Mode Switching for the Multi-Antenna Broadcast Channel Based on Delay and Channel Quantization

In conventional cellular network systems, the cell edge users are vulnerable to interference from neighboring cells. In order to deal with this problem, coordinate multipoint transmission/reception(CoMP) has been proposed in LTE-A system. Yet, in recent years, multiuser multiple- input-multiple output (MU-MIMO) has been well developed. The MU-MIMO technique allows the simultaneous transmission of multiple user signals in the same time at the same frequency band, and hence enhances the system performance effectively. Thus, LTE-A tries to integrate MU-MIMO and CoMP hoping for better peak data rate and higher spectrum efficiency. This thesis aims to investigate intra-site MU CoMP joint processing (JP) techniques. We propose a new antenna-selection-based JP method that can better use the system resource and reduce the feedback overhead. Thanks to the antenna selection technique, we can translate a multipoint CoMP problem to a virtual single-point CoMP problem such that the existing single-point MU-MIMO technique can be directly applied. Numerical results show that the proposed method outperforms the conventional approach and at the same time requires less feedback overhead.

Recently, the multiuser multiple input multiple output (MU-MIMO) system has attracted much attention as a technology that enhances the total system capacity by generating a virtual MIMO channel between a base station and multiple terminal stations. Although extensive evaluations are needed because the number of system parameters in MU-MIMO is much larger compared to the single user (SU)-MIMO, the performance of MU-MIMO in actual environments is still not well understood. This paper describes the features and effectiveness of a 16times16 multi-user (MU)- MIMO testbed in an actual indoor environment. Moreover, we propose a simple adaptive modulation scheme for MU-MIMO-OFDM transmission that employs a bit interleaver for the frequency and space domains. Finally, we evaluate the frequency efficiency by obtaining the bit error rate of this testbed in an actual indoor environment. We clarify that 16times4times4-user MU-MIMO transmission using the proposed modulation scheme achieves the frequency utilization of 870 Mbps and 1 Gbps (SNR: 31 and 36 dB) with 20 MHz bandwidth, respectively.

5G massive MIMO deployments up to 64T64R (64 Transmit and 64 Receive) antenna arrays are gaining traction, in part due to the smaller size of such arrays suitable for mid-band spectrum (2-5 GHz). A scheduling algorithm that can aggressively pair many UEs (User Equipment) on the same time-frequency resources is essential to fully derive the benefits of such an array. In this paper, an uplink scheduler design is proposed based on RME (Recursive Maximal Expansion) that ensures contiguous resource allocation and power constraint for each UE, while maximizing MU (Multi-User) MIMO (Multiple-Input Multiple-Output) gains by selecting and pairing UE groups based on the sum-PF (Proportional Fair) metric. We show, using realistic system level simulations, that up to 100% average cell-throughput gains can be achieved by MU-MIMO over SU (Single-User) MIMO in dense urban micro cell deployment scenarios presenting an attractive proposition for massive MIMO array deployments. It is observed that power control parameters play a critical role and should be optimized considering scheduler behavior, especially for cell-edge throughput improvement.

In this paper, we investigate the per-tier outage probability of multiuser multiple-input multiple-output (MU-MIMO) transmissions in heterogeneous networks (HetNets) with joint interference constraint. In the tier of cellular cell, user equipment (UE) is required to report measured channel information and the base station (BS) adopts ZF-based precoding MU-MIMO transmission to achieve multiuser diversity gain. With the constraint of cross-tier interference and unpredicted inter-beam interference, we derive the closed-form expression of outage probability of downlink MU-MIMO transmissions. Considering the capacity of nodes in the tier of ad hoc networks, a max-SINR scheduler and codebook-based MU-MIMO transmission are employed. The scheduler selects the best receiving nodes for each beam in predefined codebook according to measured signal to interference plus noise ratio (SINR), and the transmitting node performs data transmissions using orthogonal beams. In the presence of inter-node interference, inter-beam interference, and cross-tier interference, we obtain the closed-form expression of outage probability of MU-MIMO transmissions when downlink or uplink transmissions occur in cellular cell. Additionally, in case that the outage probability in ad hoc networks should satisfy quality of service (QoS) requirement, a restricted area in cellular cell in which the outage probability in ad hoc networks is not greater than a required threshold is explored. Numerical results show that the unpredictable inter-beam interference in cellular cell degrades the outage probability slightly. The restricted area increases with the outage probability threshold.

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 proposes a robust transceiver design against the effect of channel state information (CSI) estimation error to optimize precoded uplink (UL) multi-user multiple-input multiple-output (MU-MIMO) transmission in limited feedback system under the consideration of the least-square technique on CSI estimation. To improve this limited feedback precoding, the constrained minimum variance (MV) approach with quadratic form to realize the computationally-efficient optimization problem, advantageously invoking the characteristics of the CSI estimation error, is proposed to suppress the effect of CSI estimation error, multiple user interference and noise. According to the Lagrange multiplier method on this MV approach, the deterministic function to resist uncertain CSI can be obtained to optimize design of the precoder and adaptive matrices jointly. With these optimum adaptive and precoder matrices, an optimum robust weighting matrix can be obtained to facilitate the user-wise detection in precoded UL MU-MIMO system. Performance analysis shows that the proposed robust weighting matrix is an unbiased design and it also can regularize the diagonal loading factor technique, and the detection performance of the proposed robust transceiver design can be predicted simplistically by applying our derived signal-to-interference-plus-noise ratio formulation. Computer simulations are conducted to confirm the efficacy of the proposed design in both perfect and imperfect CSI estimation.

Multiple input multiple output (MIMO) processing techniques are of interest for the next generation wireless communication systems due to their potential ability to dramatically improve the system capacity. There have been existed several multi-user MIMO (MU-MIMO) transmit schemes such as zero-forcing dirty paper coding (ZF-DPC), and zero-forcing beamforming (ZFBF). In this paper, we analysis and compare the dynamic transmission schemes based on TDMA and SDMA under perfect and imperfect channel state information (CSI). The system throughput (sum data rate), the optimal user number that can be supported are evaluated under perfect CSI by computer simulations. The effect of imperfect CSI on the throughput of TDMA and SDMA is also investigated. The results show that under perfect channel state information at both transmitter and receiver, the SDMA schemes can achieve a larger throughput than the TDMA scheme because SDMA can fully explore the multiuser diversity by transmitting to the optimal set of users. In contrast, the results show that the system performance of SDMA scheme is sensitive to the channel error.

In this paper, we propose a novel unitary downlink precoding design scheme for multiuser (MU) spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems. With the perfect channel state information (p-CSI) available at the transmitter and the linear decoder at the receiver, we construct the cost function based on the minimum average probability of vector symbol error (APVSE) and give the design method of the precoding matrices. Our proposed precoding matrices can completely eliminate co-channel interference (CCI) for each user at the transmitter, and each terminal user will eventually observe an interference-free single-user (SU) channel, thus simplify the decoding of each terminal user. Finally, we derive the upper bound APVSEs of several schemes for performance comparison and the latter simulation results have shown that our proposed downlink precoding for MU SM MIMO system obtains the almost same performance as the SU precoding system.

Multiple input multiple output (MIMO) systems have high potential to achieve maximum channel capacity in wireless communication systems. Multi-user (MU) MIMO network with transmit antennas $$T_x$$ at base station can schedule as many single antenna users in one time slot. This necessitates incorporation of a user scheduling strategy. Maximizing sumrate performance without under utilization of channel resources is a primary objective of such scheduling algorithms. Another key design factor for such techniques is to optimize fairness i.e. allowing equal opportunity to all communicating entities. In this paper, we present a simple approach to solve the scheduling problem in MU-MIMO systems while addressing the conflicting objectives of sumrate and fairness. Two variants of the proposed method are also discussed that provide options for maximizing the cumulative sumrate and improving the fairness. Simulations validate the proposed algorithm as a better choice over other conventional methods as it achieves optimal performance with relatively less computational complexity and without compromising fairness. Fairness results are verified by average sumrate method and Jain’s Fariness Index.

Wireless communication paradigm has evolved from single-user single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems to multi-user (MU) MIMO counterparts, which are shown greatly improving the rate performance by transmitting to multiple users simultaneously. The sum-capacity and the capacity region of a single-cell MU MIMO downlink system or the so-called MIMO broadcast channel (MIMO-BC) can be attained by the nonlinear “Dirty Paper Coding (DPC)” scheme [1] [2] [3]. However, DPC requires a high implementation complexity due to the non-linear successive encoding/decoding at the transmitter/receiver, and is thus not suitable for real-time applications. Other studies have proposed to use alternative linear precoding schemes for the MIMO-BC, e.g., the block-diagonalization scheme [4], to reduce the complexity. More information on the key developments of single-cell MIMO communication can be found in, for example, [5] [6] [7].

Preceding with block diagonalization is an attractive approach for approaching sum capacity in multiuser MIMO (multiple input multiple output) broadcast channels. This method though requires either global channel state information at every receiver or an additional training phase, which requires additional system planning. This paper proposes a lattice based multi-user precoder that uses block diagonalization combined with pre-equalization and perturbation for the multiuser MIMO broadcast channel. Achievable rates are computed and used to show that the proposed technique approaches the capacity with block diagonalization and water-filling but does not require the additional channel state information at the receiver. Monte Carlo simulations under the case of equal power allocation show that the proposed method provides better diversity and BER (bit error rate) performance than block diagonalization with a zero-forcing receiver.

The design of multiple input multiple output (MIMO) schemes capable of achieving both high spectral and energy efficiency constitutes a challenge for next-generation wireless networks. MIMO schemes based on generalized spatial modulations (GSM) have been widely considered as a powerful technique to achieve that purpose. In this paper, a multi-user (MU) GSM MIMO system is proposed, which relies on the transmission of precoded symbols from a base station to multiple receivers. The precoder’s design is focused on the removal of the interference between users and allows the application of single-user GSM detection at the receivers, which is accomplished using a low-complexity iterative algorithm. Link level and system level simulations of a cloud radio access network (C-RAN) comprising several radio remote units (RRUs) were run in order to evaluate the performance of the proposed solution. Simulation results show that the proposed GSM MU-MIMO approach can exploit efficiently a large number of antennas deployed at the transmitter. Moreover, it can also provide large gains when compared to conventional MU-MIMO schemes with identical spectral efficiencies. In fact, regarding the simulated C-RAN scenario with perfect channel estimation, system level results showed potential gains of up to 155% and 139% in throughput and coverage, respectively, compared to traditional cellular networks. The introduction of imperfect channel estimation reduces the throughput gain to 125%.

This paper proposes applying random (opportunistic) beamforming to base station (BS) cooperative multiuser multiple-input multiple-output (MIMO) transmission. This proposal comprises two parts. First, we propose a block-diagonalized random unitary beamforming matrix. The proposed beamforming matrix achieves better throughput distribution compared to the purely random unitary beamforming matrix when the average path loss based on distance-dependent loss and shadowing loss is largely different among transmitter antennas, which is true in BS cooperative MIMO. Second, we propose an online update algorithm for a random beamforming matrix to improve the throughput compared to the purely random and channel-independent beamforming matrix generation, especially when the number of users is low. Different from conventional approaches, the proposed online update algorithm does not increase the overhead of the reference signal transmission and control delay. Simulation results show the effectiveness of the proposed method using a block-diagonalized random unitary beamforming matrix with online updates in a BS cooperative multiuser MIMO scenario.

Multiple-Input Multiple-Output (MIMO) technology has become a critical component in Beyond 5G (B5G) wireless communication systems, enhancing spectral efficiency and system capacity. However, accurate channel estimation remains a significant challenge, especially in high-mobility environments. In this study, conventional and deep learning-based channel estimation approaches were analyzed for 2×2, 4×4, and 8×8 Multi User MIMO, (MU-MIMO) systems. Least Squares (LS) method is compared with the proposed deep learning (DL)-based model, and the analyses are conducted under both perfect channel state information (P-CSI) and imperfect CSI (I-CSI) scenarios. The results demonstrate that the proposed DL-based approach achieves performance close to the P-CSI case and significantly improves estimation accuracy compared to traditional methods.

In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)–to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step.