A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs

Coordinated multi-point (CoMP) schemes have been widely studied in the recent years to tackle the inter-cell interference. In practice, latency and throughput constraints on the backhaul allow the organization of only small clusters of base stations (BSs) where joint processing (JP) can be implemented. In this work we focus on downlink CoMP-JP with multiple antenna user equipments (UEs) and propose a novel dynamic clustering algorithm. The additional degrees of freedom at the UE can be used to suppress the residual interference by using an interference rejection combiner (IRC) and allow a multistream transmission. In our proposal we first define a set of candidate clusters depending on long-term channel conditions. Then, in each time block, we develop a resource allocation scheme by jointly optimizing transmitter and receiver where: a) within each candidate cluster a weighted sum rate is estimated and then b) a set of clusters is scheduled in order to maximize the system weighted sum rate. Numerical results show that much higher rates are achieved when UEs are equipped with multiple antennas. Moreover, as this performance improvement is mainly due to the IRC, the gain achieved by the proposed approach with respect to the non-cooperative scheme decreases by increasing the number of UE antennas.Comment: 27 pages, 8 figure

Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang et al where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace based on the transmitter's knowledge of LR's channel in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and non-reciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. {For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals,} we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and hence the data detection performances at LR and UR.Comment: 1

Two-way training for discriminatory channel estimation in wireless MIMO systems

This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.This work was supported in part by the National Science Council, Taiwan, by Grant NSC 100-2628-E-007-025-MY3 and Grant NSC 101-2218-E-011-043, and in part by the Australian Research Council's Discovery Projects Funding Scheme (Project no.DP110102548)

Resource Allocation in Collocated Massive MIMO for 5G and Beyond

Massive multiuser multiple-input multiple-output (MIMO) systems have been recently introduced as a promising technology for the next generation of wireless networks. It has been proven that linear precoders/detectors such as maximum ratio transmitting/maximum ratio combining (MRT/MRC), zero forcing (ZF), and linear minimum mean square error (LMMSE) on the downlink (DL)/uplink (UL) transmission can provide near optimal performance in such systems. Acquiring channel state information (CSI) at the transmitter as well as the receiver is one of the challenges in multiuser massive MIMO that can affect the network performance. Any data transmission in multiuser massive MIMO systems starts with the user transmitting UL pilots. The base station (BS) then uses the MMSE estimation method to accurately estimate the CSI from the pilot sequences. Since the UL and DL channels are reciprocal in time division duplex (TDD) mode, the BS employs the obtained CSI to precode the data symbols prior to DL transmission. The users also need the CSI knowledge to accurately decode the DL signals. Beamforming training (BT) scheme is one of the methods that is proposed in the literature to provide the CSI knowledge for the users. In this scheme, the BS precodes and transmits a pilot sequence to the users such that each user can estimate its effective channel coefficients. Developing an optimal resource distribution method that enhances the system performance is another challenging issue in multiuser massive MIMO. As mentioned earlier, CSI acquisition is one of the requirements of multiuser massive MIMO, and UL pilot transmission is the common method to achieve that. Conventionally, equal powers have been considered for the pilot transmission phase and data transmission phase. However, it can be shown that the performance of the system under this method of power distribution is not optimal. Therefore, to further improve the performance of multiuser massive MIMO technology, especially in cases where the antenna elements are not well separated and the propagational dispersion is low, optimal resource allocation is required. Hence, the main objective of this M.A.Sc. thesis is to develop an optimal resource allocation among pilot and data symbols to maximize the spectral efficiency, assuming different receivers such as MRC, ZF, and LMMSE are employed at the BS. Since the calculation of spectral efficiency using the lower bound on the achievable rate is computationally very intensive, we first obtain closed-form expressions for the achievable UL rate of users, assuming the angular domain in the physical channel model is divided into a finite number of separate directions. An approximate expression for spectral efficiency is then developed using the aforementioned closed-form rates. Finally, we propose a resource allocation scheme in which the pilot power, data power, and training duration are optimally chosen in order to maximize the spectral efficiency in a given total power budget. Extensive simulations are conducted in MATLAB and the results are presented that illustrate the notable improvement in the achievable spectral efficiency through the proposed power allocation scheme. Moreover, the results show that the performance of the proposed method is much superior when the number of channel directions or the number of antennas at BS increases. Furthermore, while the advantage of the proposed method is more notable in the case of ZF and LMMSE receivers, it still outperforms the equal power allocation method for the MRC receiver in terms of spectral efficiency

Pilot contamination reduction in massive MIMO systems

Massive MIMO systems have been pointed out as one of the possible strategies to enhance system performance and reach the high data rates modern wireless communications demand. It represents a breakthrough in modern investigations given the new degrees of freedom and the extra dimensional space it provides. However, given the lack of channel knowledge, estimation must be employed. After this process, some interference due to the fast variation of the channel, which implies users sharing training sequences, is left. This is commonly referred to as pilot contamination and heavily compromises the throughput, specially in the large scale antennas regime. In this thesis, we will first study in detail this dramatic effect for later introducing different proposals that attempt to reduce its impact. In particular, we will start with the use of two main filters as basic processing. Next, allocation schemes to properly distribute users are discussed. Then, we will suggest projection based methods that transform the estimates with the purpose of canceling the undesired portions and strengthen the user destined signals. At the end of this analysis, it is shown numerically that the approaches presented behave well in these scenarios and help mitigate the interference created. This allows a faster and more robust transmission of information to take place in environments where pilot contamination is present