Capacity analysis of the clustered network MIMO with overlap

Ran, Rong; An, Chan-ho; Kim, Dongku; Lau, V.K.N.

doi:10.1109/wcnc.2012.6214106

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…However, most existing analyses of such systems have been carried out either in a single cluster of cooperating BSs [5], [6], or based on simplified Wyner channel models [7]- [9]. Largesystem analyses of network MIMO systems have been reported in [10], [11]. However, it is typically difficult to account for geographic locations of BSs in the analysis.…”

Section: A Related Workmentioning

confidence: 99%

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A Stochastic Analysis of Network MIMO Systems

Hosseini

Adve

2016

IEEE Trans. Signal Process.

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Abstract-This paper quantifies the benefits and limitations of cooperative communications by providing a statistical analysis of the downlink in network multiple-input multiple-output (MIMO) systems. We consider an idealized model where the multiple-antenna base-stations (BSs) are distributed according to a homogeneous Poisson point process and cooperate by forming disjoint clusters. We assume that perfect channel state information (CSI) is available at the cooperating BSs without any overhead. Multiple single-antenna users are served using zero-forcing beamforming with equal power allocation across the beams. For such a system, we obtain tractable, but accurate, approximations of the signal power and inter-cluster interference power distributions and derive a computationally efficient expression for the achievable per-BS ergodic sum rate using tools from stochastic geometry. This expression allows us to obtain the optimal loading factor, i.e., the ratio between the number of scheduled users and the number of BS antennas, that maximizes the per-BS ergodic sum rate. Further, it allows us to quantify the performance improvement of network MIMO systems as a function of the cooperating cluster size. We show that to perform zero-forcing across the distributed set of BSs within the cluster, the network MIMO system introduces a penalty in received signal power. Along with the inevitable out-of-cluster interference, we show that the per-BS ergodic sum rate of a network MIMO system does not approach that of an isolated cell even at unrealistically large cluster sizes. Nevertheless, network MIMO does provide significant rate improvement as compared to uncoordinated single-cell processing even at relatively modest cluster sizes.

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Section: A Related Workmentioning

confidence: 99%

“…Theorem 1: Under Approximations 1-5, the per-BS ergodic sum rate of a network MIMO system with ZF beamforming and equal power assignment in each cluster is given by (12) in nats per second per Hertz. In (12) , indicates the shape parameter of the Gamma distribution in (11) given by…”

Section: Stochastic Geometry Analysis Of Network Mimo Systemsmentioning

confidence: 99%

A Stochastic Analysis of Network MIMO Systems

Hosseini

Adve

2016

IEEE Trans. Signal Process.

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“…Specifically, the achievable rate regions derived in [6] and [7] are based on a two-BS-two-user channel model, while [13] and [14] consider Wyner-like channel models and report the achievable rates and capacity bounds, respectively. In [15] and [16], large-system analysis of network MIMO system is carried out. Although based on simplified models, these previous information-theoretical results already reveal the benefits of C-RAN in significantly improving the system performance.…”

Section: A Related Workmentioning

confidence: 99%

“…1) Fix w L k k , ∀k, compute the MMSE receiver u k and the corresponding MSE e k according to (21) and (22); 2) Update the MSE weight ρ k according to (14); 3) Find the optimal transmit beamformer w L k k under fixed u k and ρ k , ∀k, by solving the QCQP problem (16) with w k , H j and H k replaced by w L k k , H L k j and H L k k respectively; 4) Compute the achievable rate R k according to (23), ∀k; 5) UpdateR k = R k and β k according to (20), ∀k. Until convergence…”

Section: Algorithm 2 Wsr Maximization With Per-bs Backhaul Constraintmentioning

confidence: 99%

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

Dai

2014

IEEE Access

373

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This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities. Each user is associated with a user-centric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming. Under this setup, this paper investigates the user scheduling, BS clustering, and beamforming design problem from a network utility maximization perspective. Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints. We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots. In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, whereas in the latter case, the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints. In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted 1 -norm technique. This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach. This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes. This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.INDEX TERMS Cloud radio access network (C-RAN), network multiple-input multiple-output (MIMO), coordinated multi-point (CoMP), limited backhaul, user scheduling, base-station clustering, beamforming, weighted sum rate maximization, weighted minimum mean square error (WMMSE).

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“…Specifically, the achievable rate regions derived in [6], [7] are based on a two-BS-two-user channel model, while [13] and [14] consider Wyner-like channel models and report the achievable rates and capacity bounds, respectively. In [15], [16], large-system analysis of network MIMO system is carried out. Although based on simplified models, these previous information-theoretical results already reveal the benefits of C-RAN in significantly improving the system performance.…”

Section: A Related Workmentioning

confidence: 99%

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

Dai¹,

Yu²

2014

Preprint

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This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities. Each user is associated with a usercentric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming. Under this setup, this paper investigates the user scheduling, BS clustering and beamforming design problem from a network utility maximization perspective. Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints. We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots. In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, while in the latter case the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints. In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted ℓ1-norm technique. This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach. This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes. This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.

show abstract

Capacity analysis of the clustered network MIMO with overlap

Cited by 4 publications

References 10 publications

A Stochastic Analysis of Network MIMO Systems

A Stochastic Analysis of Network MIMO Systems

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

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