Caching content on the edge of a network can effectively localize traffic, reduce network latency and improve network throughput. In this paper, we propose an a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretic analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs) and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0-1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks.

The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA) and direction of arrival (DOA) parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC) algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM) system, and the Cramer-Rao bound (CRB) is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and 2D matrix pencil (MP) algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.