Cochannel interference cancelation for multiple access systems

Many properties of frequency-hopped spread-spectrum multiple access （FH/SSMA）, such as, robustness against frequency selective fading and jamming make it a preferred multiple access communication scheme. But cochannel interference （CCI） can seriously harm the system＇s performance. On the basis of the analysis of the CCI pattern of frequency hopped multiple access signal, a new multiuser detection algorithm for canceling the CCI in frequency hopped multiple access systems is proposed. In the proposed scheme, the CCI is canceled by first detecting the frequency hopping slot, and then classifying and regenerating the candidates of the interference pattern from the time-frequency matrix. Both the theoretical analysis and computer simulation results show that compared to the conventional detector and Mabuchi＇s detector, this new multiuser detector has lower complexity, better convergence stability, and is suitable for both additive white Gaussian noise （AWGN） channels and Rayleigh fading channels.

Performance of Relay Networks in Fading Environments with Dominant Specular Components

Radio propagation in dense and super dense wireless networks as well as indoor-to-outdoor picocell networks can have multiple line-of-sight or multiple specular components. The performance of a dual-hop decode-and-forward relaying system over multiple specular components fading channels(MSCC)with multiple Rayleigh distributed co-channel interferers in an interference-limited environment is investigated. The MSCC fading model is designed to allow direct and meaningful comparisons to be made between line-of-sight channels and non-line-of-sight channels, with exact parameter correspondences. Comparisons of outage and bit error performance between Nakagami-m/Rayleigh and MSCC/Rayleigh fading environments show that the MSCC model is needed to describe line-of-sight channels that cannot be accurately modeled by the Nakagami-m, or other fading models.

Grassmannian precoding MU-MIMO scheme

In this article, a Grassmannian precoding multiuser multiple-input multiple-output （MU-MIMO） scheme for downlink transmission is proposed. The proposed MU-MIMO scheme will perform scheduling and precoding simultaneously at the base station, to obtain both the multiuser diversity gain and the precoding gain, to maximize the system capacity. The precoding method is related to Grassmannian precoding, which extends the point-to-point single-user Grassmannian precoding to point-to-multipoint multiuser Grassmannian precoding. It provides further significant system capacity enhancement than the single user MIMO （SU-MIMO） system and also outperforms the block dia^onalization （BD） algorithm under the same simulation environment.

Efficient user selection for multi-cell multi-user MIMO systems with limited backhaul

Multi-cell multi-user multiple-input multiple-output （MC-MU-MIMO） is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing （MCP） systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit （CU）. Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.