This work proposes a novel location-based multi-group multicast framework which is termed as non-orthogonal multiple access (NOMA) assisted multi-region geocast. This novel spectrum sharing framework exploits the NOMA technology to realize the simultaneous delivery of different messages to different user groups characterized by different geographical locations. The essence of the proposed framework is that the geographical information of user groups unites NOMA and multi-group multicast to enhance the spectral efficiency (SE) and the energy efficiency (EE) of wireless transmissions. Specifically, we investigate the downlink beamforming design of the proposed framework in multiple-input single-output (MISO) settings. The decoding strategy for NOMA is designed and guaranteed by users' geographical information and required quality of service (QoS). The majorization and minimization (MM) algorithm is exploited to solve the non-convex and intractable problems therein. Comprehensive numerical experiments are further provided to show that NOMA holds tremendous promise but also limitations in terms of SE and EE, compared with spatial division multiple access (SDMA) and orthogonal multiple access (OMA).
The transmit beamforming for multi-group multicast have been initially solved with the semi-definite relaxation (SDR) method in @cite_1 @cite_11 . Further, a weighted max-min fair multi-group multicast problem has been studied in @cite_36 under per-antenna power constraints. In @cite_42 , the distributed beamforming design has been attempted for a multi-group multicasting relay network. However, all these works have neither exploited users' geographical information nor took the advantage of NOMA to further improve the SE and EE of wireless transmissions.
@cite_36 @cite_42 @cite_1 @cite_11
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The problem of transmit beamforming to multiple cochannel multicast groups is considered, when the channel state is known at the transmitter and from two viewpoints: minimizing total transmission power while guaranteeing a prescribed minimum signal-to-interference-plus-noise ratio (SINR) at each receiver; and a "fair" approach maximizing the overall minimum SINR under a total power budget. The core problem is a multicast generalization of the multiuser downlink beamforming problem; the difference is that each transmitted stream is directed to multiple receivers, each with its own channel. Such generalization is relevant and timely, e.g., in the context of the emerging WiMAX and UMTS-LTE wireless networks. The joint problem also contains single-group multicast beamforming as a special case. The latter (and therefore also the former) is NP-hard. This motivates the pursuit of computationally efficient quasi-optimal solutions. It is shown that Lagrangian relaxation coupled with suitable randomization cochannel multicast power control yield computationally efficient high-quality approximate solutions. For a significant fraction of problem instances, the solutions generated this way are exactly optimal. Extensive numerical results using both simulated and measured wireless channels are presented to corroborate our main findings. In this letter, we study the robust beamforming problem for the multi-antenna wireless broadcasting system with simultaneous information and power transmission, under the assumption of imperfect channel state information (CSI) at the transmitter. Following the worst-case deterministic model, our objective is to maximize the worst-case harvested energy for the energy receiver while guaranteeing that the rate for the information receiver is above a threshold for all possible channel realizations. Such problem is nonconvex with infinite number of constraints. Using certain transformation techniques, we convert this problem into a relaxed semidefinite programming problem (SDP) which can be solved efficiently. We further show that the solution of the relaxed SDP problem is always rank-one. This indicates that the relaxation is tight and we can get the optimal solution for the original problem. Simulation results are presented to validate the effectiveness of the proposed algorithm. A multiantenna transmitter that conveys independent sets of common data to distinct groups of users is considered. This model is known as physical layer multicasting to multiple cochannel groups. In this context, the practical constraint of a maximum permitted power level radiated by each antenna is addressed. The per-antenna power constrained system is optimized in a maximum fairness sense with respect to predetermined quality of service weights. In other words, the worst scaled user is boosted by maximizing its weighted signal-to-interference plus noise ratio. A detailed solution to tackle the weighted max-min fair multigroup multicast problem under per-antenna power constraints is therefore derived. The implications of the novel constraints are investigated via prominent applications and paradigms. What is more, robust per-antenna constrained multigroup multicast beamforming solutions are proposed. Finally, an extensive performance evaluation quantifies the gains of the proposed algorithm over existing solutions and exhibits its accuracy over per-antenna power constrained systems. This paper studies the application of non-orthogonal multiple access (NOMA) to cache-enabled cloud radio access network (CRAN) with mixed multicast and unicast transmission. Users requesting the same content are grouped together and served with a cluster of remote radio heads (RRHs) using distributed beamforming. In addition, the user with better channel condition in each group is allowed to request an extra unicast content via the NOMA protocol. Each RRH has a local cache which enables it to acquire the requested contents either from the local cache or from the central processor via the fronthaul link. Taking the maximum fronthaul capacity into consideration, we investigate the subchannel (SC) allocation problem to both RRHs and multicast groups to improve the weighted network sum rate. The optimal solution requires exhaustive search, which become prohibitively complicated as the number of RRHs and groups increases. To tackle this problem effectively, we formulate this problem as a three-sided matching problem among SCs, RRHs and multicast groups, and propose a novel low-complexity matching algorithm. We prove mathematically that the proposed algorithm converges to a stable matching within limited number of iterations. Numerical results unveil that the proposed algorithm closely approaches the optimal solution and outperforms the conventional orthogonal multiple access (OMA)-based CRAN.