This work identifies the fundamental limits of cache-aided coded multicasting in the presence of the well-known worst-user' bottleneck. This stems from the presence of receiving users with uneven channel capacities, which often forces the rate of transmission of each multicasting message to be reduced to that of the slowest user. This bottleneck, which can be detrimental in general wireless broadcast settings, motivates the analysis of coded caching over a standard Single-Input-Single-Output (SISO) Broadcast Channel (BC) with K cache-aided receivers, each with a generally different channel capacity. For this setting, we design a communication algorithm that is based on superposition coding that capitalizes on the realization that the user with the worst channel may not be the real bottleneck of communication. We then proceed to provide a converse that shows the algorithm to be near optimal, identifying the fundamental limits of this setting within a multiplicative factor of 4. Interestingly, the result reveals that, even if several users are experiencing channels with reduced capacity, the system can achieve the same optimal delivery time that would be achievable if all users enjoyed maximal capacity.
In a related line of work, the papers @cite_15 and @cite_30 studied the cache-aided topological interference channel where @math cache-aided transmitters are connected to @math cache-aided receivers, and each transmitter is connected to one receiver via a direct strong'' link and to each of the other receivers via weak'' links. Under the assumption of no channel state information at the transmitters (CSIT), the authors showed how the lack of CSIT can be ameliorated by exploiting the topology of the channel and the multicast nature of the transmissions.
@cite_30 @cite_15
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This work explores cache-aided interference management in the absence of channel state information at the transmitters (CSIT), focusing on the setting with K transmitter receiver pairs endowed with caches, where each receiver k is connected to transmitter k via a direct link with normalized capacity 1, and to any other transmitter via a cross link with normalized capacity t ≤ 1. In this setting, we explore how a combination of pre-caching at transmitters and receivers, together with interference enhancement techniques, can a) partially counter the lack of CSIT, and b) render the network self-sufficient, in the sense that the transmitters need not receive additional data after pre-caching. Toward this we present new schemes that blindly harness topology and transmitter-and-receiver caching, to create separate streams, each serving many receivers at a time. Key to the approach here is a combination of rate-splitting, interference enhancement and coded caching. We consider the content delivery problem in a fading multi-input single-output channel with cache-aided users. We are interested in the scalability of the equivalent content delivery rate when the number of users, @math , is large. Analytical results show that, using coded caching and wireless multicasting, without channel state information at the transmitter, linear scaling of the content delivery rate with respect to @math can be achieved in some different ways. First, if the multicast transmission spans over @math independent sub-channels, e.g., in quasi-static fading if @math , and in block fading or multi-carrier systems if @math , linear scaling can be obtained, when the product of the number of transmit antennas and the number of sub-channels scales logarithmically with @math . Second, even with a fixed number of antennas, we can achieve the linear scaling with a threshold-based user selection requiring only one-bit feedbacks from the users. When CSIT is available, we propose a mixed strategy that combines spatial multiplexing and multicasting. Numerical results show that, by optimizing the power split between spatial multiplexing and multicasting, we can achieve a significant gain of the content delivery rate with moderate cache size. The Interfering Broadcast Channel (IBC) applies to the downlink of (cellular and or heterogeneous) multi-cell networks, which are limited by multi-user (MU) interference. The interference alignment (IA) concept has shown that interference does not need to be inevitable. In particular spatial IA in the MIMO IBC allows for low latency transmission. However, IA requires perfect and typically global Channel State Information at the Transmitter(s) (CSIT), whose acquisition does not scale well with network size. Also, the design of transmitters (Txs) and receivers (Rxs) is coupled and hence needs to be centralized (cloud) or duplicated (distributed approach). CSIT, which is crucial in MU systems, is always imperfect in practice. We consider the joint optimal exploitation of mean (channel estimates) and covariance Gaussian partial CSIT. Indeed, in a Massive MIMO (MaMIMO) setting (esp. when combined with mmWave) the channel covariances may exhibit low rank and zero-forcing might be possible by just exploiting the covariance subspaces. But the question is the optimization of beamformers for the expected weighted sum rate (EWSR) at finite SNR. We propose explicit beamforming solutions and indicate that existing large system analysis can be extended to handle optimized beamformers with the more general partial CSIT considered here. We consider the cache-aided MISO broadcast channel (BC) in which a multi-antenna transmitter serves @math single-antenna receivers, each equipped with a cache memory. The transmitter has access to partial knowledge of the channel state information. For a symmetric setting, in terms of channel strength levels, partial channel knowledge levels and cache sizes, we characterize the generalized degrees of freedom (GDoF) up to a constant multiplicative factor. The achievability scheme exploits the interplay between spatial multiplexing gains and coded-multicasting gain. On the other hand, a cut-set-based argument in conjunction with a GDoF outer bound for a parallel MISO BC under channel uncertainty is used for the converse. We further show that the characterized order-optimal GDoF is also attained in a decentralized setting, where no coordination is required for content placement in the caches.