arXiv subjects

Alexei Ashikhmin

Publications and source records attributed to Alexei Ashikhmin.

At least 37 records · Page 2Linked to original sources

arXiv2020

Cell-Free Massive MIMO with Nonorthogonal Pilots for Internet of Things

We consider Internet of Things (IoT) organized on the principles of cell-free massive MIMO. Since the number of things is very large, orthogonal pilots cannot be assigned to all of them even if the things are stationary. This results in an unavoidable pilot contamination problem, worsened by the fact that, for IoT, since the things are operating at very low transmit power. To mitigate this problem and achieve a high throughput, we use cell-free systems with optimal linear minimum mean squared error (LMMSE) channel estimation, while traditionally simple suboptimal estimators have been used in such systems. We further derive the analytical uplink and downlink signal-to-interference-plus-noise ratio (SINR) expressions for this scenario, which depends only on large scale fading coefficients. This allows us to design new power control algorithms that require only infrequent transmit power adaptation. Simulation results show a 40% improvement in uplink and downlink throughputs and 95% in energy efficiency over existing cell-free wireless systems and at least a three-fold uplink improvement over known IoT systems based on small-cell systems.

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arXiv2020

Long-term scheduling and power control for wirelessly powered cell-free IoT

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet of Things (IoT) network which consists of distributed access points (APs) and large number of sensors. In each time slot, a subset of sensors are scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closedform expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time average achievable rate among all sensors, while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time average achievable rate is boosted significantly by the proposed scheme compare with the simple greedy transmission scheme.

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arXiv2020

Wirelessly Powered Cell-free IoT: Analysis and Optimization

In this paper, we propose a wirelessly powered Internet of Things (IoT) system based on the cell-free massive MIMO technology. In such a system, during the downlink phase, the sensors harvest radio-frequency (RF) energy emitted by the distributed access points (APs). During the uplink phase, sensors transmit data to the APs using the harvested energy. Collocated massive MIMO and small-cell IoT can be treated as special cases of cell-free IoT. We derive the tight closed-form lower bound on the amount of harvested energy, and the closed-form expression of SINR as the metrics of power transfer and data transmission, respectively. To improve the energy efficiency, we jointly optimize the uplink and downlink power control coefficients to minimize the total transmit energy consumption while meeting the target SINRs. Extended simulation results show that cell-free IoT outperforms collocated massive MIMO and small-cell IoT both in terms of the per user throughput for uplink, and the amount of energy harvested for downlink. Moreover, significant gains can be achieved by the proposed joint power control in terms of both per user throughput and energy consumption.

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arXiv2019

Quantum Data-Syndrome Codes

Performing active quantum error correction to protect fragile quantum states highly depends on the correctness of error information--error syndromes. To obtain reliable error syndromes using imperfect physical circuits, we propose the idea of quantum data-syndrome (DS) codes that are capable of correcting both data qubits and syndrome bits errors. We study fundamental properties of quantum DS codes, including split weight enumerators, generalized MacWilliams identities, and linear programming bounds. In particular, we derive Singleton and Hamming-type upper bounds on degenerate quantum DS codes. Then we study random DS codes and show that random DS codes with a relatively small additional syndrome measurements achieve the Gilbert-Varshamov bound of stabilizer codes. Constructions of quantum DS codes are also discussed. A family of quantum DS codes is based on classical linear block codes, called syndrome measurement codes, so that syndrome bits are encoded in additional redundant stabilizer measurements. Another family of quantum DS codes is CSS-type quantum DS codes based on classical cyclic codes, and this includes the Steane code and the quantum Golay code.

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arXiv2019

Quantum convolutional data-syndrome codes

We consider performance of a simple quantum convolutional code in a fault-tolerant regime using several syndrome measurement/decoding strategies and three different error models, including the circuit model.

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arXiv2018

Uplink Massive MIMO for Channels with Spatial Correlation

A massive MIMO system entails a large number of base station antennas M serving a much smaller number of users. This leads to large gains in spectral and energy efficiency compared with other technologies. As the number of antennas M grows, the performance of such systems gets limited by pilot contamination interference. Large Scale Fading Precoding/Postcoding (LSFP) was proposed in literature for mitigation of pilot contamination. It was shown recently that in channels without spatial correlation (uncorrelated base station antennas) LSFP leads to large spectral-efficiency gains. Also, recently, it was observed that if a channel has spatial correlation, then one can use this correlation to drastically reduce the pilot contamination interference in the asymptotic regime as M tends to infinity. In this work, we analyze the performance of Uplink (UL) transmission of massive MIMO systems with finitely many antennas M for channels with spatial correlation. We extend the idea of LSFP to correlated channel models and derive SINR expressions that depend only on slow fading channel components for such systems with and without LSFP. These simple expressions lead us to simple algorithms for transmit power optimization. As a result, we obtain a multi-fold increase in data transmission rates.

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arXiv2017

Massive BLAST: An Architecture for Realizing Ultra-High Data Rates for Large-Scale MIMO

A detection scheme for uplink massive MIMO, dubbed massive-BLAST or M-BLAST, is proposed. The derived algorithm is an enhancement of the well-known soft parallel interference cancellation. Using computer simulations in massive MIMO application scenarios, M-BLAST is shown to yield a substantially better error performance with reduced complexity, compared to the benchmark alternative of a one-shot linear detector, as well as the original sequential V-BLAST. Hence, M-BLAST may serve as a computationally efficient means to exploit the large number of antennas in massive MIMO.

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arXiv2017

Sectoring in Multi-cell Massive MIMO Systems

In this paper, the downlink of a typical massive MIMO system is studied when each base station is composed of three antenna arrays with directional antenna elements serving 120 degrees of the two-dimensional space. A lower bound for the achievable rate is provided. Furthermore, a power optimization problem is formulated and as a result, centralized and decentralized power allocation schemes are proposed. The simulation results reveal that using directional antennas at base stations along with sectoring can lead to a notable increase in the achievable rates by increasing the received signal power and decreasing 'pilot contamination' interference in multicell massive MIMO systems. Moreover, it is shown that using optimized power allocation can increase 0.95-likely rate in the system significantly.

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arXiv2017

Fidelity Lower Bounds for Stabilizer and CSS Quantum Codes

In this paper we estimate the fidelity of stabilizer and CSS codes. First, we derive a lower bound on the fidelity of a stabilizer code via its quantum enumerator. Next, we find the average quantum enumerators of the ensembles of finite length stabilizer and CSS codes. We use the average quantum enumerators for obtaining lower bounds on the average fidelity of these ensembles. We further improve the fidelity bounds by estimating the quantum enumerators of expurgated ensembles of stabilizer and CSS codes. Finally, we derive fidelity bounds in the asymptotic regime when the code length tends to infinity. These results tell us which code rate we can afford for achieving a target fidelity with codes of a given length. The results also show that in symmetric depolarizing channel a typical stabilizer code has better performance, in terms of fidelity and code rate, compared with a typical CSS codes, and that balanced CSS codes significantly outperform other CSS codes. Asymptotic results demonstrate that CSS codes have a fundamental performance loss compared to stabilizer codes.

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arXiv2017

Performance of Cell-Free Massive MIMO Systems with MMSE and LSFD Receivers

Cell-Free Massive MIMO comprises a large number of distributed single-antenna access points (APs) serving a much smaller number of users. There is no partitioning into cells and each user is served by all APs. In this paper, the uplink performance of cell-free systems with minimum mean squared error (MMSE) and large scale fading decoding (LSFD) receivers is investigated. The main idea of LSFD receiver is to maximize achievable throughput using only large scale fading coefficients between APs and users. Capacity lower bounds for MMSE and LSFD receivers are derived. An asymptotic approximation for signal-to-interference-plus-noise ratio (SINR) of MMSE receiver is derived as a function of large scale fading coefficients only. The obtained approximation is accurate even for a small number of antennas. MMSE and LSFD receivers demonstrate five-fold and two-fold gains respectively over matched filter (MF) receiver in terms of 5%-outage rate.

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arXiv2017

Uplink Interference Reduction in Large Scale Antenna Systems

A massive MIMO system entails a large number (tens or hundreds) of base station antennas serving a much smaller number of terminals. These systems demonstrate large gains in spectral and energy efficiency compared with conventional MIMO technology. As the number of antennas grows, the performance of a massive MIMO system gets limited by the interference caused by pilot contamination. Earlier A. Ashikhmin and T. Marzetta proposed (under the name of Pilot Contamination Precoding) Large Scale Fading Precoding (LSFP) and Decoding (LSFD) based on limited cooperation between base stations. They showed that Zero-Forcing LSFP and LSFD eliminate pilot contamination entirely and lead to an infinite throughput as the number of antennas grows. In this paper, we focus on the uplink and show that even in the case of a finite number of base station antennas, LSFD yields a very large performance gain. In particular, one of our algorithms gives a more than 140 fold increase in the 5% outage data transmission rate! We show that the performance can be improved further by optimizing the transmission powers of the users. Finally, we present decentralized LSFD that requires limited cooperation only between neighboring cells.

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arXiv2017

Cell-Free Massive MIMO versus Small Cells

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated.

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arXiv2016

Physical Layer Security in Massive MIMO

We consider a single-cell downlink massive MIMO communication in the presence of an adversary capable of jamming and eavesdropping simultaneously. We show that massive MIMO communication is naturally resilient to no training-phase jamming attack in which the adversary jams only the data communication and eavesdrops both the data communication and the training. Specifically, we show that the secure degrees of freedom (DoF) attained in the presence of such an attack is identical to the maximum DoF attained under no attack. Further, we evaluate the number of antennas that base station (BS) requires in order to establish information theoretic security without even a need for Wyner encoding. Next, we show that things are completely different once the adversary starts jamming the training phase. Specifically, we consider an attack, called training-phase jamming in which the adversary jams and eavesdrops both the training and the data communication. We show that under such an attack, the maximum secure DoF is equal to zero. Furthermore, the maximum achievable rates of users vanish even in the asymptotic regime in the number of BS antennas. To counter this attack, we develop a defense strategy in which we use a secret key to encrypt the pilot sequence assignments to hide them from the adversary, rather than encrypt the data. We show that, if the cardinality of the set of pilot signals are scaled appropriately, hiding the pilot signal assignments from the adversary enables the users to achieve secure DoF, identical to the maximum achievable DoF under no attack. Finally, we discuss how computational cryptography is a legitimate candidate to hide the pilot signal assignments. Indeed, while information theoretic security is not achieved with cryptography, the computational power necessary for the adversary to achieve a non-zero mutual information leakage rate goes to infinity.

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arXiv2016

Correction of Data and Syndrome Errors by Stabilizer Codes

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arXiv2015

Cell-Free Massive MIMO: Uniformly Great Service For Everyone

We consider the downlink of Cell-Free Massive MIMO systems, where a very large number of distributed access points (APs) simultaneously serve a much smaller number of users. Each AP uses local channel estimates obtained from received uplink pilots and applies conjugate beamforming to transmit data to the users. We derive a closed-form expression for the achievable rate. This expression enables us to design an optimal max-min power control scheme that gives equal quality of service to all users. We further compare the performance of the Cell-Free Massive MIMO system to that of a conventional small-cell network and show that the throughput of the Cell-Free system is much more concentrated around its median compared to that of the small-cell system. The Cell-Free Massive MIMO system can provide an almost $20-$fold increase in 95%-likely per-user throughput, compared with the small-cell system. Furthermore, Cell-Free systems are more robust to shadow fading correlation than small-cell systems.

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arXiv2014

Interference Reduction in Multi-Cell Massive MIMO Systems I: Large-Scale Fading Precoding and Decoding

A wireless massive MIMO system entails a large number (tens or hundreds) of base station antennas serving a much smaller number of users, with large gains in spectral-efficiency and energy-efficiency compared with conventional MIMO technology. Until recently it was believed that in multi-cellular massive MIMO system, even in the asymptotic regime, as the number of service antennas tends to infinity, the performance is limited by directed inter-cellular interference. This interference results from unavoidable re-use of reverse-link training sequences (pilot contamination) by users in different cells. We devise a new concept that leads to the effective elimination of inter-cell interference in massive MIMO systems. This is achieved by outer multi-cellular precoding, which we call Large-Scale Fading Precoding (LSFP). The main idea of LSFP is that each base station linearly combines messages aimed to users from different cells that re-use the same training sequence. Crucially, the combining coefficients depend only on the slow-fading coefficients between the users and the base stations. Each base station independently transmits its LSFP-combined symbols using conventional linear precoding that is based on estimated fast-fading coefficients. Further, we derive estimates for downlink and uplink SINRs and capacity lower bounds for the case of massive MIMO systems with LSFP and a finite number of base station antennas.

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arXiv2014

Interference Reduction in Multi-Cell Massive MIMO Systems II: Downlink Analysis for a Finite Number of Antennas

Sharing global channel information at base stations (BSs) is commonly assumed for downlink multi-cell precoding. In the context of massive multi-input multi-output (MIMO) systems where each BS is equipped with a large number of antennas, sharing instant fading channel coefficients consumes a large amount of resource. To consider practically implementable methods, we study in this paper interference reduction based on precoding using the large-scale fading coefficients that depend on the path-loss model and are independent of a specific antenna. We focus on the downlink multi-cell precoding designs when each BS is equipped with a practically finite number of antennas. In this operation regime, pilot contamination is not the dominant source of interference, and mitigation of all types of interference is required. This paper uses an optimization approach to design precoding methods for equal qualities of service (QoS) to all users in the network, i.e.,maximizing the minimum signal-to-interference-plus-noise ratios (SINRs) among all users. The formulated optimization is proved to be quasi-convex, and can be solved optimally. We also propose low-complexity suboptimal algorithms through uplink and downlink duality. Simulation results show that the proposed precoding methods improve 5% outage rate for more than 1000 times, compared to other known interference mitigation techniques.

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arXiv2011

Channel Estimation and Linear Precoding in Multiuser Multiple-Antenna TDD Systems

Traditional approaches in the analysis of downlink systems decouple the precoding and the channel estimation problems. However, in cellular systems with mobile users, these two problems are in fact tightly coupled. In this paper, this coupling is explicitly studied by accounting for channel training overhead and estimation error while determining the overall system throughput. The paper studies the problem of utilizing imperfect channel estimates for efficient linear precoding and user selection. It presents precoding methods that take into account the degree of channel estimation error. Information-theoretic lower and upper bounds are derived to evaluate the performance of these precoding methods. In typical scenarios, these bounds are close.

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