Rahul Saraswat

Bio: Rahul Saraswat is an academic researcher. The author has contributed to research in topics: Spectral efficiency & Telecommunications link. The author has an hindex of 1, co-authored 1 publications receiving 102 citations.

Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems

Abstract: This paper addresses energy-efficient design for uplink multiuser SIMO frameworks with marked channel state data (CSD) at the base station (BS). Since the CSD at the BS is constantly unreliable because of the channel estimation error and delay, the imperfectness of the CSD needs to be considered in practical framework plan. It causes interuser impedance at the zero-forcing (ZF) receiver and makes it hard to acquire the universally ideal power distribution that expands the energy efficiency (EE). Consequently, we propose a non-helpful energy efficiency uplink power control game, where every client egotistically overhauls its own uplink power. The proposed framework is utilized to examine the execution of expansive scale MU-MIMO framework by changing the quantity of BS receivers, clients and recognize the effect on limit, spectral efficiency, aggregate rate, energy efficiency and so on. The proposed work is planned & analyzed proficiently procedure/system for improvement of energy efficiency, throughput and so on.

Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means

Abstract: This paper investigates the uplink achievable rates of massive multiple-input multiple-output (MIMO) antenna systems in Ricean fading channels, using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect and imperfect channel state information (CSI). In contrast to previous relevant works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. We derive tractable expressions for the achievable uplink rate in the large-antenna limit, along with approximating results that hold for any finite number of antennas. Based on these analytical results, we obtain the scaling law that the users' transmit power should satisfy, while maintaining a desirable quality of service. In particular, it is found that regardless of the Ricean K-factor, in the case of perfect CSI, the approximations converge to the same constant value as the exact results, as the number of base station antennas,, grows large, while the transmit power of each user can be scaled down proportionally to. If CSI is estimated with uncertainty, the same result holds true but only when the Ricean K-factor is non-zero. Otherwise, if the channel experiences Rayleigh fading, we can only cut the transmit power of each user proportionally to 1 root M. In addition, we show that with an increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.

Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

Abstract: The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Abstract: Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose-to the best of our knowledge-the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.

Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems

Abstract: In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm designs the pilot beam pattern sequentially by exploiting the properties of Kalman filtering and the associated prediction error covariance matrices and also the channel statistics such as spatial and temporal channel correlation. The resulting design generates a sequentially-optimal sequence of pilot beam patterns with low complexity for a given set of system parameters. Numerical results show the effectiveness of the proposed algorithm.

Making smart use of excess antennas: Massive MIMO, small cells, and TDD

Abstract: In this paper, we present a vision beyond the conventional Long Term Evolution Fourth Generation (LTE-4G) evolution path and suggest that time division duplexing (TDD) could be a key enabler for a new heterogeneous network architecture with the potential to provide ubiquitous coverage and unprecedented spectral area efficiencies. This architecture is based on a cochannel deployment of macro base stations (BSs) with very large antenna arrays and a secondary tier of small cells (SCs) with a few antennas each. Both tiers employ a TDD protocol in a synchronized fashion. The resulting channel reciprocity enables not only the estimation of large-dimensional channels at the BSs, but also an implicit coordination between both tiers without the need to exchange user data or channel state information (CSI) over the backhaul. In particular, during the uplink (UL), the BSs and SCs can locally estimate the dominant interference sub-space. This knowledge can be leveraged for downlink (DL) precoding to reduce intra- and inter-tier interference. In other words, the BSs and SCs “sacrifice” some of their degrees of freedom for interference rejection. Our simulation results demonstrate that the proposed architecture and precoding scheme can achieve a very attractive rate region compared to several baseline scenarios. For example, with 100 antennas at each BS and four antennas at each SC, we observe an aggregate area throughput of 7.63 Gb/s/km2 (DL) and 8.93 Gb/s/km2 (UL) on a 20 MHz band shared by about 100 mobile devices.