta, has presented the basic frame structures for both the TDD and the FDD modes. However, the basic frame structures do not carefully take the hardware impairments such as phase noise (PN) into account. In this paper, by taking the PN as the key hardware impairment into account, we recommend new and efficient frame structures for both the TDD and the FDD modes. The corresponding performance comparison between the two modes is also provided by using the data transmission efficiency (DTE) as the performance measurement. The comparison may help determine which mode should be used for massive MIMO systems.

Massive multiple-input multiple-output (MIMO) will be employed in the 5th generation mobile communications (5G). To implement the massive MIMO, hybrid beamforming (HB) is one of the most promising techniques, because HB is composed of the analog beamforming (AB) and the digital beamforming (DB) and thus can decrease the number of baseband and radio frequency (RF) circuits significantly. Furthermore, HB can reduce a much larger amount of cost and power consumption than the full DB (FDB) configuration, which requires one baseband and RF circuit for each antenna element. This paper proposes a scheme to estimate both phases for phase rotators in AB and a precoding matrix for DB so that HB can approximate the block diagonalization (BD), which can nullify interference from the undesired users and form efficient transmit beams. For time division duplex (TDD) multiuser MIMO communications, the proposed scheme estimates the FDB channel matrix in the uplink (UL) and the phase for the phase rotators of AB in the downlink (DL). After obtaining an equivalent channel matrix from both the estimated FDB channel matrix and the AB precoding matrix, the scheme estimates the DB precoding matrix in DL from the equivalent channel matrix so that HB can approximate BD in DL. Computer simulations demonstrate that HB employing the proposed scheme can achieve DL average bit error rate (BER) performances that are little inferior to those of FDB.

Massive MIMO has been proposed as a means of achieving huge spectral efficiency in cellular networks. However, its implementation is usually based on linear precoding schemes using Channel State Information (CSI). For this reason, the use of the Time Division Duplex (TDD) mode is typically assumed since it removes the dependence on the number of antennas at the transmitter to acquire CSI. Unfortunately, most of the cellular network operate in the Frequency Division Duplex (FDD) mode in which the acquisition of CSI depends proportionally to the number of transmit antennas. For this reason, the implementation of Massive MIMO is handicapped to few transmit antennas for the current cellular networks. In this paper we study the use of reconfigurable antennas that can switch among a set of preset modes, i.e., radiation patterns, through Blind Interference Alignment schemes. We show that it is a solution to exploit a very large number of antennas at the transmit side, overcoming the bottleneck that today constitutes the acquisition of CSI in Massive MIMO systems operating in FDD mode.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.