A new zero-forcing block diagonalization (ZF-BD) scheme that enables both a more simplified ZF-BD and further increase in sum rate of MU-MIMO channels is proposed in this paper. The proposed scheme provides the improvement in BER performance for equivalent SU-MIMO channels. The proposed scheme consists of two components. First, a permuted channel matrix (PCM), which is given by moving the submatrix related to a target user to the bottom of a downlink MIMO channel matrix, is newly defined to obtain a precoding matrix for ZF-BD. Executing QR decomposition alone for a given PCM provides null space for the target user. Second, a partial MSQRD (PMSQRD) algorithm, which adopts MSQRD only for a target user to provide improvement in bit rate and BER performance for the user, is proposed. Some numerical simulations are performed, and the results show improvement in sum rate performance of the total system. In addition, appropriate bit allocation improves the bit error rate (BER) performance in each equivalent SU-MIMO channel. A successive interference cancellation is applied to achieve further improvement in BER performance of user terminals.

In this paper, we propose a novel user selection scheme based on jointly combining channel gain (CG) and signal to interference plus noise ratio (SINR) to improve the sum-rate as well as to reduce the computation complexity of multi-user massive multi-input multi-output (MU-massive MIMO) downlink transmission through a block diagonalization (BD) precoding technique. By jointly considering CG and SINR based user sets, sum-rate performance improvement can be achieved by selecting higher gain users with better SINR conditions as well as by eliminating the users who cause low sum-rate in the system. Through this approach, the number of possible outcomes for the user selection scheme can be reduced by counting the common users for every pair of user combinations in the selection process since the common users of CG-based and SINR-based sets possess both higher channel gains and better SINR conditions. The common users set offers not only sum-rate performance improvements but also computation complexity reduction in the proposed scheme. It is shown by means of computer simulation experiments that the proposed scheme can increase the sum-rate with lower computation complexity for various numbers of users as compared to conventional schemes requiring the same or less computational complexity.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

The potential for using millimeter-wave (mmWave) frequencies in future 5G wireless cellular communication systems has motivated the study of large-scale antenna arrays to achieve highly directional beamforming. However, the conventional fully digital beamforming (DBF) methods which require one radio frequency (RF) chain per antenna element are not viable for large-scale antenna arrays due to the high cost and large power consumption of high frequency RF chain components. Hybrid precoding can significantly reduce the number of required RF chains and relieve the huge power consumption in mmWave massive multiple-input multiple-output (MIMO) systems, thus attracting much interests from academic and industry. In this paper, we consider the downlink communication of a massive multiuser MIMO (MU-MIMO) system in the mmWave channel, and propose a low complexity hybrid block diagonal geometric mean decomposition (BD-GMD) scheme. More specially, a joint transmit-receive (Tx-Rx) analog beamforming with large-scale arrays is proposed to improve channel gain, and then a low-dimensional BD-GMD approach is implemented at the equivalent baseband channel to mitigate the inter-user interference and equalize different data streams of each user. With the help of successive interference cancellation (SIC) at the receiver, we can decompose each user's MIMO channel into parallel sub-channels with identical higher SNRs/SINRs, thus equal-rate coding can be applied across the sub-channels of each user. Finally, simulation results verify that the proposed hybrid BD-GMD precoding scheme outperforms existing conventional fully digital and hybrid precoding schemes and is able to achieve much better BER performance.

Massive multiple input multiple output (MIMO) communication system offers high rate transmission and/or support of a large number of users by invoking the power of a large array antenna, but one of its problem is the heavy computational burden required for the design and signal processing. Assuming the utilization of a large array in the transmitter side and much fewer users than the maximum possible value, this paper first presents a subarray based design approach of MIMO system with a low computational load taking into account efficient subarray grouping for the realization of higher performance; a large transmit array is first divided into subarrays based on channel gain or channel correlation, then block diagonalization is applied to each of them, and finally a large array weight is reconstructed by maximal ratio combining (MRC). In addition, the extension of the proposed method to two-stage design is studied in order to support a larger number of users; in the process of reconstruction to a large array, subarrays are again divided into groups, and block diagonalization is applied to those subarray groups. Through computer simulations, it is shown that the both channel gain and correlation based grouping strategies are effective under certain conditions, and that the number of supported users can be increased by two-stage design if certain level of performance degradation is acceptable.

This paper proposes Fast Fourier Transform (FFT) based orthogonal beam selection method at the user terminals (UTs) to reduce the number of nulls for the other users except an intended user by the Block Diagonalization (BD) algorithm in multiuser MIMO (MU-MIMO) sytems. The BD algorithm has been proposed in order to realize MU-MIMO broadcast transmission with a realistic signal processing burden. The BD algorithm cancels inter-user interference by creating the weights so that the channel matrixes for the other users are set to be zero matrixes. However, when the number of transmit antennas is equals to the total number of received antennas, the transmission rate by the BD algorithm is decreased. The proposed method realizes the performance improvement compared to the conventional BD algorithm without the burden on the UTs. It is verified via bit error rate (BER) evaluation that the proposed method is effective compared to the conventional BD algorithm and antenna selection method. Moreover, the effectiveness of proposed method is verified by the performance evaluation considering medium access control (MAC) layer in a comparison with the conventional BD algorithm which needs the channel state information (CSI) feedback. Because the proposed method can be easily applied to beamforming without the CSI feedback (implicit beamforming), it is shown that the propose method is effective from a point of view on the transmission efficiency in MU-MIMO system.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.

Multi-user MIMO (MU-MIMO) improves the system channel capacity by employing the transmission between a base station and multiple user terminals (UTs). Block Diagonalization (BD) has been proposed in order to realize MU-MIMO broadcast transmission. The BD algorithm cancels inter-user interference by creating the weights so that the channel matrixes for the other users are set to be zero matrixes. However, when the number of transmit antennas is equals to the total number of received antennas, the transmission rate by the BD algorithm is decreased. This paper proposes a new antenna selection method at the UTs to reduce the number of nulls for the other users except an intended user by the BD algorithm. It is verified via bit error rate (BER) evaluation that the proposed method is effective compared to the conventional BD algorithm, especially, when the number of users is increased with a low bit rate. Moreover, this paper evaluates the transmission rate based on IEEE802.11ac standard when considering BD algorithm with ideal user scheduling. Although the number of equivalent receive antenna is only one by the proposed method when the number of antennas at the the UT is two, it is shown that the transmission rate by the proposed method is higher than that by the conventional BD algorithm when the SNR is low even in the condition on user scheduling.

This paper proposes a novel method for adaptively controlling the admission of interference to users in our previously proposed layered partially non-orthogonal block diagonalization (BD) precoding method for downlink multiuser multiple-input multiple-output (MIMO) transmission that employs cooperation among multiple base stations (BSs). The proposed method is applicable when some of the instantaneous channel state information (CSI) feedback between the user equipment and the respective BSs is missing if the path loss between the user equipment and BS is higher than a predetermined threshold. The proposed method suppresses the loss in the transmitter diversity (beam forming) gain caused by the perfect nulling of inter-user interference in BD. By allowing the inter-user interference from a link that has a high average path loss, the overall throughput performance of simple BD is enhanced. We show that the combination of layered transmission that restricts the set of BSs used for the signal transmission and adaptive control of interference admission significantly increases the throughput of BS cooperative multiuser MIMO with partial CSI feedback.

In this paper, we propose a joint diversity algorithm for error-rate minimization in multi-user spatial multiplexing (SM) systems with block diagonalization (BD)-precoding. The proposed algorithm adapts or selects the user set, transmit antenna subset, and the number of streams by an exhaustive search over the available resources. The proposed algorithm makes use of the multi-user diversity (MUD) and the spatial diversity gains as well as the array gain through selecting the best set. Exhaustive search, however, imposes a heavy burden in terms of computational complexity which exponentially increases with the size of the total number of users, streams, and transmit antennas. For complexity reduction, we propose two suboptimal algorithms which reduce the search space by first selecting the best user or by both selecting the best user and fixing the number of streams. Simulation results show that the proposed algorithms improve error probability over the conventional algorithm due to their diversity improvement and the signal-to-noise ratio (SNR) gains over the conventional algorithm. We also show that the suboptimal algorithms significantly reduce the computational complexity over exhaustive search with low-SNR loss.

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.

In multiuser multiple-input multiple-output (MU-MIMO) wireless downlink systems, block diagonalization (BD) is a technique, where the transmit precoding matrix of each user is designed such that its subspace lies in the null space of all the other remaining users, so that multiuser interference (MUI) is completely canceled. In low signal to noise power ratio (SNR) or low signal to interference plus noise power ratio (SINR) environments, regularized BD, that lets some MUI remain and maximizes the sum rate capacity of the BD MIMO channel, was also proposed. One of the problems of both the approaches is high complexity of computation due to a lot of singular value decomposition (SVD) processes. In this paper we propose new BD techniques utilizing QR decomposition (QRD) which can be practically achieved by Gram-Schmidt orthogonalization (GSO) with lower complexity compared to the conventional method employing SVD. We can show that the performance of the proposed approaches is close to the conventional approaches, while the proposed approaches have much lower complexity.

In multiple-input multiple-output (MIMO) systems, the multiuser MIMO (MU-MIMO) systems have the potential to provide higher channel capacity owing to multiuser and spatial diversity. Block diagonalization (BD) is one of the techniques to realize MU-MIMO systems, where multiuser interference can be completely cancelled and therefore several users can be supported simultaneously. When the number of multiantenna users is larger than the number of simultaneously receiving users, it is necessary to select the users that maximize the system capacity. However, computation complexity becomes prohibitive, especially when the number of multiantenna users is large. Thus simplified user scheduling algorithms are necessary for reducing the complexity of computation. This paper proposes a simplified capacity-based user scheduling algorithm, based on analysis of the capacity-based user selection criterion. We find a new criterion that is simplified by using the properties of Gram-Schmidt orthogonalization (GSO). In simulation results, the proposed algorithm provides higher sum rate capacity than the conventional simplified norm-based algorithm; and when signal-to-noise power ratio (SNR) is high, it provides performance similar to that of the conventional simplified capacity-based algorithm, which still requires high complexity. Fairness of the users is also taken into account. With the proportionally fair (PF) criterion, the proposed algorithm provides better performance (sum rate capacity or fairness of the users) than the conventional algorithms. Simulation results also shows that the proposed algorithm has lower complexity of computation than the conventional algorithms.

For multiple-input multiple-output (MIMO) precoded transmission that has individual constraints on the maximum power of each transmit antenna or a subset of transmit antennas, the transmit power optimization problem is a non-linear convex optimization problem with a high level of computational complexity. In this paper, assuming the use of the interior point method (IPM) to solve this problem, we propose two efficient techniques that reduce the computational complexity of the IPM by appropriately setting its parameters. Based on computer simulation, the achieved reductions in the level of the computational complexity are evaluated using the proposed techniques for both the fairness and the sum-rate maximization criteria assuming i.i.d Rayleigh fading MIMO channels and block diagonalization zero-forcing as a multi-user MIMO (MU-MIMO) precoder.

Multiuser -- Multiple Input Multiple Output (MU-MIMO) techniques were proposed to increase spectrum efficiency; a key assumption was that the Mobile Terminals (MTs) were simple with only a few antennas. This paper focuses on the Block Diagonalization algorithm (BD) based on the equal power allocation strategy as a practical MU-MIMO technique. When there are many MTs inside the service area of the access point (AP), the AP must determine, at each time slot, the subset of the MTs to be spatially multiplexed. Since the transmission performance depends on the subsets of MTs, the user selection method needs to use the Channel State Information (CSI) obtained in the physical layer to maximize the Achievable Transmission Rate (ATR). In this paper, we clarify the relationship between ATR with SU-MIMO and that with MU-MIMO in a high eigenvalue channel. Based on the derived relationship, we propose a new measure for user selection. The new measure, the eigenvalue decay factor, represents the degradation of the eigenvalues in null space compared to those in SU-MIMO; it is obtained from the signal space vectors of the MTs. A user selection method based on the proposed measure identifies the combination of MTs that yields the highest ATR; our approach also reduces the computational load of user selection. We evaluate the effectiveness of user selection with the new measure using numerical formulations and computer simulations.