Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Proactive caching is capable of offloading data traffic during on-peak hours and improving system capacity, thereby attracting much attention recently. However, the energy efficiency (EE) gain brought by proactive caching has not been fully exploited yet in fading channels. In this paper, we present energy-efficient content pushing methods in fading channels, while guaranteeing the average delay or delay-outage constraint. In particular, a general rate and power adaptation scheme is adopted to support content pushing and on-demand delivery, helping to improve the EE. To maximize the EE, we derive the average delay, delay-outage probability, and EE for any given adaptive policy. Based on these results, EE maximization problems are formulated for a specific adaptive scheme, namely, the water-filling scheme. Formulated problems are then converted into one-dimensional optimization problems to reduce computational complexity. Simulations indicate a request probability threshold for content pushing, above which the content pushing performs better than the traditional on-demand transmission.

Proactive caching or pushing is capable of not only reducing the peak data rate and access latency, but also improving the overall spectral efficiency, thereby attracting much attention recently. However, there has not been much work on the energy efficiency (EE) of proactive caching over wireless links. In this paper, we present an energy-efficient pushing policy based on opportunistic transmissions with high EE in the physical layer. Our aim is to fully exploit the timeslots in which the channel gain is sufficiently high, while assuring that a user receives its desired content item within a certain average or maximal delay bound. To this end, we investigate the average delay, delay-outage probability, and average energy consumption of proactive caching with opportunistic transmissions. Based on these, we formulate two pushing-oriented EE maximization problems, the solutions of which imply optimal pushing methods. Simulations show that our pushing policies achieve a significant EE gain over on-demand opportunistic transmissions when the content item is popular.

Proactive caching is recognized as a promising approach to handle the rapid growth of data traffic, thereby attracting much attention recently. As a key performance metric of caching, the hit ratio is determined by demand probabilities of users for content items and caching decisions. Because the recommendation system is capable of shaping user demands, the joint caching and recommendation holds the potential of improving the hit ratio substantially. In this paper, joint pushing and recommendation (JPR) schemes are presented for multiuser multiple-input single-output (MISO) systems, in which content items are pushed over MISO downlinks with multicast beamforming. Aiming at maximizing the effective throughput, we formulate a multi-stage stochastic programming problem under the constraints of transmit power and quality of experience (QoE). Since the formulated problem is intractable, suboptimal online JPR policies are presented based on the convex-concave procedure and branch-and-bound methods. Simulations show that presented JPR policies are capable of attaining significant effective throughput gains.

Joint pushing and caching (JPC) is a promising approach to achieve high energy efficiency in content delivery systems. However, when content files are delivered in Gaussian broadcast channel, the optimal JPC policy that minimizes the transmission energy is still unknown. This paper focuses on the optimal JPC policy design in Gaussian broadcast channel, where the base station is capable of pushing private and common messages simultaneously. By pushing common messages to cache- enabled users, the transmission energy can be reduced, because the asynchronous requests for the same content file can be served by a single transmission. By analyzing the structure of the optimal policy under the constraints of buffer sizes and users' requests, we find that the pushing rate of the optimal policy remains unchanged between consecutive time instants corresponding to request times and request deadlines. Based on the structure of optimal pushing rate, we present a finite- dimension convex optimization problem, whose solution can be used to construct an optimal JPC policy.

Proactive caching holds the promise of increasing network throughput by caching popular contents at mobile users. The cache hit ratio heavily relies on the demand probability of a user for a content. As a powerful tool to shape user demand, the recommendation system has been introduced into caching networks to further improve the cache hit ratio. However, the joint design of pushing and recommendation for users with time-varying connectivity is still unknown. In this paper, we study joint pushing and recommendation (JPR) policies that maximize the effective throughput in wireless networks with time-varying user connectivity. Two offline and one online JPR policies are presented based on noncausal, statistical, and causal connectivity information, respectively. It is shown that the causal feedback brings significant throughput gain, especially when the recommendation weight and the connection probability are relatively small.

In order to achieve high spectral efficiency, considerable attention has been paid to full-duplex (FD) and non-orthogonal multiple access (NOMA) technologies in the past decades. When FD users and half-duplex (HD) users access to the base station simultaneously, however, the rate region is still unknown. In this paper, we investigate hybrid NOMA (H-NOMA) systems, where a multi-antenna FD base station serves both a FD user and a HD uplink user over the same frequency band. The base station adopts successive interference cancellation (SIC) to decode the uplink signals, while the FD user is capable of adopting SIC to decode and cancel the inter-user interference. Given fixed transmission power, the achievable rate region of the H-NOMA system is the convex hull of the union of the rate regions of two alternative schemes with and without SIC at the FD user. Specifically, we derive the explicit expression of the rate region and demonstrate the potential of the proposed switching methods via numerical results.