Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2103.12970

Outage Probability Expressions for an IRS-Assisted System with and without Source-Destination Link for the Case of Quantized Phase Shifts in $κ-μ$ fading

Mavilla Charishma·Athira Subhash·Shashank Shekhar·Sheetal Kalyani
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this work, we study the outage probability (OP) at the destination of an intelligent reflecting surface (IRS) assisted communication system in a $κ-μ$ fading environment. A practical system model that takes into account the presence of phase error due to quantization at the IRS when a) source-destination (SD) link is present and b) SD link is absent is considered. First, an exact expression is derived, and then we derive three simple approximations for the OP using the following approaches: (i) uni-variate dimension reduction, (ii) moment matching and, (iii) Kullback-Leibler divergence minimization. The resulting expressions for OP are simple to evaluate and quite tight even in the tail region. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We also study the impact of the number of bits available for quantization, the position of IRS with respect to the source and destination and the number of IRS elements on the OP for systems with and without an SD link.

Explore related subjects

cs.ITmath.IT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mavilla Charishma, Athira Subhash, Shashank Shekhar, Sheetal Kalyani. 2021-07-31. Outage Probability Expressions for an IRS-Assisted System with and without Source-Destination Link for the Case of Quantized Phase Shifts in $κ-μ$ fading. https://arxiv.org/abs/2103.12970

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Expurgated Error Exponent is Not Universally Achievable

We study the universal attainability of the expurgated error exponent for discrete memoryless channels (DMCs). While the random-coding exponent is known to be universally attainable for every fixed input distribution via maximum mutual information (MMI) decoding for DMCs, it remains open whether the expurgated exponent can be attained universally. We show that this is not the case in general. Specifically, we construct a family of DMCs for which no single sequence of codes can attain the expurgated exponent simultaneously for all channels in the family, even at rate zero. In addition, for the same channel family, we show that MMI decoding fails to achieve the expurgated exponent for any channel in the family.

cs.IT↗
arXiv2026

Asymmetric Stream Allocation and Linear Decodability in MIMO Coded Caching

Coded caching (CC) can transform cache memory at network devices into an active communication resource and significantly enhance the Degrees of Freedom (DoF) of multi-input multi-output (MIMO) systems by jointly exploiting global caching and spatial multiplexing gains. Existing linearly decodable MIMO-CC designs, however, largely rely on symmetric stream allocation, where all scheduled users receive the same number of streams, which induces coarse DoF granularity and may leave spatial dimensions unused. This letter studies one-shot linearly decodable MIMO-CC delivery with arbitrary per-user stream allocations. We derive a sufficient stream-count decodability condition, expressed through per-user stream counts and multicast-codeword multiplicities, that generalizes the symmetric common-stream feasibility rule. Building on this condition, we develop a greedy multicast scheduling procedure with certified linear decodability, which redistributes coded multicast messages across transmission intervals to realize asymmetric stream allocations. Numerical results show that the proposed scheduler fills DoF-granularity gaps and improves finite-SNR symmetric rates over the state of the art.

cs.IT↗
arXiv2026

Universal Feature Selection with Noisy Observations and Weak Symmetry Conditions

This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate noisy observations as well as attribute structures that may exhibit directional preferences. We introduce the notion of weak spherical symmetry, quantified by second-moment distances, which allows controlled deviations from rotational invariance. Under this relaxed condition, we develop a universal feature selection framework based on the singular value decomposition of the canonical dependence matrix computed from noisy data. Our main result shows that the selected features achieve asymptotically optimal error exponents up to a residual term that depends on the symmetry deviation $δ$ and the noise levels $η_1, η_2$. When $δ, η_1, η_2$ are relatively small, our result recovers that of [5], thereby demonstrating that exact spherical symmetry is unnecessary. Overall, our findings highlight the robustness of the selection framework against second-moment deviations and observation noise, thereby broadening its applicability across diverse inference tasks and providing a theoretically grounded tool for universal feature selection in practical scenarios.

cs.IT↗