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

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.

In this work we investigate the degrees of freedom (DoF) performance of the multiple-input multiple-output (MIMO) interference channel (IC) with limited channel state information (CSI) feedback. We generalise two space-time interference alignment (STIA) algorithms to the K user MIMO IC with a transmit power constraint and show how these schemes exploit both current and outdated CSI to achieve an improved DoF performance. We show that by combining the STIA schemes with traditional methods major DoF gain can be achieved for a variety of limited CSI feedback scenarios in comparison to what would be obtained via the use of the traditional techniques on their own. In particular we prove that optimal DoF can still be achieved in situations where current CSI is unavailable for a derived fraction of the time. Finally we validate the promised DoF results via system simulation and show how these DoF gains reflect in terms of sum rate achievable.

Recently, substantial attention has been paid to increase the achievable rates of wireless networks using different kinds of limited channel state information (CSI) feedback. This paper aims to investigate the effect of limited noisy CSI feedback on the achievable rates of power-limited block fading channels constrained to have limited outage probability. Considering different power allocation strategies and various fading conditions, the results are obtained for different outage probabilities and are compared with the case where there is no outage limitation. Moreover, considering different outage probability constraints, the effect of feedback channel noise on the average rates of the forward channel is studied and compared with the ones obtained under noise-free feedback link assumption. Simulation results show that 1) although the outage probability constraint reduces the channel average rate, its influence diminishes as the channel state information feedback or the input power increases, 2) the effect of the outage probability constraint decreases as the forward channel fading severity or the feedback channel noise is reduced.

Recently, substantial attention has been paid to increase the achievable rates of wireless networks using different kinds of limited channel state information (CSI) feedback. This paper aims to investigate the effect of limited CSI feedback on the achievable rates of power-limited block fading channels constrained to have limited outage probability. Considering different power allocation strategies and various fading conditions, the results are obtained for different outage probabilities and are compared with the case where there is no outage limitation. Simulation results show that 1) although the outage probability constraint reduces the channel average rate, its influence diminishes as the channel state information feedback or the input power increases, 2) the effect of the outage probability constraint decreases as the channel fading severity is reduced.

Block diagonalization (BD) as a low complexity MIMO technique has been considered for inter-cell interference mitigation based on the perfect channel state information (CSI) in many previous works. However, the imperfect CSI feedback is more practical and beneficial for lower complexity in real situation. Thus, in this paper, the performance of BD for inter-cell interference mitigation is investigated under the constraint of imperfect CSI feedback. Simulation results demonstrated that BD algorithm still can work efficiently with limited feedback for the inter-cell interference cancellation.

In the multiuser multiple-input multiple-output (MU-MIMO) system, to reduce the influence of channel correlation on system performance, the base station (BS) should select the appropriate subset of user equipments (UEs) according to their channel state information (CSI). Due to a lack of channel reciprocity, the downlink CSI needs to be fed back to the BS in frequency division duplexing (FDD) mode. Some scholars have exploited kinds of deep neural networks (DNNs) for sensing and recovering CSI. However, user selection after all the CSI is reconstructed by DNNs will bring a great time delay. In this paper, we propose a deep learning-based CSI feedback scheme called US-CsiNet. Based on adversarial autoencoder (AAE), US-CsiNet can explicitly cover user schedule information while representing CSI. At the UE side, the encoder of US-CsiNet maps the CSI into codewords of which part are feature information for user schedule. Then the BS applies these partial codewords to separate the UEs into different groups and select active UEs. Finally, the decoder of AAE reconstructs the CSI of these active UEs. US-CsiNet can not only simplify the user selection process but also guarantee the accuracy of CSI reconstruction. The simulation results show that the proposed approach outperforms maximum channel gain (MCG) user selection algorithms and achieves the nearly same performance with semiorthogonal user selection (SUS) which needs full CSI of all users at the BS.

In this paper, the ergodic sum rate and the outage probability of a downlink single-antenna channel with K users are analyzed in the presence of Rayleigh flat fading, where limited channel state information (CSI) feedback is assumed. Specifically, only 1-bit feedback per fading block per user is available at the base station. We first study the ergodic sum rate of the 1-bit feedback scheme and consider the impact of feedback delay on the system. A closed-form expression for the achievable ergodic sum rate is presented as a function of the fading temporal correlation coefficient. It is proved that the sum rate scales as log log K, which is the same scaling law achieved by the optimal nondelayed full CSI feedback scheme. The sum-rate degradation due to outdated CSI is also evaluated in the asymptotic regimes of either large K or low SNR. The outage performance of the 1-bit feedback scheme for both instantaneous and outdated feedback is then investigated. Expressions for the outage probabilities are derived, along with the corresponding diversity-multiplexing tradeoffs (DMTs). It is shown that, with instantaneous feedback, a power allocation based on the feedback bits enables doubling of the DMT compared with the case with short-term power constraint in which a dynamic power allocation is not allowed. However, with outdated feedback, the advantage of power allocation is lost, and the DMT reverts to that achievable with no CSI feedback. Nevertheless, for finite SNR, improvement in terms of outage probability can still be obtained.

In this paper, we present a new beamforming scheme for a downlink of multiuser multiple-input multiple- output (MIMO) communication systems. Recently, a block- diagonalization (BD) algorithm has been proposed for the multiuser MIMO downlink where both a base station and each user have multiple antennas. However, the BD algorithm is not efficient when the number of supported streams per user is smaller than that of receive antennas. Since the BD method utilizes the nullspace based on the channel matrix without considering the receive combining, the degree of freedom for beamforming cannot be fully exploited at the transmitter. In this paper, we optimize the receive beamforming vector under a zero forcing (ZF) constraint, where all inter-user interference is driven to zero. We propose an efficient algorithm to find the optimum receive vector by an iterative procedure. The proposed algorithm requires two phase values feedforward information for the receive combining vector. Also, we present another algorithm which needs only one phase value by using a decomposition of the complex general unitary matrix. Simulation results show that the proposed beamforming scheme outperforms the conventional BD algorithm in terms of error probability and obtains the diversity enhancement by utilizing the degree of freedom at the base station.

Closed-loop MIMO system can provide dramatic gains in channel capacity. However, mobile terminal (MT) must feedback the downlink channel state information (CSI) to Base station (BS), and transmitting CSI is a heavy burden consuming considerable uplink bandwidth. To address this bottleneck problem, a zero-overhead downlink CSI feedback scheme is proposed in this paper. After receiving the downlink pilots, MT directly superposes the uplink pilot on the received analog downlink pilot, forms the hybrid pilot (HP), and then transmits HP to BS via uplink pilot channel. Because downlink CSI can be recovered from HP at BS, and HP is fed back to BS via the uplink pilot channel, no extra uplink channel is occupied and the overhead for CSI feedback is zero. Moreover, since no CSI estimation process is performed at MT, the terminal's complexity can be reduced. Numerical Simulations verify that, the proposed downlink CSI feedback has the higher precision than the traditional feedback schemes, and the MSE of CSI feedback can be reduced significantly.

Multicell cooperative processing (MCP) can overcome the detrimental effects of inter-cell interference (ICI). However, since mobile terminals (MTs) need to estimate and feed back channel state information (CSI) related to several cooperative base stations (BSs), reductions in CSI feedback overhead and delay are more demanding in MCP enabled networks. In this paper, a novel overlaid-pilot based CSI feedback scheme for multicell cooperative networks is proposed. MTs directly overlay the received downlink pilot data of all the cooperative BSs on the uplink pilot data and then broadcast it to the cooperative BSs. Each cooperative BS can recover multiple CSIs from the overlaid pilot by a presented overlaid-pilot based channel estimation algorithm. Because the overlaid pilot is fed back on the uplink pilot channel, no extra uplink channel is occupied and the downlink CSI feedback process is with zero-overhead regardless of the number of cooperative BSs. In addition, as it just needs one broadcast to feed back multiple CSIs to BSs, the feedback delay is decreased significantly. Numerical simulations show that the performance of the proposed CSI feedback scheme outperforms the traditional feedback scheme.

In this paper, an expression for the user's achievable data rate in the multi-user multiple-input multiple-output (MU-MIMO) system with limited feedback (LF) of channel state information (CSI) is derived. The energy efficiency (EE) is optimized through power allocation under quality of service (QoS) constraints. Based on mathematical equivalence and Lagrange multiplier approach, an energy-efficient unequal power allocation (EEUPA) with LF of CSI scheme is proposed. The simulation results show that as the number of transmitting antennas increases, the EE also increases which is promising for the next generation wireless communication networks. Moreover, it can be seen that the QoS requirement has an effect on the EE of the system. Ultimately, the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation (EEEPA) with LF of CSI schemes.

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.

MIMO downlink with base station coordination in a cellular network is considered. In centralized coordinated transmission from a cluster of base stations, the channel state information (CSI) of users needs to be sent to a central processor for precoding and resource allocation. Real time CSI feedback from the users to their home base station and from the base stations to the central processor is a serious challenge from a practical point of view. CSI feedback schemes based on user channel strength and distance of a user to its home base station are proposed that reduce feedback overhead while having not much affect on system throughput. Numerical results show the effectiveness of the proposed schemes.

IEEE 802.11ac is a next generation wireless local area network (WLAN) standard employing multi-user multiple-input multiple-output (MU-MIMO) scheme for the first time. Since precoding and user selection schemes influence the performance of MU-MIMO transmission, accurate channel state information (CSI) is required to select users and obtain precoding matrix. In this paper, we evaluate the performance of linear precoding schemes such as channel inversion (CI) and block diagonalization (BD) with limited CSI by using compressed feedback. We also describe the greedy user selection metrics and linear precoding schemes of low complexity. Our results compare the achievable sum-rate between the user selection metrics and provide the packet error rate (PER) performance under the realistic channel models.

In a massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station (BS) to achieve high performance gain. The user equipment (UE) needs to estimate CSI and then feeds it back to the BS in the frequency division duplexing (FDD) mode. Effective compression of CSI will significantly reduce the cost of channel feedback, and many deep learning (DL) based channel compression schemes have been proposed to achieve this goal. In this paper, rather than viewing CSI as images as in most existing works, we propose a new perspective of viewing CSI as an information sequence and analogize CSI feedback to a machine translation task. Further, we propose a novel sequence to sequence (Seq2Seq) model for CSI feedback composed of only recurrent neural networks and a small-scale fully connected layer, avoiding the convolution and pooling structure commonly used in current DL-based works. The advantage of this scheme is that it fully integrates the physical characteristics of the MIMO channel in the spatial domain into the structure of the neural network and avoids the loss of some prominent unsmooth or discontinuous features caused by the inappropriate convolution and pooling operation. Simulation results show that the proposed Seq2Seq model outperforms other DL-based CSI compression techniques under various communication scenarios.