This paper presents a probabilistic greedy pursuit （PGP） algorithm for compressed wide-band spectrum sensing under cognitive radio （CR） scenario. PGP relies on streaming compressed sensing （CS） framework, which differs from traditional CS processing way that only focuses on fixed-length signal＇s compressive sampling and reconstruction. It utilizes analog-to-information converter （AIC） to perform sub-Nyquist rate signal acquisition at the radio front-end （RF） of CR, the measurement process of which is carefully designed for streaming framework. Since the sparsity of wide-band spectrum is unavailable in practical situation, PGP introduces the probabilistic scheme by dynamically updating support confidence coefficient and utilizes greedy pursuit to perform streaming spectrum estimation, which gains sensing performance promotion progressively. The proposed algorithm enables robust spectrum estimation without the priori sparsity knowledge, and keeps low computational complexity simultaneously, which is more suitable for practical on-line applications. Various simulations and comparisons validate the effectiveness of our approach.
A hybrid system of cellular mode and device-to-device （D2D） mode is considered in this paper, where the cellular resource is reused by the D2D transmission. With the objective of capacity maximization, the power optimization of D2D sub-system is considered, taking into account quality of service （QoS） requirement. The power optimization problem is divided into two stages： The first stage is the admission control scheme design based on the QoS requirement of D2D users, and the second is power allocation to maximize aggregate throughput of admissible D2D users. For the D2D admission control problem, a heuristic sorting-based algorithm is proposed to index the admissible D2D links, where gain to Interference ratio （GIR） sorting criterion is used. Applying an approximate form of Shannon capacity, the power allocation problem can be solved by convex optimization and geometric programming tools efficiently. Based on the theoretical analysis, a practical algorithm is proposed. The precision can reach a trade-off between complexity and performance. Numerical simulation results confirm that combining with GIR sorting method, the proposed scheme can significantly improve the D2D system＇s capacity and fairness.
FU Zi-xi HU Chun-jing PENG Tao LU Qian-xi WANG Wen-bo
端到端D2D(Device-to-Device)用户通过盲检测LTE系统中小区用户的物理层下行控制信道PDCCH(Physical Downlink Control Channel)获得小区中用户的资源分配信息,复用频谱资源,可以实现不经过基站的直联通信,提高无线资源利用率。提出一种基于"路径度量优势"进行PDCCH盲检测的实现方式,描述了它的计算方法,并对其性能进行了仿真分析。
With the development of wireless networks, the amount of multiple services increased sharply in recent years. High quality multiple services with low price are urgently needed especially in new generation mobile communication systems, e.g., 3G/LTE networks. It is important to enhance the availability of data service resources. Services have strong association which are used by clients with similar behavior habits in networks. Such feature results in service behavior convergence （SBC） and its utilization will enhance resource efficiency. This paper proposes two applications of service behavior： service prediction and a scheduling algorithm which enhances bandwidth efficiency. Convergence cells are classified according to SBC and hot-spot services are broadcasted separately in each convergence cell. It is demonstrated by stimulation that the bandwidth is saved 80% more than classical cellular system and nearly 20% more than traditional broadcasting system.
