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Rate splitting (RS) is a potentially powerful and flexible technique for multi-antenna downlink transmission. In this paper, we address several technical challenges towards its practical implementation for beyond 5G systems. To this end, we focus on a single-cell system with a multi-antenna base station (BS) and K single-antenna receivers. We consider RS in its most general form with 2 K − 1 streams, and joint decoding to fully exploit the potential of RS. First, we investigate the achievable rates under joint decoding and formulate the precoder design problems to maximize a general utility function, or to minimize the transmit power under pre-defined rate targets. Building upon the concave-convex procedure (CCCP), we propose precoder design algorithms for an arbitrary number of users. Our proposed algorithms approximate the intractable non-convex problems with a number of successively refined convex problems, and provably converge to stationary points of the original problems. Then, to reduce the decoding complexity, we consider the optimization of the precoder and the decoding order under successive decoding. Further, we propose a stream selection algorithm to reduce the number of precoded signals. With a reduced number of streams and successive decoding at the receivers, our proposed algorithm can even be implemented when the number of users is relatively large, whereas the complexity was previously considered as prohibitively high in the same setting. Finally, we propose a simple adaptation of our algorithms to account for the imperfection of the channel state information at the transmitter. Numerical results demonstrate that the general RS scheme provides a substantial performance gain as compared to state-of-the-art linear precoding schemes, especially with a moderately large number of users.

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A 360 virtual reality (VR) video, recording a scene of interest in every direction, provides VR users with immersive viewing experience. However, transmission of a 360 VR video which is of a much larger size than a traditional video to mobile users brings a heavy burden to a wireless network. In this paper, we consider multi-quality multicast of a 360 VR video from a single server to multiple users using time division multiple access (TDMA). To improve transmission efficiency, tiling is adopted, and each tile is pre-encoded into multiple representations with different qualities. We optimize the quality level selection, transmission time allocation and transmission power allocation to maximize the total utility of all users under the transmission time and power allocation constraints as well as the quality smoothness constraints for mixed-quality tiles. The problem is a challenging mixed discrete-continuous optimization problem. We propose two low-complexity algorithms to obtain two suboptimal solutions, using continuous relaxation and DC programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.Index Terms-virtual reality, 360 video, multi-quality multicast, convex optimization, difference of convex programming.

When a cache is shared by multiple cores, its space may be allocated either by sharing, partitioning, or both. We call the last case partition-sharing. This paper studies partition-sharing as a general solution, and presents a theory an technique for optimizing partition-sharing. We present a theory and a technique to optimize partition sharing. The theory shows that the problem of partition-sharing is reducible to the problem of partitioning. The technique uses dynamic programming to optimize partitioning for overall miss ratio, and for two different kinds of fairness.Finally, the paper evaluates the effect of optimal cache sharing and compares it with conventional solutions for thousands of 4-program co-run groups, with nearly 180 million different ways to share the cache by each co-run group. Optimal partition-sharing is on average 26% better than freefor-all sharing, and 98% better than equal partitioning. We also demonstrate the trade-off between optimal partitioning and fair partitioning.