Kala Praveen Bagadi

Bio: Kala Praveen Bagadi is an academic researcher from VIT University. The author has contributed to research in topic(s): Multiuser detection & Orthogonal frequency-division multiplexing. The author has an hindex of 10, co-authored 34 publication(s) receiving 273 citation(s). Previous affiliations of Kala Praveen Bagadi include National Institute of Technology, Rourkela.

MIMO-OFDM Channel Estimation Using Pilot Carries

Abstract: A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper. The estimation of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation. The performance of MIMO OFDM and SISO OFDM are evaluated on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level. Further enhancement of performance can be achieved through maximum diversity Space Time Block Coding (STBC) and Maximum Likelihood Detection at transmission and reception ends respectively. MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.

Modelling of IoT Traffic and Its Impact on LoRaWAN

Abstract: The recent growth of IoT use-cases in a wide array of industrial, utility and environmental applications has necessitated the need for connectivity solutions with diverse requirements. Connectivity through BLE, Zigbee and 6LoPAN are examples of short-range IoT deployments. But to provide connectivity to a high density of devices over larger coverage areas, Low-Power Wide-Area Network (LPWAN) technologies in both licensed as well as unlicensed bands have been considered. In this paper, we consider modelling the traffic from IoT devices connected through LPWAN technologies. Due to diverse applications of IoT, it is not trivial to have a single traffic model to represent all of them, but the traffic can be broadly classified as either periodic, event-triggered, or a combination of both. We evaluate the performance of LoRaWAN, one such LPWAN technology, in the presence of a hybrid of both traffic types, where the event propagates spatially over time. In a practical deployment of sensor based IoT devices, the devices are usually densely deployed to ensure sufficient & reliable measurement. Thereby, when an event occurs, they exhibit spatial & temporal correlation in their traffic rate due to the natural phenomena of the metric they measure. We use the CMMPP model to represent such characteristic traffic from independent IoT devices triggered by an event. The characteristics of LoRa, the physical layer of LoRaWAN, is abstracted based on required signal strength and interference thresholds for different modulation parameters. Through system simulations, we demonstrate that there is a significant performance hit in LoRaWAN based networks, during the occurrence of events. In particular, using the packet delivery rate (PDR) as the metric, we found that while the system was able to handle regular updates from the devices with a PDR > 80%, event-driven traffic nearly impaired the network causing the PDR to drop below 10%.

Neural network-based adaptive multiuser detection schemes in SDMA–OFDM system for wireless application

Abstract: Neural network applications in adaptive multiuser detection (MUD) schemes are suggested here in the context of space division multiple access–orthogonal frequency division multiplexing system. In this paper, various neural network (NN) models like feed forward network (FFN), recurrent neural network (RNN) and radial basis function network (RBFN) are adopted for MUD. MUD using NN models outperforms other existing schemes like genetic algorithm--assisted minimum bit error rate (MBER) and minimum mean square error MUDs in terms of BER performance and convergence speed. Among these NN models, the FNN MUD performs efficiently as RNN in full load scenario, where the number of users is equal to number of receiving antennas. In overload scenario, where the number of users is more than the number of receiving antennas, the FNN MUD performs better than RNN MUD. Further, the RBFN MUD provides a significant enhancement in performance over FNN and RNN MUDs under both overload and full load scenarios because of its better classification ability due to Gaussian nonlinearity. Extensive simulation analysis considering Stanford University Interim channel models applied for fixed wireless applications shows improvement in convergence speed and BER performance of the proposed NN-based MUD algorithms.

Recent trends in multiuser detection techniques for SDMA-OFDM communication system

Abstract: The space division multiple access-orthogonal frequency division multiplexing (SDMA-OFDM) technique is emerged as a most competitive technology for future wireless communication system as it can provide high spectral efficiency and resistance from inter symbol interference (ISI). The SDMA like multiple access techniques are prone to multiple access interference (MAI) because multiple users transmit their data simultaneously. Such a receiver requires appropriate multiuser detection (MUD) scheme to detect individual user's signals correctly by mitigating MAI. Further, due to non-linear behaviour of wireless channel, the signals at SDMA receiver often become linearly non-separable. As a result, MUD becomes a challenging multidimensional optimization problem. Considering these challenges, this paper reviews various MUD techniques for SDMA-OFDM system.

Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

Abstract: The space division multiple access–orthogonal frequency division multiplexing (SDMA–OFDM) wireless system has become very popular owing high spectral efficiency and high load capability. The optimal maximum likelihood multiuser detection (MUD) technique suffers from high computational complexity. On the other hand the linear minimum mean square error (MMSE) MUD techniques yields poor performance and also fails to detect users in overload scenario, where the number of users are more than that of number of receiving antennas. By contrast, the differential evolution algorithm (DEA) aided minimum symbol error rate (MSER) MUD can sustain in overload scenario as it can directly minimizes probability of error rather than mean square error. However, all these classical techniques are still complex as these do channel estimation and multiuser detection sequentially. In this paper, complex multi layer perceptron (CMLP) neural network model is suggested for MUD in SDMA–OFDM system as it do both channel approximation and MUD simultaneously. Simulation results prove that the CMLP aided MUD performs better than the MMSE and MSER techniques in terms of enhanced bit error rate performance with low computational complexity.

OFDM for Optical Communications

Abstract: Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

Analysis and Performance Optimization of LoRa Networks With Time and Antenna Diversity

Abstract: Low power wide area network (LPWAN) technologies are increasingly catching the attention of the Internet-of-Things market and have brought the need for reliable knowledge about the performance of such networks. This paper is concerned with the performance and scalability of LoRa networks, a leading LPWAN technology. Several recently published articles have analyzed the ability of LoRa networks to scale, i.e., their ability to support increased traffic and number of nodes. This paper proposes to employ message replication and gateways with multiple receive antennas to achieve, respectively, time and spatial diversity. The paper presents the proposed schemes and evaluates them through theoretical analysis and computer simulations. Results show that LoRa networks are highly sensitive to the increase in user and traffic density, but both message replication and multiple antennas can enhance performance. Message replication has an optimum number of message copies for each network configuration, and its utilization is more beneficial in low-density networks, while the use of multiple receive antennas at the gateway is always beneficial.

A Survey on LoRaWAN Architecture, Protocol and Technologies

Abstract: Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.