Massive Multiple-Input Multiple-Output (MIMO), with its spatial multiplexing and channel hardening, has the potential to provide high capacity and reliability for massive video services. In spite of this, massive MIMO in the physical layer does not fully showcase its abilities in video applications unless it is specifically designed to do so. In this paper, we consider a cell-free massive MIMO system as an edge node for scheduling massive streams, and thus the standard server-to-client transmission is split into server-to-edge and edge-to-client. When the server-to-edge transmission is ideal, edge schedules massive streams to alleviate user conflicts in cell-free massive MIMO. Moreover, we propose a novel edge-to-client grouping algorithm for assigning the streams with severe interference to different time slots. The proposed algorithm achieves an innovative.7-approximation ratio while keeping the group sizes within a certain range. When the server-to-edge transmission is non-ideal, we design a special transmission framework called Aggressive, wherein the server sends the next video chunk when the current chunk reaches the edge rather than the client. Thus, the proposed framework saves considerable server-to-edge latency compared with the traditional framework. Simulation results show that the proposed user grouping algorithm improves the achievable rate by around 18% and the Aggressive framework improves the average bitrate.

A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5× throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

Femtocells are attractive candidates in the future cellular system such as long-term evolution (LTE). However, the interference introduced by femtocells may hinder the improved system performance in LTE networks. In this paper, we consider the challenging problem of interference management in LTE networks with femtocells, propose two frequency reuse patterns and a novel interference avoidance method with the frequency resources of the macrocell reused by femtocells. The key idea of the interference avoidance method is to identify the macrocell user equipments (MUEs) which are near femtocells with the help of spectrum sensing performed by femtocells and avoid MUEs and nearby femtocells using the same frequency resources through spectrum scheduling. Moreover, we conduct simulations to verify that our proposed method is successful in avoiding the potential interference and improving the network performance.

In this paper, we analyze the noise distribution and correlation characteristic on each subcarrier in OQAM-OFDM systems, and present a method based on the maximum likelihood (ML) criterion to improve the channel estimation. Then, the performance results are obtained by considering an IEEE 802.22 channel model and the proposed method is compared to the conventional frequency domain averaging (FDA) method. Simulation results show that the proposed method is an efficient method to improve the channel estimation.

In this paper, a novel spectrum sharing scheme is proposed to improve the achievable traffic load of secondary users (SUs) under grade of service (GoS) constraints in cognitive wireless networks with heterogeneous traffic. The key idea of the proposed scheme is to introduce preemptive priority and buffering mechanism for real-time traffic and non-real-time traffic, respectively, according to their different delay characteristics. The proposed scheme can reduce the forced termination probability and the blocking probability for heterogeneous calls simultaneously. Numerical results show that the proposed scheme can effectively improve the achievable traffic load of SUs under GoS constraints.