107,684 research outputs found
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View
The grand objective of 5G wireless technology is to support three generic
services with vastly heterogeneous requirements: enhanced mobile broadband
(eMBB), massive machine-type communications (mMTC), and ultra-reliable
low-latency communications (URLLC). Service heterogeneity can be accommodated
by network slicing, through which each service is allocated resources to
provide performance guarantees and isolation from the other services. Slicing
of the Radio Access Network (RAN) is typically done by means of orthogonal
resource allocation among the services. This work studies the potential
advantages of allowing for non-orthogonal sharing of RAN resources in uplink
communications from a set of eMBB, mMTC and URLLC devices to a common base
station. The approach is referred to as Heterogeneous Non-Orthogonal Multiple
Access (H-NOMA), in contrast to the conventional NOMA techniques that involve
users with homogeneous requirements and hence can be investigated through a
standard multiple access channel. The study devises a communication-theoretic
model that accounts for the heterogeneous requirements and characteristics of
the three services. The concept of reliability diversity is introduced as a
design principle that leverages the different reliability requirements across
the services in order to ensure performance guarantees with non-orthogonal RAN
slicing. This study reveals that H-NOMA can lead, in some regimes, to
significant gains in terms of performance trade-offs among the three generic
services as compared to orthogonal slicing.Comment: Submitted to IEE
Recognizing well-parenthesized expressions in the streaming model
Motivated by a concrete problem and with the goal of understanding the sense
in which the complexity of streaming algorithms is related to the complexity of
formal languages, we investigate the problem Dyck(s) of checking matching
parentheses, with different types of parenthesis.
We present a one-pass randomized streaming algorithm for Dyck(2) with space
\Order(\sqrt{n}\log n), time per letter \polylog (n), and one-sided error.
We prove that this one-pass algorithm is optimal, up to a \polylog n factor,
even when two-sided error is allowed. For the lower bound, we prove a direct
sum result on hard instances by following the "information cost" approach, but
with a few twists. Indeed, we play a subtle game between public and private
coins. This mixture between public and private coins results from a balancing
act between the direct sum result and a combinatorial lower bound for the base
case.
Surprisingly, the space requirement shrinks drastically if we have access to
the input stream in reverse. We present a two-pass randomized streaming
algorithm for Dyck(2) with space \Order((\log n)^2), time \polylog (n) and
one-sided error, where the second pass is in the reverse direction. Both
algorithms can be extended to Dyck(s) since this problem is reducible to
Dyck(2) for a suitable notion of reduction in the streaming model.Comment: 20 pages, 5 figure
On the Complexity of Exact Maximum-Likelihood Decoding for Asymptotically Good Low Density Parity Check Codes: A New Perspective
The problem of exact maximum-likelihood (ML) decoding of general linear codes is well-known to be NP-hard. In this paper, we show that exact ML decoding of a class of asymptotically good low density parity check codes — expander codes — over binary symmetric channels (BSCs) is possible with an average-case polynomial complexity. This offers a new way of looking at the complexity issue of exact ML decoding for communication systems where the randomness in channel plays a fundamental central role. More precisely, for any bit-flipping probability p in a nontrivial range, there exists a rate region of non-zero support and a family of asymptotically good codes which achieve error probability exponentially decaying in coding length n while admitting exact ML decoding in average-case polynomial time. As p approaches zero, this rate region approaches the Shannon channel capacity region. Similar results can be extended to AWGN channels, suggesting it may be feasible to eliminate the error floor phenomenon associated with belief-propagation decoding of LDPC codes in the high SNR regime. The derivations are based on a hierarchy of ML certificate decoding algorithms adaptive to the channel realization. In this process, we propose an efficient O(n^2) new ML certificate algorithm based on the max-flow algorithm. Moreover, exact ML decoding of the considered class of codes constructed from LDPC codes with regular left degree, of which the considered expander codes are a special case, remains NP-hard; thus giving an interesting contrast between the worst-case and average-case complexities
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