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Register a new userTree-Algorithms with Multi-Packet Reception and Successive Interference Cancellation
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Added by
Yash Deshpande
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this paper, we perform a thorough analysis of tree-algorithms with multi-packet reception (MPR) and successive interference cancellation (SIC). We first derive the basic performance parameters, which are the expected length of the collision resolution interval and the normalized throughput, conditioned on the number of contending users. We then study their asymptotic behaviour, identifying an oscillatory component that amplifies with the increase in MPR. In the next step, we derive the throughput for the gated and windowed access, assuming Poisson arrivals. We show that for windowed access, the bound on maximum stable normalized throughput increases with the increase in MPR. We also analyze d-ary tree algorithms with MPR and SIC, showing deficiencies of the analysis performed in the seminal paper on tree-algorithms with SIC by Yu and Giannakis.
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Conventional power-domain non-orthogonal multiple access (NOMA) relies on precise power control, which requires real-time channel state information at transmitters. This requirement severely limits its application to future wireless communication systems. To address this problem, we consider NOMA without power allocation, where we exploit the random channel fading and opportunistically perform successive interference cancellation (SIC) detection. To mitigate the multi-user interference, we propose a random NOMA where users randomly transmit their data packets with a certain probability. Then a cross-slot SIC packet recovery scheme is proposed to recover transmitted data packets. We model the cross-slot SIC packet recovery as a Markov process, and provide a throughput analysis, based on which the sum rate is maximized by jointly optimizing the transmission probability and the encoding rate of users.
Dynamic successive cancellation flip (DSCF) decoding of polar codes is a powerful algorithm that can achieve the error correction performance of successive cancellation list (SCL) decoding, with a complexity that is close to that of successive cancellation (SC) decoding at practical signal-to-noise ratio (SNR) regimes. However, DSCF decoding requires costly transcendental computations which adversely affect its implementation complexity. In this paper, we first show that a direct application of common approximation schemes on the conventional DSCF decoding results in significant error-correction performance loss. We then introduce a training parameter and propose an approximation scheme which completely removes the need to perform transcendental computations in DSCF decoding, with almost no error-correction performance degradation.
We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed since the last received update was generated. In this paper, we model the source-destination link using queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.
Interference between nodes is a critical impairment in mobile ad hoc networks (MANETs). This paper studies the role of multiple antennas in mitigating such interference. Specifically, a network is studied in which receivers apply zero-forcing beamforming to cancel the strongest interferers. Assuming a network with Poisson distributed transmitters and independent Rayleigh fading channels, the transmission capacity is derived, which gives the maximum number of successful transmissions per unit area. Mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information (CSI). It is shown that, if each node cancels L interferers, the transmission capacity decreases as the outage probability to the power of 1/(L+1) as the outage probability vanishes. For fixed outage probability, as L grows, the transmission capacity increases as L to the power of (1-2/alpha) where alpha is the path-loss exponent. Moreover, CSI inaccuracy is shown to have no effect on the transmission capacity scaling as the outage probability vanishes, provided that the CSI training sequence has an appropriate length, which we derived. Numerical results suggest that canceling merely one interferer by each node increases the transmission capacity by an order of magnitude or more, even when the CSI is imperfect.
In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e.g. with the latest wireless technologies such as 5G and WiFi. To this end we introduce a general model where packet arrival probabilities are determined based on the received Signal-to-Interference-and-Noise Ratio and with two different receivers design schemes, one implementing standard multi-packet reception technique and one implementing Successive Interference Cancellation decoding algorithm in addition. Then we cast the power allocation problem as an optimization task where the mean error covariance at the remote estimator is minimized, while penalizing the mean transmission power consumption. For the infinite-horizon problem we show the existence of a stationary optimal policy, while for the finite-horizon case we derive some structural properties under the special scenario where the overall system to be estimated can be seen as a set of independent subsystems. Numerical simulations illustrate the improvement given by the proposed receivers over orthogonal schemes that schedules only one sensor transmission at a time in order to avoid interference.
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