This paper presents a content-centric transmission design in a cloud radio access network (cloud RAN) by incorporating multicasting and caching. Users requesting a same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache and it acquires the requested contents either from its local cache or from the central processor (CP) via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-ofservice constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed ℓ 0 -norm approximation and other techniques, the SBF problem is transformed into the difference of convex (DC) programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of heuristic caching strategies are also evaluated. Index TermsCloud radio access network (Cloud RAN), caching, multicasting, content-centric wireless networks, sparse beamforming.

Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper. Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem. A conventional method to tackle this problem is to approximate it as a semi-definite programming problem via semi-definite relaxation, whose performance, however, deteriorates considerably as the number of per-group users goes large. A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution by treating it as a difference of convex programming problem, whose complexity, however, increases dramatically as the problem size increases. In this paper, we propose a low-complexity highperformance algorithm for multi-group multicast beamforming design in large-scale wireless systems by leveraging the alternating direction method of multipliers (ADMM) together with CCP. In specific, the original non-convex QCQP problem is first approximated as a sequence of convex subproblems via CCP. Each convex subproblem is then reformulated as a novel ADMM form. Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel. Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude. Index TermsPhysical layer multicasting, large-scale optimization, non-convex quadratically constrained quadratic programming (QCQP), convex-concave procedure (CCP), alternating direction method of multipliers (ADMM). I. INTRODUCTIONMulticasting is a promising approach to deliver a common message to multiple receivers by exploiting the broadcast nature of wireless medium. It has great potential in many applications such as live video streaming, venue casting, mobile application updates, advertisements, and public group communications [2]- [4]. It can also be used in heterogeneous networks (HetNets) with wireless backhaul to push common information from a macro base station (BS) to multiple small BSs [5]. Recently, multicasting is shown to be useful for content delivery even when user demands are different in wireless cache networks [6], [7].Physical layer multicasting via beamforming further boosts the energy and spectrum efficiencies by exploiting channel state information at the transmitter. Multicast beamforming is first considered in [8] for a single group of users. The similar problem for multiple co-channel groups is later studied in [9]. In [10], coordinated multicast beamforming among BSs in multi-cell networks is investigated. The design of multicast beamforming in cellular networks with massive multiple-input multiple-output (MIMO) is studied in [11]. In general, the design of multicast beamforming is a non-convex quadratically constrained quadratic programming (QCQP) problem and its global optimal solution is difficult to obtain. A prevailing method to tackle this p...

The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this work, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and unicast services concurrently in cooperative multi-cell cellular networks with limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast message first, subtracts it, and then decodes its dedicated unicast message. We formulate a joint multicast and unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-unicast rate region. Index TermsLayered-division multiplexing (LDM), non-orthogonal multicast and unicast transmission, branch-and-bound (BB), sparse beamforming, convex-concave procedure (CCP). I. INTRODUCTIONThe broadcast nature of the wireless medium makes multicasting an efficient point-to-multipoint communication mechanism to deliver a same content concurrently to multiple interested users or devices. Recently, multicast services have been gaining increasing interests in cellular networks due to emerging applications such as live video streaming, venue casting, proactive multimedia content pushing, software updates, and public group communications [2]. In conventional cellular networks, multicast services have been allocated different time or frequency resources from those allocated to unicast services and adopt single-frequency network (SFN) transmission, as in the 3GPP specifications known as LTE-multicast [3]. However, such orthogonal resource sharing and transmission scheme has low spectrum efficiency and can significantly degrade the performance of the existing unicast services. Techniques that allow cellular networks to carry multicast and unicast services jointly in a more spectrum-efficient way are highly desirable. There are also many practical scenarios where a user needs to receive both multicast and unicast signals at the same time. For example, the network operator would like to offer multicast services like proactive content pushing, automatic software updates, and public group announcements to its subscribers without interrupting their on-going unicast services. Content providers can also embed personalized information (e.g., preferred subtitles and targeted advertisements) via unicast transmission along the multicast-based video streami...

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric multicast transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, which are either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed ℓ0/ℓ2-norm of beamforming vectors. To solve this nonconvex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.

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