Search

We propose a partial cooperative zero-forcing (PCZF) decoding scheme for the uplink cell-free massive MIMO system, wherein the neighboring access points (APs) around each user equipment (UE) share the channel state information (CSI) and jointly suppress the interference using the zero-forcing technique. Using asymptotic analysis, we derive a closed-form asymptotic expression for a lower-bound on the achievable rates. Considering the unique and complex form of the achievable rates, we propose power control schemes according to two criteria. The first criterion is to maximize the minimum achievable rate. For this criterion we propose a Target-SINR-Tracking (TST) based bisection algorithm. Since the power control update functions are standard interference functions, the TST based bisection method always converges to the optimal solution. The second criterion is to maximize the sum-rate, for which we propose two power control algorithms: randomization and scaling algorithm (RSA) and fractional programming algorithm (FPA). In each iteration of the RAS algorithm, we first exploit the randomization technique to transform the sum-rate maximization problem into a series of power minimization problems, and then improve the sum-rate by scaling. In the FP algorithm, we derive a lower-bound on the sum-rate, and then propose an iterative approach based on the Lagrangian dual transform and fractional programming to maximize the sum-rate lower-bound. Numerical results validate the theoretical analysis and verify the efficiency of the proposed power control algorithms.

An Internet-of-Things (IoT) system supports a massive number of IoT devices wirelessly. We show how to use cell-free (CF) massive multiple input and multiple output (MIMO) to provide a scalable and energy-efficient IoT system. We employ optimal linear estimation with random pilots to acquire channel state information (CSI) for MIMO precoding and decoding. In the uplink (UL), we employ optimal linear decoder and utilize random matrix (RM) theory to obtain two accurate signal-to-interference plus noise ratio (SINR) approximations involving only large-scale fading coefficients. We derive several max–min type power control algorithms based on both exact SINR expression and RM approximations. Next we consider the power control problem for downlink (DL) transmission. To avoid solving a time-consuming quasiconcave problem that requires repeat tests for the feasibility of a second-order cone programming (SOCP) problem, we develop a neural network (NN) aided power control algorithm that results in 30 times reduction in computation time. This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs. Both UL and DL power control algorithms allow visibly improve the system spectral efficiency (SE) and, more importantly, lead to multifold improvements in energy efficiency (EE), which is crucial for IoT networks.

Non-cooperative cellular massive MIMO, combined with power control, is known to lead to significant improvements in per-user throughput compared with conventional LTE technology. In this paper, we investigate further refinements to massive MIMO, first, in the form of three-fold sectorization, and second, coordinated multi-point operation (with and without sectorization), in which the three base stations cooperate in the joint service of their users. For these scenarios, we analyze the downlink performance for both maximum-ratio and zero-forcing precoding and derive closed-form lower-bound expressions on the achievable rate of the users. These expressions are then used to formulate power optimization problems with two throughput fairness criteria: ${i}$ ) network-wide max-min fairness, and ii ) per-cell max-min fairness. Furthermore, we provide centralized and decentralized power control strategies to optimize the transmit powers in the network. We demonstrate that employing sectorized antenna elements mitigates the detrimental effects of pilot contamination by rejecting a portion of interfering pilots in the spatial domain during channel estimation phase. Simulation results with practical sectorized antennas reveal that sectorization and multi-point coordination combined with sectorization lead to more than $1.7\times $ and $2.6\times $ improvements in the 95%-likely per-user throughput, respectively.

We investigate the feasibility of using Massive MIMO to support URLLC in both coherence interval based and 3GPP compliant pilot settings. We consider grant-free uplink transmission with MMSE receiver and adopt 3GPP channel models. In the coherence interval based pilot setting, by extensive system level simulations, we find that using a Massive MIMO base station with 128 antennas and MMSE receiver, URLLC requirements can be achieved in Urban Macro (UMa) Non-Line of Sight (NLoS) with orthogonal pilots and Neyman-Pearson detector. However, in the 3GPP compliant pilot setting, even by using the covariance matrix of Physical Resource Block (PRB) subcarriers for active UE detection and channel estimation as well as open-loop power control, we find that URLLC requirements are still challenging to achieve due to the insufficient pilot length and pilot symbol location regulations in a PRB., 5 pages, 5 figures, to appear in the 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

We consider a wirelessly powered Internet of Things (IoT) based on cell-free massive MIMO with energy harvesting. 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. We assume that single antenna sensors send uplink pilots in order to allow APs to detect active users and estimate their channel coefficients. We assume that each AP is equipped with N$\geq$1 antennas and uses the linear minimum mean square error (LMMSE) channel estimation. Through an asymptotic analysis, we derive closedform approximations for the variance of the LMMSE channel coefficient estimates, the amount of harvested energy, and the achievable rates for the uplink data transmission. We next use these expressions to jointly optimize the charging duration and uplink and downlink power control coefficients to minimize the total transmit energy consumptions of APs and sensors, which is crucially important for IoT sensors. Simulation results verify the accuracy of the obtained expressions, and shows that significant gains in energy efficiency can be achieved by the proposed optimization algorithms.

We study internet of things (IoT) systems supported by cell-free (CF) massive MIMO (mMIMO) with optimal linear channel estimation. For the uplink, we consider optimal linear MIMO receiver and obtain an uplink SINR approximation involving only large-scale fading coefficients using random matrix (RM) theory. Using this approximation we design several max-min power control algorithms that incorporate power and rate weighting coefficients to achieve a target rate with high energy efficiency. For the downlink, we consider maximum ratio (MR) beamforming. Instead of solving a complex quasi-concave problem for downlink power control, we employ a neural network (NN) technique to obtain comparable power control with around 30 times reduction in computation time. For large networks we proposed a different NN based power control algorithm. This algorithm is sub-optimal, but its big advantage is that it is scalable., 6 pages, 7 figures. 2020 IEEE Global Communications Conference (GLOBECOM). arXiv admin note: text overlap with arXiv:2005.06696

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.

We consider a single-cell downlink time-division duplex-based massive MIMO communication in the presence of an adversary capable of jamming and eavesdropping simultaneously. We show that the 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 (SDoF) attained in the presence of such an attack are identical to the maximum DoF attainable under no attack. Furthermore, we evaluate the number of base station (BS) antennas necessary in order to establish information theoretic security without even a need for Wyner encoding for a given rate of information leakage to the attacker. Next, we show that things are completely different once the adversary starts jamming the training phase. Specifically, we consider the pilot contamination 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 achieved SDoF is identical to zero. Furthermore, the maximum achievable secure rates of users also vanish, even in the asymptotic regime in the number of the BS antennas. We finally address this attack and show that, under training-phase jamming , if the number of pilot signals is scaled in a certain way and the pilot signal assignments can be hidden from the adversary, the users achieve an SDoF identical to the maximum achievable DoF under no attack.

A wireless massive multiple-input multiple-output (MIMO) system entails a large number of base station antennas serving a much smaller number of users, with large gains in spectral efficiency and energy efficiency compared with the conventional MIMO technology. Until recently, it was believed that as the number of base station antennas tends to infinity, the performance of such systems is limited by directed inter-cellular interference caused by unavoidable re-use of training sequences (pilot contamination) by users in different cells. We devise a new concept of large-scale fading precoding (LSFP) that leads to the effective elimination of inter-cell interference. The main idea of LSFP is that base stations linearly combine messages aimed at users from different cells that re-use the same training sequence. Crucially, the combining coefficients depend only on the large-scale fading coefficients between the users and the base stations. These coefficients change slowly and their number does not depend on the number of base station antennas. Thus, the traffic between base stations stays constant even if the number of antennas tends to infinity. Furthermore, we derive a capacity lower bound for massive MIMO systems with LSFP and a finite number of base station antennas. In this regime, mitigation of all types of interference, not only the pilot contamination, is required. We consider optimal and suboptimal LSFP precodings that take into account all sources of interference. Our simulations results show that LSFP provides significant gain even for the case of moderate number of base station antennas.

Cell-free Massive multiple-input multiple-output (MIMO) comprises a large number of distributed low-cost low-power single antenna access points (APs) connected to a network controller. The number of AP antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all APs simultaneously. The simplest linear precoding schemes are conjugate beamforming and zero-forcing. Max–min power control provides equal throughput to all users and is considered in this paper. Surprisingly, under max–min power control, most APs are found to transmit at less than full power. The zero-forcing precoder significantly outperforms conjugate beamforming. For zero-forcing, a near-optimal power control algorithm is developed that is considerably simpler than exact max–min power control. An alternative to cell-free systems is small-cell operation in which each user is served by only one AP for which power optimization algorithms are also developed. Cell-free Massive MIMO is shown to provide five- to ten-fold improvement in 95%-likely per-user throughput over small-cell operation.

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 the 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. Ashikhmin and Marzetta proposed (under the name of Pilot Contamination Precoding) large scale fading precoding (LSFP) and large scale fading 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.

We correct the achievable rates (42) and (47) for small cells in “Cell-free massive MIMO versus small cells,” IEEE Trans. Wireless Commun. , vol. 16, 2017.

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, which is severe for IoT since the things are operating at very low transmit power. To mitigate this problem and achieve a high uplink throughput we use Cell-Free systems with optimal LMMSE channel estimation, while traditionally simple suboptimal estimators have been used in such systems. We further derive an analytical SINR expression 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 over existing Cell-Free wireless systems and a 10-fold improvement over known IoT systems based on small-cell systems.

Non-cooperative cellular Massive MIMO, combined with max-min power control, is known to give substantial improvements in per-user throughput compared with conventional 4G technology. We investigate further refinements to Massive MIMO, first, in the form of three-fold sectorization, which can be viewed as an effective reduction in cell size, and second, three-fold sectorization combined with coordinated multi-point operation, in which the three sectors cooperate in the joint service of their users. We analyze the downlink performance of the system for zero-forcing precoding and propose centralized and decentralized power control schemes. The simulation results reveal that sectorization, and multi-point coordinated sectorization lead to 2.36× and 8.56× improvements in the 95%-likely per-user throughput, respectively.

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.

Cell-free massive multiple-input multiple-output (MIMO) was recently proposed as an alternative to partitioning coverage area into cells. The main idea is to have many access points (APs) distributed over the coverage area to serve a smaller number of users. Users are served by all APs simultaneously by sharing time and frequency resources. In this paper, we study the deployment of multi-antenna APs in cell-free massive MIMO systems. In particular, we study the advantages and disadvantages of using multi-antenna APs with respect to the achievable rates, the backhauling traffic, and the infrastructure cost to give results on how disperse antennas should be among APs.

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.

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., Comment: EEE Transactions on Wireless Communications, accepted for publication

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.

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., Comment: Accepted for publication in IEEE Wireless Communications Letters

Massive multiple-input multiple-output (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of state-of-the-art research on the topic, which has recently attracted considerable attention. We begin with an information theoretic analysis to illustrate the conjectured advantages of massive MIMO, and then we address implementation issues related to channel estimation, detection and precoding schemes. We particularly focus on the potential impact of pilot contamination caused by the use of non-orthogonal pilot sequences by users in adjacent cells. We also analyze the energy efficiency achieved by massive MIMO systems, and demonstrate how the degrees of freedom provided by massive MIMO systems enable efficient single-carrier transmission. Finally, the challenges and opportunities associated with implementing massive MIMO in future wireless communications systems are discussed.

This paper considers a multi-cell multiple antenna system with precoding used at the base stations for downlink transmission. For precoding at the base stations, channel state information (CSI) is essential at the base stations. A popular technique for obtaining this CSI in time division duplex (TDD) systems is uplink training by utilizing the reciprocity of the wireless medium. This paper mathematically characterizes the impact that uplink training has on the performance of such multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multi-cell systems. Furthermore, it develops a new multi-cell MMSE-based precoding method that mitigate this problem. In addition to being a linear precoding method, this precoding method has a simple closed-form expression that results from an intuitive optimization problem formulation. Numerical results show significant performance gains compared to certain popular single-cell precoding methods., Comment: 23 pages, 4 figures

This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multiple-input and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1.8 dB of the capacity of MIMO systems with 8 transmit/receive antennas at spectral efficiencies up to ? = 24 bits/channel use (b/ch). This result improves upon best performance achieved by turbo coded systems using list sphere decoding (LSD) detector by 2.3-3.8 dB, leading to nearly 50% reduction in the capacity gap. Detailed comparisons of the Log-MAP-tb b-MCMC with LSD based detectors demonstrate that MCMC detector is indeed the detector of choice for achieving channel capacity both in terms of performance and complexity.

Cell-Free Massive MIMO systems comprise a large number of distributed, low cost, and low power access point antennas, connected to a network controller. The number of antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all access point antennas simultaneously. In this paper, we define cell-free systems and analyze algorithms for power optimization and linear pre-coding. Compared with the conventional small-cell scheme, Cell-Free Massive MIMO can yield more than ten-fold improvement in terms of 5%-outage rate.

We consider a single-cell downlink massive MIMO system in the presence of an adversary capable of jamming and eavesdropping. We show that, classical attacks of eavesdropping and data jamming can be simultaneously rendered useless. Specifically, we show that, the secure degrees of freedom (DoF) attained in the presence of classical attacks is as same as the maximum DoF attained under no attack. We propose a novel beamforming strategy that establishes information theoretic security without need to Wyner encoding. We next introduce a new attack strategy that involves jamming pilot signals and eavesdropping in succession. 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 number of base station (BS) antennas. We develop counter strategies for this new attack in which we use secret key to encrypt the pilot sequence. We show that hiding the training signal assignments from the adversary enables the users to achieve a secure DoF, identical to the one achieved under no attack.

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., Comment: SPAWC 2015

A coding and modulation technique is studied where the coded bits of an irregular low-density parity-check (LDPC) code are passed directly to a modulator. At the receiver, the variable nodes of the LDPC decoder graph are connected to detector nodes, and iterative decoding is accomplished by viewing the variable and detector nodes as one decoder. The code is optimized by performing a curve fitting on extrinsic information transfer charts. Design examples are given for additive white Gaussian noise channels, as well as multiple-input, multiple-output (MIMO) fading channels where the receiver, but not the transmitter, knows the channel. For the MIMO channels, the technique operates within 1.25 dB of capacity for various antenna configurations, and thereby outperforms a scheme employing a parallel concatenated (turbo) code by wide margins when there are more transmit than receive antennas.

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 the 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. Ashikhmin and Marzetta proposed (under the name of Pilot Contamination Precoding) large scale fading precoding (LSFP) and large scale fading 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.

A large scale antenna system (LSAS) entails a large number of base station (BS) antennas serving a much smaller number of terminals. LSAS demonstrates large gain in spectral-efficiency and energy-efficiency compared with conventional MIMO schemes. In the regime of infinite number of BS antennas, the performance of LSAS can be improved unboundedly by using a multi-cell cooperation method called pilot contamination precoding (PCP). As the number of BS antennas grows PCP completely cancels inter-cell interference resulted from unavoidable use of nonorthogonal pilot sequences in neighboring cells. In this paper, we consider downlink PCP design in the finite antenna regime, where besides pilot contamination, three other types of interference become prominent. We formulate the PCP design as maximizing the minimum signal to interference-plus-noise ratio (SINR) subject to network sum power constraint. We propose optimal and simple suboptimal algorithms for finding PCP matrices. We show that in a practical scenario, say with M = 100 antennas, these approaches give very significant improvement over conventional LSASs.

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

This paper considers the problem of interference mitigation in multi-cell multi-antenna time division duplex (TDD) wireless systems for downlink transmission. An efficient way to obtain channel state information (CSI) at the base station is by using uplink pilots and reciprocity of the downlink channel. At the same time, it has been shown that pilots from different cells contaminate each other, resulting in corruption of precoding matrices used by base stations, and high inter-cell interference. This paper studies the effects of shifting the location of pilots in time frames used in neighboring cells, and its effectiveness in obtaining better channel estimates, and, thereby, inter-cell interference reduction.

This paper considers a multi-cell multiple antenna system with precoding at the base stations for downlink transmission. To enable precoding, channel state information (CSI) is obtained via uplink training. This paper mathematically characterizes the impact that uplink training has on the performance of multi-cell multiple antenna systems. When non-orthogonal training sequences are used for uplink training, it is shown that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells. This problem of pilot contamination is analyzed in this paper. A multi-cell MMSE-based precoding is proposed that, when combined with frequency/time/pilot reuse techniques, mitigate this problem.

We present linear precoding methods for downlink transmission in multi-user multiple-input multiple-output (MIMO) systems where channel state information (CSI) can be obtained through training. In this paper, channel training overhead and estimation error are rigorously accounted for while determining the net system throughput. First, we consider the case with training on the reverse link only. We study a precoding method which is a combination of two schemes: selection of users with largest estimated gains and zero-forcing to selected users. Next, we consider the case with training on both reverse and forward links. We obtain a lower bound on the weighted-sum capacity, and propose an algorithm to determine an efficient precoding matrix. This precoding method effectively utilizes the training on the reverse and forward links in improving net throughput.

It is well known that availability of the channel state information (CSI) at the transmitter significantly increases the throughput of MIMO broadcast systems. For even larger increase of the throughput it is important to identify and to transmit to receivers whose channel vectors are almost orthogonal to each other. Since typically the feedback link from the receivers to the transmitter is low-rate, only a quantized version of the CSI can be sent back to the transmitter. In this paper we consider the problem of designing Grassmannian packings that can serve as good quantization codebooks, have low maximum likelihood decoding complexity, and allow to identify receivers with orthogonal, and/or almost orthogonal, channel vectors in a very efficient way.

In this work we study joint code design and MIMO (multiple-input multiple-output) detection based on Markov Chain Monte Carlo (MCMC) approach. The extrinsic information transfer (EXIT) technique is applied to find optimal irregular LDPC codes that are matched to the characteristics of the MCMC detector. For a large system of 8 transmit and 8 receive antennas and 16 QAM modulation, the optimized LDPC code achieves within 1.4 dB of the channel capacity. This result improves best published results by 2.3 dB, where 1.3 dB is due to the MCMC detection and 1 dB is due to code optimization.