R bits user selection switch feedback for zero forcing MU-MIMO based on low rate codebook

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

This paper proposes an adaptive multi-mode transmission strategy to improve the spectral efficiency achieved in the multiple-input multiple-output (MIMO) broadcast channel with delayed and quantized channel state information. The adaptive strategy adjusts the number of active users, denoted as the transmission mode, to balance transmit array gain, spatial division multiplexing gain, and residual inter-user interference. Accurate closed-form approximations are derived for the achievable rates for different modes, which help identify the active mode that maximizes the average sum throughput for given feedback delay and channel quantization error. The proposed transmission strategy can be easily combined with round-robin scheduling to serve a large number of users. As instantaneous channel information is not exploited, the proposed algorithm cannot provide multiuser diversity gain, but it is still able to provide throughput gain over single-user MIMO at moderate signal-to-noise ratio. In addition, it has a light feedback overhead and only requires feedback of instantaneous channel state information from a small number of users. In the system with a feedback load constraint, it is shown that the proposed algorithm provides performance close to that achieved by opportunistic scheduling with instantaneous feedback from a large number of users.

This paper studies the fairness, power allocation, and channel state information (CSI) quantization in multiuser systems utilizing multiple feedback bits per user. For a given set of schedulers, we obtain the system throughput with different power allocation strategies and any combination of different fading distributions. Moreover, the system performance under user different outage probability constraints is investigated. Assuming homogenous users, as a special case, the throughput is determined for fixed and random request networks. Considering nonidentical fading channels between the transmitter and receivers, the throughput is found under the K-significant average feedback bit allocation technique, and two suboptimal fairness schemes are investigated which satisfy different quality-of-service requirements. The results show that using optimal power allocation, the first quantization region (QR) of each user is the only QR for which no power may be allocated. The system outage probability vanishes as the number of users goes to infinity. Users hard outage probability constraints can be satisfied at the cost of one more QR in the channel quantizer. Finally, the proposed fairness schemes are more flexible than the standard proportional fair (PF) scheduling in dealing with the throughput-fairness tradeoff. However, their superiority over PF scheduling depends on the fairness constraint.

Downlink transmission techniques for multiuser (MU) multiple‐input multiple‐output (MIMO) systems have been comprehensively studied during the last two decades. The well‐known low complexity linear precoding schemes are currently deployed in long‐term evolution (LTE) networks. However, these schemes exhibit serious shortcomings in scenarios when users’ channels are strongly correlated. The nonlinear precoding schemes show better performance, but their complexity is prohibitively high for a real‐time implementation. Two‐stage precoding schemes, proposed in the standardization process for 5G new radio (5G NR), combine these two approaches and present a reasonable trade‐off between computational complexity and performance degradation. Before applying the precoding procedure, users should be properly allocated into beamforming subgroups. Yet, the optimal solution for user selection problem requires an exhaustive search which is infeasible in practical scenarios. Suboptimal user grouping approaches have been mostly focused on capacity maximization through greedy user selection. Recently, overlapping user grouping concept was introduced. It ensures that each user is scheduled in at least one beamforming subgroup. To the best of our knowledge, the existing two‐stage precoding schemes proposed in literature have not considered overlapping user grouping strategy that solves user selection, ordering, and coverage problem simultaneously. In this paper, we present a two‐stage precoding technique for MU‐MIMO based on the overlapping user grouping approach and assess its computational complexity and performance in IoT‐oriented 5G environment. The proposed solution deploys two‐stage precoding in which linear zero forcing (ZF) precoding suppresses interference between the beamforming subgroups and nonlinear Tomlinson‐Harashima precoding (THP) mitigates interuser interference within subgroups. The overlapping user grouping approach enables additional capacity improvement, while ZF‐THP precoding attains balance between the capacity gains and suffered computational complexity. The proposed algorithm achieves up to 45% higher MU‐MIMO system capacity with lower complexity order in comparison with two‐stage precoding schemes based on legacy user grouping strategies.

Wireless network is one of the most important things in the development of telecommunication. However, the existing wireless technology has not been able to efficiently create a very high data rate because it was very sensitive to fading. Therefore, Orthogonal Frequency Division Multiplexing (OFDM) technology combined with MIMO (Multiple Input Multiple Output) transceiver system was used to gain more diversity and bandwidth efficiency. The propagation performed on the OFDM MIMO system was multipath propagation. To reduce Intersymbol Interference (ISI) was used Zero Forcing (ZF) equalizer which works by combining channel response and equalizer response itself to eliminate ISI. This study aims to determine the effect of Zero Forcing Equalizer on OFDM MIMO system using rayleigh fading channel and compared the result with OFDM MIMO STBC system. The research method used was simulation using MatLab R2015a. The simulation results showed that the performance of OFDM MIMO ZF system was worse than OFDM MIMO STBC system i.e BER vs EbNo simulation, eye diagram simulation, and constellation diagram simulation. The OFDM MIMO ZF system was required an Eb / No value more than 25 dB to achieve BER 10-4 while the OFDM MIMO STBC system only required an Eb / No value of 10.5 dB to achieve BER 10-4. The eye pattern generated by the OFDM MIMO ZF system was more closed and the dispersion of constellation signals away from the ideal point while OFDM MIMO STBC system displayed a more open eye pattern and the dispersion of its constellation signal closer to the ideal point. It indicated more ISI occurs in the OFDM MIMO ZF system than that in OFDM MIMO STBC system.

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 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, we focus on downlink zero forcing (ZF) beamforming design scheme for multiuser (MU) spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems. Based on the minimum average probability of symbol error (PSE), we give the optimal design method of the beamforming weight matrices. Furthermore, we derive the upper bound PSEs to evaluate the impact of channel feedback errors on the system performance. Comparison of the upper bounds of the average PSE for several schemes shows that the downlink ZF beamforming for multiuser spatial multiplexing MIMO schemes provide almost the same performance as the SU beamforming scheme.

In this paper, a method based on max SINR criterion is proposed to mitigate the interuser interference for downlink multiuser spatial multiplexing MIMO systems. Unlike Zero Forcing (ZF) scheme in which the SNR is decreased when the interference is eliminated completely, max SINR method makes a compromise between noise and interuser interference. When the number of substreams is larger than the difference between the number of base station antennas and the sum of interference mobile station antennas, the ZF is infeasible. An existing Coordinated tx-rx Block Diagonalization(COOR BD) method uses a preprocessing at receiver to cancel the interuser interference. However it cannot obtain more receive diversity gain due to the preprocessing. Analysis and simulation show that the max SINR scheme has better performance than the ZF method. Moreover when the ZF is infeasible, the max SINR scheme can obtain more receive diversity gain than COOR BD in two users case.

ABSTRACTIn massive MIMO systems, inter-user interference has effect on the transmitted signals, so linear precoding techniques are employed for multiuser transmission channels to remove this inter-user interference. It is very complicated to design a suitable linear precoding technique having low computational complexity, which will give an excellent Bit Error Rate (BER) performance. In this paper, we analyse BER under imperfect Channel-State-Information (CSI) using Zero-Forcing (ZF) linear precoding scheme in the downlink, massive MIMO in Time-Division-Duplex (TDD) mode. We derive the closed-form expressions for BER that are obtained from the new expressions derived for Signal-Interference-Noise Ratio (SINR), and then analysis with different parameters using numerical and Monte Carlo simulated results is carried out in MATLAB under imperfect CSI. It was found that our theoretical analysis agrees well with the simulated results. In our analysis, BER was observed in most cases to be good when the number of Base Station (BS) antennas is large enough to accommodate the served users, but when the number of users grows up, the BER performance degrades depending on how many BS antennas are capable of handling the additional users. Also, increasing the downlink transmit power was found to improve the performance, but it is not an energy-efficient way for massive MIMO.

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.

Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. Compared with the system using perfect channel state information (CSI), we analyze the achievable rate loss due to CSI mismatch arising from both channel estimation and CSI quantization feedback. An upper bound to the rate loss is firstly derived to characterize the effect of imperfect CSI. Given the rate loss bound, we then present a limited feedback strategy for both two-hop channels in the relay system to guarantee a bounded rate loss for growing SNRs. This newly presented strategy reveals the relationship between CSI quantization level and other system parameters, including the transmit power and the pilots for channel estimation, on the entire two-hop system. Finally, computer simulations are carried out for verifying the correctness of our derived results.

In Multi User (MU) Massive MIMO Base Station is provided with large number of antennas to serve number of single antenna user terminals. It offers several benefits over conventional MIMO and takes the advantage of multipath propagation by sending and receiving multiple data signals in same time-frequency resource which increases the throughput, coverage, and reliability. Massive MIMO, averages out the small scale fading and thermal noise. This averaging is done as the number of BS antennas increases without limit (M ≫ K). One of the limiting factor for the performance of Massive MIMO system is pilot contamination. Reuse of the orthogonal pilot sequences in adjacent cells cause pilot contamination. Pilot contamination causes poor channel estimation at the BS and degrades the performance of the system. The proposed scheme improves the performance of downlink by optimized allocation of pilot sequences and using fractional frequency reuse factor (1/N). All the available pilot signals are equally divided among the cells as per the cluster size (N = 1, 3, 7). In the proposed scheme orthogonal pilot signal is transmitted by each user terminal to get the Channel State Information (CSI) to estimate the channel. SIR and terminal capacity for the downlink are calculated for the given number of user terminals using linear precoding (Matched Filter and Zero Forcing) schemes which are the function of the estimated channel and the pilot transmission power (ρ p ). It is observed that there is significant improvement in Pilot Signal to Interference Plus Noise (PSINR), SIR and terminal capacity by reducing fractional frequency reuse factor i.e. (N = 1, 3, 7).

On a multi-antenna downlink channel, separation of multiple users by transmit beamforming enables simultaneous transmission from the base station to the users, resulting in high sum throughput. This paper proposes and analyzes a practical algorithm for joint scheduling and orthogonal beamforming, which is enabled by feedback of quantized channel state information (CSI). In this approach, each user quantizes CSI using a codebook comprised of multiple orthonormal vector sets and sends back quantized CSI. Using feedback CSI, the base station jointly selects a set of orthogonal beamforming vectors and schedules a subset of feedback users for downlink transmission such that the throughput is maximized. For moderate to large numbers of users, the proposed algorithm achieves higher sum capacities than the conventional ones.