Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

Zhao, Junlin; Gündüz, Deniz; Simeone, Osvaldo; Gómez-Barquero, David

doi:10.1109/tbc.2019.2932339

Cited by 26 publications

(14 citation statements)

References 30 publications

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“…The decoding order of s 0 and s k can be optimized for each instantaneous channel condition. The decoding order follows the rules that the data stream intended for more users has a higher decoding priority [5], [7]. Hence, we assume that the multicast stream is decoded first and removed from the received signal using SIC before decoding the unicast streams at all the users.…”

Section: A Mu-lpmentioning

confidence: 99%

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis

Mao

Clerckx

2019

IEEE Trans. Commun.

257

151

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In a Non-Orthogonal Unicast and Multicast (NOUM) transmission system, a multicast stream intended to all the receivers is superimposed in the power domain on the unicast streams. One layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding its intended unicast stream. In this paper, we first show that a linearly-precoded 1layer Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast messages into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all the users, the SIC is better reused for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference among the unicast streams. We further propose multi-layer transmission strategies based on the generalized RS and power-domain Non-Orthogonal Multiple Access (NOMA). Two different objectives are studied for the design of the precoders, namely, maximizing the Weighted Sum Rate (WSR) of the unicast messages and maximizing the system Energy Efficiency (EE), both subject to Quality of Service (QoS) rate requirements of all the messages and a sum power constraint. A Weighted Minimum Mean Square Error (WMMSE)-based algorithm and a Successive Convex Approximation (SCA)-based algorithm are proposed to solve the WSR and EE problems, respectively. Numerical results show that the proposed RS-assisted NOUM transmission strategies are more spectrally and energy efficient than the conventional Multi-User Linear-Precoding (MU-LP), Orthogonal Multiple Access (OMA) and power-domain NOMA in a wide range of user deployments (with a diversity of channel directions, channel strengths and qualities of channel state information at the transmitter) and network loads (underloaded and overloaded regimes). It is superior for the downlink multi-antenna NOUM transmission.

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Section: A Mu-lpmentioning

confidence: 99%

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis

Mao

Clerckx

2019

IEEE Trans. Commun.

257

151

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“…Its boundary is calculated by varying the weights assigned to users. We follow the weights in [11], where the weight of user-1 is fixed to u 1 = 1 for each weight of user-2 in u 2 ∈ 10 [−3,−1,−0.95,··· ,0.95, 1,3] . The precoders of RS, MU-LP and SC-SIC are initialized using the same methods as discussed in [10].…”

Section: Optimization Frameworkmentioning

confidence: 99%

“…Index Terms-non-orthogonal multicast and unicast transmission, rate-splitting, rate region, WMMSE algorithm I. INTRODUCTION The scarcity of radio resources and the heterogeneity of wireless applications in 5G and beyond motivate recent research on the non-orthogonal unicast and multicast transmission [1]- [4], which is based on Layered Division Multiplexing (LDM) in the literature of digital television systems [5]. The unicast and multicast messages are precoded and superimposed at the transmitter and then broadcast to the receivers in the same timefrequency resources.…”

mentioning

confidence: 99%

“…Each receiver decodes and removes the multicast message using Successive Interference Cancellation (SIC) before decoding its intended unicast message. Joint unicast and multicast beamforming of multi-cell cooperative transmission has been studied, e.g., for transmit power minimization [1], [2], Weighted Sum Rate (WSR) maximization [3] and energy efficiency maximization [4]. However, all of the above works consider the use of Multi-User Linear Precoding (MU-LP) beamforming for the unicast messages.…”

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confidence: 99%

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Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission

Mao

Clerckx

2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In a superimposed unicast and multicast transmission system, one layer of Successive Interference Cancellation (SIC) is required at each receiver to remove the multicast stream before decoding the unicast stream. In this paper, we show that a linearly-precoded Rate-Splitting (RS) strategy at the transmitter can efficiently exploit this existing SIC receiver architecture. By splitting the unicast message into common and private parts and encoding the common parts along with the multicast message into a super-common stream decoded by all users, the SIC is used for the dual purpose of separating the unicast and multicast streams as well as better managing the multi-user interference between the unicast streams. The precoders are designed with the objective of maximizing the Weighted Sum Rate (WSR) of the unicast messages subject to a Quality of Service (QoS) requirement of the multicast message and a sum power constraint. Numerical results show that RS outperforms existing Multi-User Linear-Precoding (MU-LP) and power-domain Non-Orthogonal Multiple Access (NOMA) in a wide range of user deployments (with a diversity of channel directions and channel strengths). Moreover, since one layer of SIC is required to separate the unicast and multicast streams, the performance gain of RS comes without any increase in the receiver complexity compared with MU-LP. Hence, in such non-orthogonal unicast and multicast transmissions, RS provides rate and QoS enhancements at no extra cost for the receivers.Index Terms-non-orthogonal multicast and unicast transmission, rate-splitting, rate region, WMMSE algorithm

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“…The performance of the joint multicast and unicast transmission with partial CSI is studied in [11]. A more general scenario is considered in [12] for a multi-cell network, where each base station (BS) sends multiple independent multicast messages and each user can decode an arbitrary subset of these multicast messages from all BSs using successive group decoding.Recently, layered-division multiplexing (LDM), a form of non-orthogonal multiplexing technology [13], has been introduced in cellular networks for joint multicast and unicast transmission [14], [15]. It is a key technology for next-generation terrestrial digital television standard ATSC 3.0 [16].…”

mentioning

confidence: 99%

Joint Base Station Clustering and Beamforming for Non-Orthogonal Multicast and Unicast Transmission With Backhaul Constraints

Chen

Tao

Liu

2018

IEEE Trans. Wireless Commun.

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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...

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Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

Cited by 26 publications

References 30 publications

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis

Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission

Joint Base Station Clustering and Beamforming for Non-Orthogonal Multicast and Unicast Transmission With Backhaul Constraints

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