arXiv subjects

Hossein Asgharimoghaddam

Publications and source records attributed to Hossein Asgharimoghaddam.

4 recordsLinked to original sources

arXiv2020

Capacity Approaching Low Density Spreading in Uplink NOMA via Asymptotic Analysis

Low-density spreading non-orthogonal multiple-access (LDS-NOMA) is considered where $K$ single-antenna user-equipments (UEs) communicate with a base-station (BS) over $F$ fading sub-carriers. Each UE $k$ spreads its data symbols over $d_k<F$ sub-carriers. We aim to identify the LDS-code allocations that maximize the ergodic mutual information (EMI). The BS assigns resources solely based on pathlosses. Conducting analysis in the regime where $F$, $K$, and ${d_k,\forall k}$ converge to $+\infty$ at the same rate, we present EMI as a deterministic equivalent plus a residual term. The deterministic equivalent is a function of pathlosses and spreading codes, and the small residual term scales as $\mathcal{O}(\frac{1}{\min(d_k^2)})$. We formulate an optimization problem to get the set of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic EMI. This yields a simple resource allocation rule that facilitates the construction of desired LDS-codes via an efficient partitioning algorithm. The acquired LDS-codes additionally harness the small incremental gain inherent in the residual term, and thus, attain near-optimal values of EMI in the finite regime. While regular LDS-NOMA is found to be asymptotically optimal in symmetric models, an irregular spreading arises in generic asymmetric cases. The spectral efficiency enhancement relative to regular and random spreading is validated numerically.

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arXiv2019

Two-stage Beamformer Design via Deterministic Equivalents

Complexity reduction of optimal linear receiver is considered in a scenario where both the number of single-antenna user equipments (UEs) $K$ and base station (BS) antennas $N$ are large. Two-stage beamforming (TSB) greatly alleviates the high implementation complexity of large scale multiantenna receiver by concatenating a statistical outer beamformer (OBF) with an instantaneous inner beamformer (IBF) design. Using asymptotic large system analysis, we propose a novel TSB method that adjusts the dimensions of user-specific OBF matrices based on the projection of the optimal minimum mean square error (MMSE) vectors into the beam domain. The beam domain is first divided into $S$ narrow sectors such that each sector contains $D$ DFT beams. Then, so-called deterministic equivalents are computed for the amplitude-projection of the optimal MMSE vectors into each sector in asymptotic regime where $N$ , $K$ and $D$ grow large with a non-trivial ratio $N/K = C$ and $N/D = S$. Given the approximations for the sector-specific values, the structure and dimension of each UE specific OBF vector are optimized based on the statistical channel properties and the amount of overlap among users in angular domain. The numerical analysis shows that the attained SINR values closely follow the optimal MMSE receiver while the computational burden is greatly reduced.

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arXiv2019

Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems

In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between machine type communications (MTC) and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. If there is a large number of MTDs to choose from for transmission at each time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS.

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arXiv2018

Decentralizing Multicell Beamforming via Deterministic Equivalents

This paper focuses on developing a decentralized framework for coordinated minimum power beamforming wherein $L$ base stations (BSs), each equipped with $N$ antennas, serve $K$ single-antenna users with specific rate constraints. This is realized by considering user specific intercell interference (ICI) strength as the principal coupling parameter among BSs. First, explicit deterministic expressions for transmit powers are derived for spatially correlated channels in the asymptotic regime in which $N$ and $K$ grow large with a non-trivial ratio $K/N$. These asymptotic expressions are then used to compute approximations of the optimal ICI values that depend only on the channel statistics. By relying on the approximate ICI values as coordination parameters, a distributed non-iterative coordination algorithm, suitable for large networks with limited backhaul, is proposed. A heuristic algorithm is also proposed relaxing coordination requirements even further as it only needs pathloss values for non-local channels. The proposed algorithms satisfy the target rates for all users even when $N$ and $K$ are relatively small. Finally, the potential benefits of grouping users with similar statistics are investigated to further reduce the overhead and computational effort of the proposed solutions. Simulation results show that the proposed methods yield near-optimal performance.

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