11 research outputs found
Remote Source Coding under Gaussian Noise : Dueling Roles of Power and Entropy Power
The distributed remote source coding (so-called CEO) problem is studied in
the case where the underlying source, not necessarily Gaussian, has finite
differential entropy and the observation noise is Gaussian. The main result is
a new lower bound for the sum-rate-distortion function under arbitrary
distortion measures. When specialized to the case of mean-squared error, it is
shown that the bound exactly mirrors a corresponding upper bound, except that
the upper bound has the source power (variance) whereas the lower bound has the
source entropy power. Bounds exhibiting this pleasing duality of power and
entropy power have been well known for direct and centralized source coding
since Shannon's work. While the bounds hold generally, their value is most
pronounced when interpreted as a function of the number of agents in the CEO
problem
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
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
On the Capacity of Cloud Radio Access Networks with Oblivious Relaying
International audienceWe study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying \`a-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and "noisy network coding", are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer (CEO) source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. We also discuss the suboptimality of separate decompression-decoding and the role of time-sharing. Furthermore, we study the related distributed information bottleneck problem and characterize optimal tradeoffs between rates (i.e., complexity) and information (i.e., accuracy) in the vector Gaussian case