Multi-User Diversity vs. Accurate Channel State Information in MIMO Downlink Channels

In a multiple transmit antenna, single antenna per receiver downlink channel with limited channel state feedback, we consider the following question: given a constraint on the total system-wide feedback load, is it preferable to get low-rate/coarse channel feedback from a large number of receivers or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. We show that there is a strong preference for obtaining high-quality feedback, and that obtaining near-perfect channel information from as many receivers as possible provides a significantly larger sum rate than collecting a few feedback bits from a large number of users.Comment: Submitted to IEEE Transactions on Communications, July 200

Resource allocation and feedback in wireless multiuser networks

This thesis focuses on the design of algorithms for resource allocation and feedback in wireless multiuser and heterogeneous networks. In particular, three key design challenges expected to have a major impact on future wireless networks are considered: cross-layer scheduling; structured quantization codebook design for MU-MIMO networks with limited feedback; and resource allocation to provide physical layer security. The first design challenge is cross-layer scheduling, where policies are proposed for two network architectures: user scheduling in single-cell multiuser networks aided by a relay; and base station (BS) scheduling in CoMP. These scheduling policies are then analyzed to guarantee satisfaction of three performance metrics: SEP; packet delay; and packet loss probability (PLP) due to buffer overflow. The concept of the τ-achievable PLP region is also introduced to explicitly describe the tradeoff in PLP between different users. The second design challenge is structured quantization codebook design in wireless networks with limited feedback, for both MU-MIMO and CoMP. In the MU-MIMO network, two codebook constructions are proposed, which are based on structured transformations of a base codebook. In the CoMP network, a low-complexity construction is proposed to solve the problem of variable codebook dimensions due to changes in the number of coordinated BSs. The proposed construction is shown to have comparable performance with the standard approach based on a random search, while only requiring linear instead of exponential complexity. The final design challenge is resource allocation for physical layer security in MU-MIMO. To guarantee physical layer security, the achievable secrecy sum-rate is explicitly derived for the regularized channel inversion (RCI) precoder. To improve performance, power allocation and precoder design are jointly optimized using a new algorithm based on convex optimization techniques

Resource allocation and feedback in wireless multiuser networks

This thesis focuses on the design of algorithms for resource allocation and feedback in wireless multiuser and heterogeneous networks. In particular, three key design challenges expected to have a major impact on future wireless networks are considered: cross-layer scheduling; structured quantization codebook design for MU-MIMO networks with limited feedback; and resource allocation to provide physical layer security. The first design challenge is cross-layer scheduling, where policies are proposed for two network architectures: user scheduling in single-cell multiuser networks aided by a relay; and base station (BS) scheduling in CoMP. These scheduling policies are then analyzed to guarantee satisfaction of three performance metrics: SEP; packet delay; and packet loss probability (PLP) due to buffer overflow. The concept of the τ-achievable PLP region is also introduced to explicitly describe the tradeoff in PLP between different users. The second design challenge is structured quantization codebook design in wireless networks with limited feedback, for both MU-MIMO and CoMP. In the MU-MIMO network, two codebook constructions are proposed, which are based on structured transformations of a base codebook. In the CoMP network, a low-complexity construction is proposed to solve the problem of variable codebook dimensions due to changes in the number of coordinated BSs. The proposed construction is shown to have comparable performance with the standard approach based on a random search, while only requiring linear instead of exponential complexity. The final design challenge is resource allocation for physical layer security in MU-MIMO. To guarantee physical layer security, the achievable secrecy sum-rate is explicitly derived for the regularized channel inversion (RCI) precoder. To improve performance, power allocation and precoder design are jointly optimized using a new algorithm based on convex optimization techniques

Massive MIMO 시스템을 위한 채널 추정 및 피드백 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 이정우.To meet the demand of high throughput in next generation wireless systems, various directions for physical layer evolution are being explored. Massive multiple-input multiple-output (MIMO) systems, characterized by a large number of antennas at the transmitter, are expected to become a key enabler for spectral efficiency improvement. In massive MIMO systems, thanks to the orthogonality between different users' channels, high spectral and energy efficiency can be achieved through simple signal processing techniques. However, to get such advantages, accurate channel state information (CSI) needs to be available, and acquiring CSI in massive MIMO systems is challenging due to the increased channel dimension. In frequency division duplexing (FDD) systems, where CSI at the transmitter is achieved through downlink training and uplink feedback, the overhead for the training and feedback increases proportionally to the number of antennas, and the resource for data transmission becomes scarce in massive MIMO systems. In time division duplexing (TDD) systems, where the channel reciprocity holds and the downlink CSI can be obtained through uplink training, pilot contamination due to correlated pilots becomes a performance bottleneck when the number of antennas increases. In this dissertation, I propose efficient CSI acquisition techniques for various massive MIMO systems. First, I develop a downlink training technique for FDD massive MIMO systems, which estimates the downlink channel with small overhead. To this end, compressed sensing tools are utilized, and the training overhead can be highly reduced by exploiting the previous channel information. Next, a limited feedback scheme is developed for FDD massive MIMO systems. The proposed scheme reduces the feedback overhead using a dimension reduction technique that exploits spatial and temporal correlation of the channel. Lastly, I analyze the effect of pilot contamination, which has been regarded as a performance bottleneck in multi-cell massive MIMO systems, and propose two uplink training strategies. An iterative pilot design scheme is developed for small networks, and a scalable training framework is also proposed for networks with many cells.1 Introduction 1 1.1 Massive MIMO 1 1.2 CSI Acquisition in Massive MIMO Systems 3 1.3 Contributions and Organization 6 1.4 Notations 7 2 Compressed Sensing-Aided Downlink Training 9 2.1 Introduction 10 2.2 System Model 13 2.2.1 Channel Model 13 2.2.2 Downlink Channel Estimation 16 2.3 CS-Aided Channel Training 19 2.3.1 Training Sequence Design 20 2.3.2 Channel Estimation 21 2.3.3 Estimation Error 23 2.4 Discussions 26 2.4.1 Design of Measurement Matrix 26 2.4.2 Extension to MIMO Systems 27 2.4.3 Comparison to CS with Partial Support Information 28 2.5 Simulation Results 29 2.6 Conclusion 37 3 Projection-Based Differential Feedback 39 3.1 Introduction 40 3.2 System Model 44 3.2.1 Multi-User Beamforming with Limited Feedback 45 3.2.2 Massive MIMO Channel 47 3.3 Projection-Based Differential Feedback 48 3.3.1 Projection-Based Differential Feedback Framework 48 3.3.2 Projection for PBDF Framework 51 3.3.3 Efficient Algorithm 57 3.4 Discussions 58 3.4.1 Projection with Imperfect CSIR 58 3.4.2 Acquisition of Channel Statistics 61 3.5 Simulation Results 62 3.6 Conclusion 69 4 Mitigating Pilot Contamination via Pilot Design 71 4.1 Introduction 72 4.2 System Model 73 4.2.1 Multi-cell Massive MIMO Systems 74 4.2.2 Uplink Channel Training 75 4.2.3 Data Transmission 77 4.3 Iterative Pilot Design Algorithm 78 4.3.1 Algorithm 79 4.3.2 Proof of Convergence 81 4.4 Generalized Pilot Reuse 81 4.4.1 Concept of Pilot Reuse Schemes 81 4.4.2 Pilot Design based on Grassmannian Subspace Packing 82 4.5 Simulation Results 85 4.5.1 Iterative Pilot Design 85 4.5.2 Generalized Pilot Reuse 87 4.6 Conclusion 89 5 Conclusion 91 5.1 Summary 91 5.2 Future Directions 93 Bibliography 96 Abstract (In Korean) 109Docto