arXiv subjects

Sudarshan Mukherjee

Publications and source records attributed to Sudarshan Mukherjee.

15 recordsLinked to original sources

arXiv2020

Base Station Coordination Scheme for Multi-tier Ultra-dense Networks

In this paper, we consider a relative received link power (RRLP)-based coordinated multi-point (CoMP) joint transmission (JT) in the multi-tier ultra-dense networks (UDN). In this CoMP scheme, we identify the cooperating base stations (BSs) by comparing the average received link power (ARLP) of the neighbouring BSs with respect to the BS having the strongest ARLP (i.e., the main link BS) to a user. To analyze the performance of this CoMP scheme in the downlink multi-tier UDN, we first approximate the received signal power distribution, and derive the coverage probability using stochastic geometry. After revisiting the area spectral efficiency (ASE) to make it more suitable for CoMP transmission in UDN, we also analyze the ASE and the network energy efficiency (NEE). Using simulations, we validate the derived coverage probability, and investigate the CoMP performance in multi-tier UDN. Our simulations show that the RRLP-based CoMP scheme can outperform the fixed number of strongest BS-based CoMP scheme in the high BS density regime. Our study of the NEE performance reveals that not only the RRLP-based CoMP scheme is more efficient than conventional non-CoMP transmission scenario, but also its NEE performance improves with the average number of cooperating BSs.

cs.IT↗

arXiv2020

Performance Analysis of Cell Free Massive MIMO systems in LoS/ NLoS Channels

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and their associated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes.

cs.IT↗

arXiv2019

Edge Computing-Enabled Cell-Free Massive MIMO Systems

Mobile edge computing (MEC) has been introduced to provide additional computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell-free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the cloud computing capability. After deriving successful communication and computing probabilities using stochastic geometry and queueing theory, we present the successful edge computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.

cs.IT↗

arXiv2018

Timing Advance Estimation and Beamforming of Random Access Response in Crowded TDD Massive MIMO Systems

Timing advance (TA) estimation at the base station (BS) and reliable decoding of random access response (RAR) at the users are the most important steps in the initial random access (RA) procedure. However, due to the limited availability of physical resources dedicated for RA, successful completion of RA requests would become increasingly difficult in high user density scenarios, due to contention among users requesting RA. In this paper, we propose to use the large antenna array at the massive MIMO BS to jointly group RA requests from different users using the same RA preamble. We then beamform the common RAR of each detected user group onto the same frequency resource, in such a way that most users in the group can reliably decode the RAR. The proposed RAR beamforming therefore automatically resolves the problem of collision between multiple RA requests on the same RA preamble, which reduces the RA latency significantly as compared to LTE. Analysis and simulations also reveal that for a fixed desired SINR of the received RAR, both the required per-user RA preamble transmission power and the total RAR beamforming power can be decreased roughly by 1.5 dB with every doubling in the number of BS antennas.

cs.IT↗

arXiv2016

Information Theoretic Performance of Periodogram-based CFO Estimation in Massive MU-MIMO Systems

In this paper, we study the information theoretic performance of the modified time-reversal maximum ratio combining (TR-MRC) receiver (presented in [9]) with the spatially averaged periodogram-based carrier frequency offset (CFO) estimator (proposed in [7]) in multi-user massive MIMO systems. Our analysis shows that an $\mathcal{O}(\sqrt{M})$ array gain is achieved with this periodogram-based CFO estimator, which is same as the array gain achieved in the ideal/zero CFO scenario ($M$ is the number of base station antennas). Information theoretic performance comparison with the correlation-based CFO estimator for massive MIMO systems (proposed in [6]) reveals that this periodogram-based CFO estimator is more energy efficient in slowly time-varying channels.

cs.IT↗

arXiv2016

Impact of Frequency Selectivity on the Information Rate Performance of CFO Impaired Single-Carrier Massive MU-MIMO Uplink

In this paper, we study the impact of frequency-selectivity on the gap between the required per-user transmit power in the residual CFO scenario (i.e. after CFO estimation/compensation at the base-station (BS) from [6]) and that in the ideal/zero CFO scenario, for a fixed per-user information rate, in single-carrier massive MU-MIMO uplink systems with the TR-MRC receiver. Information theoretic analysis reveals that this gap decreases with increasing frequency-selectivity of the channel. Also, in the residual CFO scenario, an $\mathcal{O}(\sqrt{M})$ array gain is still achievable ($M$ is the number of BS antennas) in frequency-selective channels with imperfect channel estimates.

cs.IT↗

arXiv2016

Information Rate Performance of Massive MU-MIMO Uplink with Constant Envelope Pilot-based Frequency Synchronization

In this paper, we consider a constant envelope (CE) pilot-based low-complexity technique for frequency synchronization in multi-user massive MIMO systems. Study of the complexity-performance trade-off shows that this CE-pilot-based technique provides better MSE performance when compared to existing low-complexity high-PAPR pilot-based CFO (carrier frequency offset) estimator. Numerical study of the information rate performance of the TR-MRC receiver in imperfect CSI scenario with this CE-pilot based CFO estimator shows that it is more energy-and-spectrally efficient than existing low-complexity CFO estimator in massive MIMO systems. It is also observed that with this CE-pilot based CFO estimation, an $\mathcal{O}(\sqrt{M})$ array gain is achievable.

cs.IT↗

arXiv2016

Constant Envelope Pilot-Based Low-Complexity CFO Estimation in Massive MU-MIMO Systems

In this paper we consider a constant envelope pilot signal based carrier frequency offset (CFO) estimation in massive multiple-input multiple-output (MIMO) systems. The proposed algorithm performs spatial averaging on the periodogram of the received pilots across the base station (BS) antennas. Our study reveals that the proposed algorithm has complexity only linear in $M$ (the number of BS antennas). Further our analysis and numerical simulations also reveal that with fixed number of users and a fixed pilot length, the minimum required transmit pilot power decreases as $\frac{1}{\sqrt{M}}$ with increasing $M$, while maintaining a fixed desired mean squared error (MSE) of CFO estimation.

cs.IT↗

arXiv2016

How Much Bandpass Filtering is Required in Massive MIMO Basestations?

In this paper, we study the impact of aliased out-of-band interference signals on the information sum-rate of the maximum ratio combining receiver in massive multiple-input multiple-output (MIMO) uplink, with both perfect and imperfect channel estimates, in order to determine the required out-of-band attenuation in RF bandpass filters (BPFs). With imperfect channel estimates, our study reveals that as the number of base-station (BS) antennas ($M$) increases, the required attenuation at the BPFs increases as $\mathcal{O}(\sqrt{M})$ with $M \to \infty$, provided the desired information sum-rate (both in the presence and in the absence of AOOBIs (aliased out-of-band interferers)) remains fixed. This implies a practical limit on the number of BS antennas due to the increase in BPF design complexity and power consumption with increasing $M$.

cs.IT↗

arXiv2016

Impact of Underlaid Multi-antenna D2D on Cellular Downlink in Massive MIMO Systems

In this paper, we consider a massive multiple-input multiple-output (MIMO) downlink system underlaid with a network of multi-antenna D2D user equipments (UEs). Each D2D transmitter (Tx) uses all its antennas to beamform information towards its desired D2D receiver, which uses only a single antenna for reception. While beamforming at the D2D Tx reduces D2D interference to the neighbouring cellular UEs (CUEs), the cellular-to-D2D interference is also negligible due to highly directional beamforming at the massive MIMO base station. For the above proposed system, we analyze the average per-user spectral efficiency (SE) of CUEs ($R^{c,d}$) as a function of the D2D area spectral efficiency (ASE). Our analysis reveals that for a fixed D2D ASE ($R_0^{(d)}$) and fixed number of D2D antennas ($N$), with increasing density of D2D Txs ($λ$), $R^{c,d}$ increases (for sufficiently large $λ$) and approaches a fundamental limit $R_{\infty}^{c,d}$ as $λ\to \infty$. Also, $R_{\infty}^{c,d}$ depends on $R_{0}^{(d)}$ and $N$, only through the ratio $\frac{R_{0}^{(d)}}{N-1}$, i.e, for a given fundamental limit $R_{\infty}^{c,d}$, the D2D ASE can be approximately doubled with every doubling in $N$.

cs.IT↗

arXiv2015

Impact of CFO Estimation on the Performance of ZF Receiver in Massive MU-MIMO Systems

In this paper, we study the impact of carrier frequency offset (CFO) estimation/compensation on the information rate performance of the zero-forcing (ZF) receiver in the uplink of a multi-user massive multiple-input multiple-output (MIMO) system. Analysis of the derived closed-form expression of the per-user information rate reveals that with increasing number of BS antennas $M$, an $\mathcal{O}(\sqrt{M})$ array gain is achievable, which is same as that achieved in the ideal zero CFO scenario. Also it is observed that compared to the ideal zero CFO case, the performance degradation in the presence of residual CFO (after CFO compensation) is the same for both ZF and MRC.

cs.IT↗

arXiv2015

Low-Complexity CFO Estimation for Multi-User Massive MIMO Systems

Low-complexity carrier frequency offset (CFO) estimation and compensation in multi-user massive multiple-input multiple-output (MIMO) systems is a challenging problem. The existing CFO estimation algorithms incur tremendous increase in complexity with increasing number of base station (BS) antennas, $M$ and number of user terminals (UTs) $K$ (i.e. massive MIMO regime). In this paper, we address this problem by proposing a novel low-complexity algorithm for CFO estimation which uses the pilot signal received at the BS during special uplink slots. The total per-channel use complexity of the proposed algorithm increases only linearly with increasing $M$ and is independent of $K$. Analysis reveals that the CFO estimation accuracy can be considerably improved by increasing $M$ and $K$ (i.e. massive MIMO regime). For example, for a fixed $K$ and a fixed training length, the required per-user radiated power during uplink training decreases as $\frac{1}{\sqrt{M}}$ with increasing $M$.

cs.IT↗

arXiv2014

Constant-Envelope Precoding with Time-Variation Constraint on the Transmitted Phase Angles

We consider downlink precoding in a frequency-selective multi-user massive MIMO system with highly efficient but non-linear power amplifiers at the base station (BS). A low-complexity precoding algorithm is proposed, which generates constant-envelope (CE) transmit signals for each BS antenna. To avoid large variations in the phase angle transmitted from each antenna, the difference of the phase angles transmitted in consecutive channel uses is limited to $[-απ\,,\, απ]$ for a fixed $0 < α\leq 1$. To achieve a desired per-user information rate, the extra total transmit power required under the time variation constraint when compared to the special case of no time variation constraint (i.e., $α=1$), is small for many practical values of $α$. In a i.i.d. Rayleigh fading channel with $80$ BS antennas, $5$ single-antenna users and a desired per-user information rate of $1$ bit-per-channel-use, the extra total transmit power required is less than $2.0$ dB when $α= 1/2$.

cs.IT↗

arXiv2014

Energy-Spectral Efficiency Trade-off for a Massive SU-MIMO System with Transceiver Power Consumption

We consider a single user (SU) massive MIMO system with multiple antennas at the transmitter (base station) and a single antenna at the user terminal (UT). Taking transceiver power consumption into consideration, for a given spectral efficiency (SE) we maximize the energy efficiency (EE) as a function of the number of base station (BS) antennas $M$, resulting in a closed-form expression for the optimal SE-EE trade-off. It is observed that in contrast to the classical SE-EE trade-off (which considers only the radiated power), with transceiver power consumption taken into account, the EE increases with increasing SE when SE is sufficiently small. Further, for a fixed SE we analyze the impact of varying cell size (i.e., equivalently average channel gain $G_c$) on the optimal EE. We show the interesting result that for sufficiently small $G_c$, the optimal EE decreases as $\mathcal{O}(\sqrt{G_c})$ with decreasing $G_c$. Our analysis also reveals that for sufficiently small SE (or large $G_c$), the EE is insensitive to the power amplifier efficiency.

cs.IT↗

arXiv2014

On the Energy-Spectral Efficiency Trade-off of the MRC Receiver in Massive MIMO Systems with Transceiver Power Consumption

We consider the uplink of a multiuser massive MIMO system wherein a base station (BS) having $M$ antennas communicates coherently with $K$ single antenna user terminals (UTs). We study the energy efficiency of this system while taking the transceiver power consumption at the UTs and the BS into consideration. For a given spectral efficiency $R$ and fixed transceiver power consumption parameters, we propose and analyze the problem of maximizing the energy efficiency as a function of $(M,K)$. For the maximum ratio combining (MRC) detector at the BS we show that with increasing $R$, $(M,K)$ can be adaptively increased in such a way that the energy efficiency converges to a positive constant as $R \rightarrow \infty$ ($(M,K)$ is increased in such a way that a constant per-user spectral efficiency $R/K$ is maintained). This is in contrast to the fixed $(M,K)$ scenario where the energy efficiency is known to converge to zero as $R \rightarrow \infty$. We also observe that for large $R$, the optimal $(M,K)$ maximizing the energy efficiency is such that, the total power consumed by the power amplifiers (PA) in all the $K$ UTs is a small fraction of the total system power consumption.

cs.IT↗