A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems

Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

Self-Organization Approaches for Optimization in Cognitive Radio Networks

Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.

An Adaptive Traffic Distribution Scheme for CMT based on Lotka-Volterra Model in Multihomed Networks

CMT(concurrent multipath transfer) can increase throughput and transmission efficiency in multihomed networks. However, it is still an important challenge about distributing traffic adaptively into multiple access networks. Based on LV(Lotka.Volterra) model, we propose an adaptive traffic distribution scheme. In the scheme, two competition modes are concluded, multiple S.D(source.destination) streams competition for bandwidth of one path and multipath competition for traffic between each multihomed S.D host pair. Actually, each access network can establish a path for S.D pairs. So, in the first mode, each path is analogous to a predator, and overall traffic in a multihomed host is analogous to prey. Then, each path has to compete for the traffic by path information, e.g., bandwidth and congestion level. In the other one, if several S.D pairs pass through a shared path simultaneously, they will compete for bandwidth of the path. Here, each S.D pair is analogous to a predator, and the bandwidth of the common path is analogous to the prey. At last, compared with other three schemes, uniform traffic distribution, greedy path selection, random path selection in OPNET simulator, the proposed scheme can perform better onreducing file transmission time and increasing network throughput in FTP service.

Data management solutions based on the data distribution service communication model

This article presents a data management solution based on the data distribution service （DDS） communication model. The basic DDS communication model consists of a unidirectional data exchange where applications that publish data ＇push＇ the relevant data, which is updated to the local caches of co-located subscribers to the data [1]. DDS has no specified center node to forward data packets and maintain the communication data. This type of publish-subscribe （P/S） model presents integrity and consistency challenges in data management. Unlike peer-to-peer （P2P） distributed storage, DDS applications have a hard real-time environment and fewer data features, and the core problem is ensuring the integrity and consistency of data in distributed systems under this hard real-time environment. This article begins with a brief introduction of the communication model used by DDS, then analyzes persistent data management problems caused by such model, and provides an appropriate solution to these problems. This solution has been implemented in a prototype system of the real-time service bus （RTSB） of Tsinghua University.

Analysis of Classical Encryption Techniques in Cloud Computing

Cloud computing has become a significant computing model in the IT industry. In this emerging model,computing resources such as software, hardware, networking, and storage can be accessed anywhere in the world on a pay-per-use basis. However, storing sensitive data on un-trusted servers is a challenging issue for this model. To guarantee confidentiality and proper access control of outsourced sensitive data, classical encryption techniques are used. However, such access control schemes are not feasible in cloud computing because of their lack of flexibility, scalability, and fine-grained access control. Instead, Attribute-Based Encryption（ABE） techniques are used in the cloud. This paper extensively surveys all ABE schemes and creates a comparison table for the key criteria for these schemes in cloud applications.