28 research outputs found
Wireless Communications in the Era of Big Data
The rapidly growing wave of wireless data service is pushing against the
boundary of our communication network's processing power. The pervasive and
exponentially increasing data traffic present imminent challenges to all the
aspects of the wireless system design, such as spectrum efficiency, computing
capabilities and fronthaul/backhaul link capacity. In this article, we discuss
the challenges and opportunities in the design of scalable wireless systems to
embrace such a "bigdata" era. On one hand, we review the state-of-the-art
networking architectures and signal processing techniques adaptable for
managing the bigdata traffic in wireless networks. On the other hand, instead
of viewing mobile bigdata as a unwanted burden, we introduce methods to
capitalize from the vast data traffic, for building a bigdata-aware wireless
network with better wireless service quality and new mobile applications. We
highlight several promising future research directions for wireless
communications in the mobile bigdata era.Comment: This article is accepted and to appear in IEEE Communications
Magazin
Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks
We consider scalable cell-free massive multiple-input multiple-output
networks under an open radio access network paradigm comprising user equipments
(UEs), radio units (RUs), and decentralized processing units (DUs). UEs are
served by dynamically allocated user-centric clusters of RUs. The corresponding
cluster processors (implementing the physical layer for each user) are hosted
by the DUs as software-defined virtual network functions. Unlike the current
literature, mainly focused on the characterization of the user rates under
unrestricted fronthaul communication and computation, in this work we
explicitly take into account the fronthaul topology, the limited fronthaul
communication capacity, and computation constraints at the DUs. In particular,
we systematically address the new problem of joint fronthaul load balancing and
allocation of the computation resource. As a consequence of our new
optimization framework, we present representative numerical results
highlighting the existence of an optimal number of quantization bits in the
analog-to-digital conversion at the RUs.Comment: 13 pages, 5 figures, submitted to IEEE Transactions on Wireless
Communication
Distributed Information Bottleneck for a Primitive Gaussian Diamond MIMO Channel
This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation
Cell-free Massive MIMO is considered as a promising technology for satisfying
the increasing number of users and high rate expectations in beyond-5G
networks. The key idea is to let many distributed access points (APs)
communicate with all users in the network, possibly by using joint coherent
signal processing. The aim of this paper is to provide the first comprehensive
analysis of this technology under different degrees of cooperation among the
APs. Particularly, the uplink spectral efficiencies of four different cell-free
implementations are analyzed, with spatially correlated fading and arbitrary
linear processing. It turns out that it is possible to outperform conventional
Cellular Massive MIMO and small cell networks by a wide margin, but only using
global or local minimum mean-square error (MMSE) combining. This is in sharp
contrast to the existing literature, which advocates for maximum-ratio
combining. Also, we show that a centralized implementation with optimal MMSE
processing not only maximizes the SE but largely reduces the fronthaul
signaling compared to the standard distributed approach. This makes it the
preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding
is also investigated and shown to bring negligible improvements.Comment: 14 pages, 6 figures, To appear in IEEE Transactions on Wireless
Communication
Making Cell-Free Massive MIMO Competitive with MMSE Processing and Centralized Implementation
Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements
Scalable Cell-Free Massive MIMO Systems
Imagine a coverage area with many wireless access points that cooperate to
jointly serve the users, instead of creating autonomous cells. Such a cell-free
network operation can potentially resolve many of the interference issues that
appear in current cellular networks. This ambition was previously called
Network MIMO (multiple-input multiple-output) and has recently reappeared under
the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits
of cell-free operation in a practically feasible way, with computational
complexity and fronthaul requirements that are scalable to large networks with
many users. We propose a new framework for scalable Cell-Free Massive MIMO
systems by exploiting the dynamic cooperation cluster concept from the Network
MIMO literature. We provide a novel algorithm for joint initial access, pilot
assignment, and cluster formation that is proved to be scalable. Moreover, we
adapt the standard channel estimation, precoding, and combining methods to
become scalable. A new uplink and downlink duality is proved and used to
heuristically design the precoding vectors on the basis of the combining
vectors. Interestingly, the proposed scalable precoding and combining
outperform conventional maximum ratio processing and also performs closely to
the best unscalable alternatives.Comment: To appear in IEEE Transactions on Communications, 14 pages, 6 figure