Haige Xiang

Polite Water-Filling for Weighted Sum-Rate Maximization in MIMO B-MAC Networks Under Multiple Linear Constraints

Optimization under multiple linear constraints is important for practical systems with individual power constraints, per-antenna power constraints, and/or interference constraints as in cognitive radios. While for single-user multiple-input multiple-output (MIMO) channel transmitter optimization, no one uses general purpose convex programming because water-filling is optimal and much simpler, it is not true for MIMO multiaccess channels (MAC), broadcast channels (BC), and the nonconvex optimization of interference networks because the traditional water-filling is far from optimal for networks. We recently found the right form of water-filling, polite water-filling, for capacity or achievable regions of the general MIMO interference networks, named B-MAC networks, which include BC, MAC, interference channels, X networks, and most practical wireless networks as special cases. In this paper, we extend the polite water-filling results from a single linear constraint to multiple linear constraints and use weighted sum-rate maximization as an example to show how to design high efficiency and low complexity algorithms, which find optimal solution for convex cases and locally optimal solution for nonconvex cases. Several times faster convergence speed and orders of magnitude higher accuracy than the state-of-the-art are demonstrated by numerical examples.

MIMO B-MAC Interference Network Optimization Under Rate Constraints by Polite Water-Filling and Duality

We take two new approaches to design efficient algorithms for transmitter optimization under rate constraints in order to guarantee the Quality of Service for MIMO B-MAC interference networks. A B-MAC network is a generalized interference network that is a combination of multiple interfering broadcast channels (BC) and multiaccess channels (MAC). Two related optimization problems, maximizing the minimum of weighted rates under a sum-power constraint and minimizing the sum-power under rate constraints, are considered. The first approach takes advantage of existing algorithms for SINR problems by building a bridge between rate and SINR through the design of optimal mappings between them. The second approach exploits the polite water-filling structure, which is the network version of water-filling satisfied by all the Pareto optimal input of a large class of achievable regions of B-MAC networks. It replaces most generic optimization algorithms currently used for such networks and reduces the complexity while demonstrating superior performance even in non-convex cases. Both centralized and distributed algorithms are designed and the performance is analyzed in addition to numeric examples.

Opportunistic Scheduling for Heterogeneous Services in Downlink OFDMA System

We consider opportunistic scheduling in a downlink OFDMA system to support heterogeneous services. Given the users queue state information (QSI) and channel state information (CSI), the proposed sum waiting time based scheduling (SWBS) algorithm can guarantee QoS requirements for both real-time (RT) and non-real-time (NRT) service. The benefit is attained from scheduling heterogeneous services based on sum packet waiting time to fulfill the QoS requirements and exploiting multiuser diversity to achieve effective resource utilization. Simulation results illustrate that the max packet loss ratio and packet delay for real-time service and the throughput for non-real-time service are improved by the proposed algorithm.

Joint user scheduling and antenna selection in distributed massive MIMO systems with limited backhaul capacity

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station (BS). In a distributed massive MIMO system, the capacity of fiber backhaul that links base station and remote radio heads is usually limited, which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink. To solve this problem, we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity. Three sub-optimal iterative algorithms with the objective of sum-rate maximization are proposed for the joint optimization of antenna selection and user scheduling, either based on greedy fashion or Frobenius-norm criteria. Convergence and complexity analysis are presented for the algorithms. The provided Monte Carlo simulations show that, one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.

A greedy-based resource allocation algorithm for multicast and unicast services in OFDM system

We propose a resource allocation algorithm for OFDM system to provide QoS guarantee for unicast and multicast service concurrently. The optimization objective is to maximize the sum rate of the unicast service under the constraint of both the total power and the minimal rate provision of the multicast service, which can be solved by traditional two-step approach. To decrease the complexity, a greedy-based algorithm is proposed. According to the theoretical analysis and the simulation results, the performance of the proposed algorithm is close to the two-step approach meanwhile the complexity is effectively decreased. In conclusion, the proposed algorithm can balance the performance and the complexity well and so is suitable to be applied to practical scenario.

Analysis of channel correlation characteristics in GEO mobile satellite communications

Channel prediction is the premise for the application of adaptive transmission in mobile satellite communications with long propagation delay. Particularly, in GEO mobile satellite communication systems, the satellite channel correlation characteristics are analyzed for the mobile terminals at different positions and along different directions, and the predictability of the satellite channel is discussed associated with the channel correlation results. The foundation for the application of adaptive transmission in GEO mobile satellite communications is established accordingly.

Step Reduced K-Best Sphere Decoding

We propose an algorithm that reduces the complexity of the K-best sphere decoding (K-best SD) algorithm, which is a powerful parallel detection algorithm for multiple-input multiple-output systems (MIMO). By analyzing the probability of different nodes to be the final solution, the algorithm prunes some nodes during the tree search to reduce the complexity. Simulation results prove that compared with the K-best SD algorithm the proposed algorithm performance drops very little. Compared with the famous fixed-complexity sphere decoding (FSD) with the same complexity, the proposed algorithm has better performance.

Expanded Time-Frequency OFDM and its interference suppression techniques

An approach to interference suppression in an Expanded Time-Frequency OFDM (ETF-OFDM) system is presented in this paper. Because of the special structure, the ETF-OFDM system inherently suppresses variously co-existing interference simultaneously, such as single tone interference, narrow band interference, and short-time pulse burst interference, to a certain extent. We can obtain an improved performance with simple interference suppression methods which fit in the system and involve low complexity. Simulation results prove that under the same simulation conditions, the ETF-OFDM system outperforms the MC-CDMA system with the same interference suppression methods in presence of the same interference.

Scalable ad hoc networks for arbitrary-cast: practical broadcast-relay transmission strategy leveraging physical-layer network coding

The capacity of wireless ad hoc networks is constrained by the interference of concurrent transmissions among nodes. Instead of only trying to avoid the interference, physical-layer network coding (PNC) is a new approach that embraces the interference initiatively. We employ a network form of interference cancellation, with the PNC approach, and propose the multihop, broadcast-relay transmission strategy in linear, rectangular, and hexagonal networks. The theoretical analysis shows that it gains the transmission efficiency by the factors of 2.5 for the rectangular networks and 2 for the hexagonal networks. We also propose a practical signal recovery algorithm in the physical layer to deal with the influence of multipath fading channels and time synchronization errors, as well as to use media access control (MAC) protocols that support the simultaneous receptions. This transmission strategy obtains the same efficiency from one-to-one communication to one-to-many. By our approach, the number of the users/terminals of the network has better scalability, and the overall network throughput is improved.

Superimposed training design based on Bayesian optimisation for channel estimation in two-way relay networks

In this study, the superimposed training strategy is introduced into orthogonal frequency division multiplexing-modulated amplify-and-forward two-way relay network (TWRN) to perform two-hop transmission-compatible individual channel estimation. Through the superposition of an additional training vector at the relay under power allocation, the separated source–relay channel information can be directly obtained at the destination and then used to estimate the channels. The closed-form Bayesian Cramer-Rao lower bound (CRLB) is derived for the estimation of block-fading frequency-selective channels with random channel parameters, and orthogonal training vectors from the two source nodes are required to keep the Bayesian CRLB simple because of the self-interference in the TWRN. A set of optimal training vectors designed from the Bayesian CRLB are applied in an iterative linear minimum mean-square-error channel estimation algorithm, and the mean-square-error performance is provided to verify the Bayesian CRLB results.