Search for: All records

We present a novel Packet Type (PT)-based design framework for the finite-length analysis of Device-to-Device (D2D) coded caching. By the exploitation of the asymmetry in the coded delivery phase, two fundamental forms of subpacketization reduction gain for D2D coded caching, i.e., the subfile saving gain and the further splitting saving gain, are identified in the PT framework. The proposed framework features a streamlined design process which uses several key concepts including user grouping, subfile and packet types, multicast group types, transmitter selection, local/global further splitting factor, and PT design as an integer optimization. In particular, based on a predefined user grouping, the subfile and multicast group types can be determined and the cache placement of the users can be correspondingly determined. In this stage, subfiles of certain types can be potentially excluded without being used in the designed caching scheme, which we refer to as subfile saving gain. In the delivery phase, by a careful selection of the transmitters within each type of multicast groups, a smaller number of packets that each subfile needs to be further split into can be achieved, leading to the further splitting saving gain. The joint effect of these two gains results in an overall subpacketization reduction compared to the Ji-Caire-Molisch (JCM) scheme [1]. Using the PT framework, a new class of D2D caching schemes is constructed with order reduction on subpacketization but the same rate when compared to the JCM scheme. 

We consider the cache-aided multiuser private information retrieval (MuPIR) problem with a focus on the special case of two messages, two users and arbitrary number of databases where the users have distinct demands of the messages. We characterize the optimal memory-load trade-off for the considered MuPIR problem by proposing a novel achievable scheme and a tight converse. The proposed achievable scheme uses the idea of cache-aided interference alignment (CIA) developed in the literature by the same authors. The proposed converse uses a tree-like decoding structure to incorporate both the decodability and privacy requirements of the users. While the optimal characterization of the cache-aided MuPIR problem is challenging in general, this work provides insight into understanding the general structure of the cache-aided MuPIR problem. 

In the coded caching literature, the notion of privacy is considered only against demands. On the motivation that multi-round transmissions almost appear everywhere in real communication systems, this paper formulates the coded caching problem with private demands and caches. Only one existing private caching scheme, which is based on introducing virtual users, can preserve the privacy of demands and caches simultaneously, but at the cost of an extremely large subpacketization exponential in the product of the number of users (K) and files (N) in the system. In order to reduce the subpacketization while satisfying the privacy constraints, we propose a novel approach which constructs private coded caching schemes through private information retrieval (PIR). Based on this approach, we propose novel schemes with private demands and caches which have a subpacketization level in the order exponential with K instead of NK in the virtual user scheme. As a by-product, for the coded caching problem with private demands, a private coded caching scheme could be obtained from the proposed approach, which generally improves the memory-load tradeoff of the private coded caching scheme by Yan and Tuninetti. 

In this work, we develop a two time-scale deep learning approach for beamforming and phase shift (BF-PS) design in time-varying RIS-aided networks. In contrast to most existing works that assume perfect CSI for BF-PS design, we take into account the cost of channel estimation and utilize Long Short-Term Memory (LSTM) networks to design BF-PS from limited samples of estimated channel CSI. An LSTM channel extrapolator is designed first to generate high resolution estimates of the cascaded BS-RIS-user channel from sampled signals acquired at a slow time scale. Subsequently, the outputs of the channel extrapolator are fed into an LSTM-based two stage neural network for the joint design of BF-PS at a fast time scale of per coherence time. To address the critical issue that training overhead increases linearly with the number of RIS elements, we consider various pilot structures and sampling patterns in time and space to evaluate the efficiency and sum-rate performance of the proposed two time-scale design. Our results show that the proposed two time-scale design can achieve good spectral efficiency when taking into account the pilot overhead required for training. The proposed design also outperforms a direct BF-PS design that does not employ a channel extrapolator. These demonstrate the feasibility of applying RIS in time-varying channels with reasonable pilot overhead. 

In this paper, a practical precoding method for the downlink of filter bank multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) is developed. The proposed method includes a two-stage precoder consisting of a fractionally spaced prefilter (FSP) per subcarrier for flattening/equalizing the channel across the subcarrier band, followed by a conventional precoder whose goal is to concentrate the signals of different users at their spatial locations. This way, each user receives only the intended information. In this paper, we take note that channel reciprocity may not hold perfectly in practical scenarios due to the mismatch of radio chains in uplink and downlink. Additionally, channel state information (CSI) at the base station may not be perfectly known. This, together with imperfect channel reciprocity can lead to detrimental effects on the downlink precoder performance. We theoretically analyze the performance of the proposed precoder in the presence of imperfect CSI and channel reciprocity calibration errors. This leads to an effective method for compensating these effects. Finally, we numerically evaluate the performance of the proposed precoder. Our results show that the proposed precoder leads to an excellent performance when benchmarked against OFDM.