Proactive caching has emerged as a promising means to accommodate increased demands for wireless capacity. However, studies of proactive caching usually focus on minimizing the overall load of cache-aided networks. How to calculate each user's caching gain is still an open problem. In this paper, a two-phase cache-aided network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. Effective throughput is considered as a performance metric, which describes the reduction in each user's transmission cost. All possible values of effective throughputs achieved by legitimate caching policies form an achievable domain. It is proved that the achievable domain is a convex set and can be characterized by its boundary. A special type of caching policies, termed uncoded placement absolutely-fair (UPAF) caching, is studied. For the two-user case, games are formulated to allocate effective throughput gains for the two users. For the general multiuser case, a UPAF policy is proposed to organize user cooperation. It is shown that users with more concentrated preferences can obtain higher effective throughputs.

In order to handle users' heterogeneous quality of service requirements at the network edge, user preference aware lossy data compression is presented for edge caching. The symbols generated by an information source are compressed and then are transmitted to a base station. Before being transmitted to users, the symbols can be re-compressed at the network edge. User preferences are described by weights on the symbols. All possible values of the rates over the transmission links and the distortions suffered by the users form an achievable domain. The achievable domain is a convex set and an optimization problem is formulated in order to obtain its lower boundary. For discrete sources, the optimization problem is nonconvex and a DCA-based iterative algorithm is proposed to provide suboptimal code designs.

Remote reconstruction of trajectories of dynamical systems is now emerging as a mission-critical application in Industry 4.0 and beyond. Unfortunately, the statistical properties of signals generated by dynamical systems usually remain unknown, which prohibits the use of classic source coding methods relying on a statistical source model. To overcome this difficulty, we present a paradigm shift data transmission scheme, assuring that the reconstruction error is limited with the aid of the computation unit. It is found that the bit rate for transmitting trajectories has a significant relationship with the predictability of dynamical systems. Together with the concept of the Lyapunov exponent, an error growth function is introduced in this paper to classify the dynamical systems according to their predictability. A general expression of the bit rate is obtained in this paper. Furthermore, it is shown that the asymptotic value of the bit rate to reconstruct trajectories of a chaotic system is given by its Lyapunov exponent. The bit rate to reconstruct trajectories of non-chaotic systems is also presented. Simulation results show that our scheme outperforms conventional information-theory-based coding schemes, and can significantly reduce bandwidth requirements.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which presents a major challenge for UAV-aided communications. In this paper, we propose a latency-minimized transmission scheme for satisfying legitimate users' (LUs') content requests securely against Eve. By leveraging physical-layer security (PLS) techniques, we formulate a transmission latency minimization problem by jointly optimizing the UAV trajectory and user association. The resulting problem is a mixed-integer nonlinear program (MINLP), which is known to be NP hard. Furthermore, the dimension of optimization variables is indeterminate, which again makes our problem very challenging. To efficiently address this, we utilize bisection to search for the minimum transmission delay and introduce a variational penalty method to address the associated subproblem via an inexact block coordinate descent approach. Moreover, we present a characterization for the optimal solution. Simulation results are provided to demonstrate the superior performance of the proposed design.

Data compression is an efficient technique for saving data storage and transmission costs in networks. Traditional data compression methods usually compress each content item according to its own statistical distribution of symbols and do not take into account user preferences on various content items. However, user preferences significantly impact the statistical distributions of symbols transmitted over communication links. This paper presents an edge source coding method to compress data at the network edge, in which codebooks are designed based on not only the statistical distributions of symbols in the content items but also the user preferences. In edge source coding, multiple content items might be compressed via the same codebook. For discrete user preferences, DCA (difference of convex functions programming algorithm) based and k-means++ based algorithms are proposed to derive codebook designs. For continuous user preferences, a sampling method is applied to yield codebook designs. In addition, edge source coding is extended to the two-user case and codebooks are designed to utilize multicasting opportunities. Simulation results demonstrate that edge source coding significantly reduces transmission costs for short content items.

Source coding is an efficient technique to save data storage and transmission costs. Traditional source coding methods are usually designed based on the statistical distribution of symbols generated by the information source. Notice that, in content-centric networks, the user preference has a significant impact on the statistical distribution of symbols transmitted in the links. This paper presents an edge source coding method to compress data in the network edge. An optimization problem is formulated to obtain the optimal codebook design. Based on the solution of the single codebook case, an optimality condition for the optimization problem is presented. For discrete user preferences, a variant of the k-means++ algorithm is used to give a suboptimal solution. For continuous user preferences, two algorithms are proposed to give codebook designs. Both theoretical analysis and simulations demonstrate the optimal codebook design should take into account the user preferences.

Proactive caching is a promising means to handle increasing wireless traffic. However, how heterogeneous user preferences impact the caching gain is still an open problem. In this paper, a two-phase cache-aided multicasting network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. It is shown that the achievable domain of effective throughput of the users is a convex set and can be characterized by its boundary in the positive orthant. A special type of caching schemes, named uncoded placement absolutely fair (UPAF) caching, is studied. For the two user case, the achievable domain of UPAF policies has a piecewise linear boundary. For the multiuser case, a feasible UPAF policy is proposed to obtain caching and multicasting gains. Simulation results demonstrate that users with more concentrated preferences can attain higher effective throughput.

Pushing and caching are recognized as promising techniques to improve network capacity and handle explosive growth in data traffic. Caching schemes for synchronous requests have been extensively investigated, but little attention has been paid to the asynchronous case. However, asynchronism is an inherent property of user requests. In this paper, request delay information (RDI) is introduced to characterize the asynchronous user requests. Based on RDI, a coded joint pushing and caching (C-JPC) method is proposed to minimize the network traffic by jointly determining when and which data packets are to be pushed and whether they should be cached. Optimal offline and online C-JPC policies for noncausal and causal RDI can be obtained by solving optimization problems, which however are intractable. Fountain coded caching (FCC) and generalized coded caching (GCC) methods are further proposed to give sub-optimal C-JPC policies with low complexity. In addition, lower and upper bounds on the optimal traffic volume are presented. It is shown that FCC and GCC achieve optimal or near-optimal volumes of traffic in some special cases. Simulation results demonstrate that C-JPC brings significant multicasting gains.

Multicasting can reduce network traffic in multiuser systems by serving multiple users simultaneously. The benefits of multicasting basically come from the overlapping and synchronism of user requests. Pushing and caching are techniques that prestore content items in buffers closer to users based on the prediction of user requests, thereby providing a promising approach to eliminating the asynchronism of user requests and creating multicasting opportunities. This paper studies a multiuser wireless communication system, in which the buffers for caching are deployed in the user terminals. Based on the request delay information (RDI) which describes when the users request content items in a deterministic or statistical way, a joint pushing and caching (JPC) method is presented to schedule the content items pushed by the base station and cached in the user buffers. In this paper, multicasting JPC systems work in two modes, without or with the feedback of RDI. Furthermore, RDI is classified into three forms, namely, noncausal, statistical, and causal. Static and dynamic JPC policies are, respectively, proposed for the two work modes. In addition, the effective throughput resulting from JPC under the three forms of RDI is demonstrated via theoretical analysis and simulation.

Caching is a promising technique to reduce traffic by storing popular content items in the buffers of users prior to user demand. In this paper, a two-phase physical layer multicasting system is investigated, in which the users are equipped with buffers and content request delay information (RDI) is available at the serving base station (BS). Based on RDI, transmissions are initiated to satisfy user requests within delay constraints. To minimize the expected traffic, a joint pushing and caching (JPC) method is presented to jointly determine the content items transmitted by the BS and cached in the user buffers. It is shown that minimizing the expected traffic can be transformed into a network coding problem. A framework is presented to formulate the JPC policies via optimization problems, which however are intractable. To find the optimal uncoded caching policy, the optimization problems are reduced to linear programs. In addition, a generalized coded caching algorithm is proposed to give a sub- optimal coded JPC policy. A lower bound on the expected traffic is also analyzed. Simulations show that the traffic resulting from the proposed algorithms approaches the lower bound if the buffer size is large.

For content-centric wireless networks, pushing and proactive caching hold the promise of greatly improving system throughput in the future. However, the throughput gains obtained from these techniques are limited by the energy of the base station and the buffer size of the receivers. To make full use of this energy and these buffers, this paper presents a Joint Pushing and Caching (JPC) method for physical layer multicasting systems, where the request delay information (RDI) is exploited in noncausal, statistical, and causal form. In JPC, the content items pushed by the base station and removed from the buffers of receivers in each timeslot are jointly determined. With noncausal or statistical RDI, an offline policy is presented by formulating and solving an optimization problem in order to maximize the effective throughput. With causal RDI, an online policy is presented to further enhance the effective throughput with little signalling overhead. Causal feedback of RDI is found to bring a substantial throughput gain.