arXiv subjects

T. G. Venkatesh

Publications and source records attributed to T. G. Venkatesh.

6 recordsLinked to original sources

arXiv2026

Integrated Sensing, Communication, and Computing for NR-V2X: A Cross-Layer Resource Allocation Framework Using Multi-Agent Reinforcement Learning

Integrated sensing, communication, and computation (ISCC) is emerging as a unified design paradigm for future vehicular networks that require joint environment perception, safety-critical information exchange, and latency-sensitive task processing. In New Radio Vehicle-to-Everything (NR-V2X) Mode 2, autonomous resource selection is performed through sensing-based semi-persistent scheduling (SB-SPS), which is effective for distributed communication resource reservation but does not explicitly consider sensing-resource demand, task-induced computation workload, and the additional latency introduced by mobile edge computing (MEC) offloading. This paper develops multi-agent proximal policy optimization-based SB-SPS (MAPPO-SPS), an ISCC-aware cross-layer scheduler that jointly adapts SB-SPS reservation, radio-resource partitioning, and overflow-driven computation-offloading decisions at control epochs. The scheduling problem is formulated as a cooperative partially observable Markov game and solved using MAPPO with centralized training and decentralized execution (CTDE). Simulation results show that MAPPO-SPS achieves a balanced tradeoff among CRLB-based sensing accuracy, packet reception ratio (PRR), effective throughput, energy consumption, and end-to-end delay.

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arXiv2022

A High Capacity Preamble Sequence for Random Access in Beyond 5G Networks: Design and Analysis

The widely used Zadoff-Chu sequence (ZC sequence) for random access preamble in 5G has limitations in terms of the total number of preambles generated, forcing the reuse of preambles. Hence, the probability of collision of preambles of UEs increase, resulting in the failure of random access procedure. To truly qualify beyond 5G networks as green technology, the preamble capacity should be increased without sacrificing energy efficiency. In this paper, we propose a new candidate preamble sequence called $mALL$ sequence using the concept of cover sequences to achieve higher preamble capacity without degrading the power efficiency and hence minimizing device's carbon footprint. We compare the performance of $mALL$ sequence with Zadoff-Chu sequence and other sequences in the literature, such as $mZC$ and $aZC$ sequences. We evaluate the performance of the preamble sequences in terms of periodic correlation, detection probability and the effect of diversity combining. Also, this paper explores the Peak to Average Power Ratio (PAPR) and Cubic Metric(CM) for these sequences, as these are essential parameters to evaluate energy efficiency. We show that the preamble capacity of the proposed $mALL$ sequence is $10^{4}$ times higher than that of legacy ZC sequence without any deterioration in the detection performance.

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arXiv2016

QoS Provisioning with Adaptive Backoff Algorithm for IEEE 802.11ac Under Multipacket Reception

Recent advances in physical layer communication techniques, enable receivers to decode multiple simultaneous transmissions. This technique is known as the multipacket reception (MPR). In this paper, we propose an enhancement to the IEEE 802.11ac EDCA protocol for channels supporting MPR for QoS provisioning. We show that in the case of MPR, in addition to CWmin, CWmax and AIFSN, we can use two more parameters namely (i) threshold and (ii) counter decrement value, that can offer service differentiation. The performance evaluation of the different metrics of the proposed protocol (throughput, delay, and jitter) is carried out using extensive simulations.

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arXiv2016

Optimal Channel Sensing Strategy for Cognitive Radio Networks with Heavy-Tailed Idle Times

In Cognitive Radio Network (CRN), the secondary user (SU) opportunistically access the wireless channels whenever they are free from the licensed / Primary User (PU). Even after occupying the channel, the SU has to sense the channel intermittently to detect reappearance of PU, so that it can stop its transmission and avoid interference to PU. Frequent channel sensing results in the degradation of SU's throughput whereas sparse sensing increases the interference experienced by the PU. Thus, optimal sensing interval policy plays a vital role in CRN. In the literature, optimal channel sensing strategy has been analysed for the case when the ON-OFF time distributions of PU are exponential. However, the analysis of recent spectrum measurement traces reveals that PU exhibits heavy-tailed idle times which can be approximated well with Hyper-exponential distribution (HED). In our work, we deduce the structure of optimal sensing interval policy for channels with HED OFF times through Markov Decision Process (MDP). We then use dynamic programming framework to derive sub-optimal sensing interval policies. A new Multishot sensing interval policy is proposed and it is compared with existing policies for its performance in terms of number of channel sensing and interference to PU.

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arXiv2016

Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times

We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary users (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; but not useful to evaluate the performance of CSA under realistic conditions. An in-depth analysis of independent spectrum measurement traces reveals that wireless channels have typically heavy-tailed PU OFF times. In this paper, we propose an extension to the Predictive CSA framework and its generalization for heavy tailed PU OFF time distribution, which represents realistic scenarios. In particular, we calculate the probability of channel being idle for hyper-exponential OFF times to use in CSA. We implement our proposed CSA framework in a wireless test-bed and comprehensively evaluate its performance by recreating the realistic PU channel occupancy patterns. The proposed CSA shows significant reduction in channel switches and energy consumption as compared to Predictive CSA which always assumes exponential PU ON-OFF times.Through our work, we show the impact of the PU channel occupancy pattern on the performance of CSA in multichannel CRN.

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arXiv2013

Adaptive Backoff Algorithm for IEEE 802.11 DCF under MPR Wireless Channels

As a result of the recent advances in physical (PHY) layer communication techniques, it is possible to receive multiple packets at the receiver concurrently. This capability of a receiver to decode multiple simultaneous transmissions is known as multi-packet reception (MPR). In this paper, we propose a simple Medium Access Control (MAC) protocol for an MPR wireless channel, where we modify the backoff procedure as a function of number of ongoing transmissions in the channel. Our protocol is backward compatible with the IEEE 802.11 DCF protocol. The performance analysis of the proposed protocol is carried out using extensive simulations and it is compared with some of the existing MPR MAC protocols. The proposed mechanism improves the throughput and delay performance of the IEEE 802.11 DCF.

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