Mitigating Pilot Contamination through Location-Aware Pilot Assignment in Massive MIMO Networks

We propose a novel algorithm to design user load-achieving pilot sequences that mitigate pilot contamination in multi-cell massive multiple-input multiple-output (MIMO) networks. To this end, we first derive expressions for the user load and the load region of the network considering both small-scale and large-scale propagation effects. We then develop the pilot sequence algorithm for multi-cell massive MIMO networks as per the rules of generalized Welch bound equality design. Notably, we find that our algorithm and the corresponding downlink power allocation ensure that the user load is achieved when the signal-to-interference-plus-noise ratio (SINR) requirements for the users lie within the load region. Furthermore, we demonstrate the performance advantage of our proposed design relative to the existing designs, in terms of a larger load region and a higher maximum permitted SINR. Finally, we show that our proposed design can satisfy the pre-defined SINR requirements for users with a finite number of antennas at the base station (BS), while the existing designs cannot satisfy the same requirements even with an infinite number of antennas at the BS.

Massive multiple-input multiple-output (MIMO) systems, where base stations (BSs) are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the BS in closed form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the BS to communicate with the detected UEs. The favorable propagation conditions of massive MIMO suppress interference among UEs, whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in massive MIMO networks, under uncorrelated and correlated fading channels. With $2.5 \times 10^{3}$ UEs that may simultaneously become active with probability 1%, a total of 16 frequency–time codes, and 100 antennas, a given UE is detected with probability 75%, and the accuracy of its timing estimate is on the order of few samples.

Secure communication of intelligent reflecting surface-aided NOMA in massive MIMO networks

The communication technology industry's energy consumption and the resulting energy-related pollution are becoming serious societal and economic challenges. One of the most essential parameters for selecting potential technologies for upcoming wireless communication networks is bandwidth efficiency (BE). However, the main objective is to develop new creative network architecture and technologies that are required to meet the rapid growth in cellular data demand while maintaining same amount of power.Also, Energy Efficiency (EE) has emerged as a key parameter for assessing the performance of wireless communication networks. In this context, the recently suggested massive multiple-input multiple-output (MIMO) technology, which comes under multiuser MIMO with an excess of base station (BS) antennas to support a substantial number of user equipment's (UE) is one of the main technology enablers for 5G.In this paper, new power consumption model is addressed and also novel EE maximization algorithm is proposed which optimizes the resource allocation in massive MIMO network.

Cell-free (CF) massive multiple-input multiple-output (MIMO) system is introduced to fulfill the requirement of higher data rates by serving a user through multiple access points (APs). However, due to a large number of users and a limited number of pilot sequences, the CF network suffers from severe inter-user interference, which deteriorates the achievable throughput. In this work, we propose an interference aware user-group (IA-UG) pilot assignment scheme that addresses the issue of inter-user interference while supporting a large number of users. In the proposed IA-UG scheme, we count the number of common APs between two user pairs and use it as a likely measure of the number of interferers. Subsequently, we focuses on creating the user-groups by minimizing the number of interferers in the group having the maximum count. Numerical results show that the proposed pilot assignment scheme outperforms the existing schemes and yields a superior quality of channel estimates as well as the throughput.

This paper presents new learning-based, energy-delay-aware power control strategies for the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks. We first propose a new algorithm to adaptively select an appropriate set of multi-antenna access points (APs) to serve each user at any time instant. A multi-level hidden Markov model (HMM) is constructed to predict the trajectory of a user and forecast the changing set of serving APs for the user over time. Given the selected, time-changing sets of serving APs, we also formulate a new multi-objective power control problem to minimize a weighted sum of the energy and delay costs of the users. By employing Bernoulli bandit learning (BBL) and Gaussian bandit learning (GBL), two new energy-delay-aware power control strategies are developed to adapt unpredictable channel dynamics, minimize the energy-delay costs online, and remain effective over a long time even when sharp changes occur in the channels. Numerical results demonstrate that the new predictive selection of serving APs and the new learning-based energy-delay-aware power control policies can significantly improve the reliability of uplink transmissions in dynamic CF massive MIMO networks, as compared to state-of-the-art static oracle solutions.

Recently, a large-scale fading precoding (LSFP) for the wireless massive multiple-input, multiple-output (MIMO) systems has been proposed. In this precoding, the channel information of all the cells using re-use pilot sequences is processed jointly, and pilot contamination and interference due to a certain number of antennas are effectively eliminated. Additionally, recent studies have found that research in the asymptotic field can be applied to the wireless large-scale MIMO systems. In the LSFP, pilot contamination and signal interference will be completely eliminated when a number of antennas at a base station tend to be unlimited. In this research found that the LSFP method can eliminate most pilot contamination and interference in practical applications only when the number of antennas of the base station reaches hundreds of orders, which greatly increases the equipment construction cost. On the other hand, channel inversion denotes a multi-user channel modulation technology, where a vector signal generated between a user and a base station is used to form an inverse channel matrix so that the channels of each user are balanced during the transmission. In this paper, the channel inversion technology is used in the LSFP. The improved LSFP can effectively reduce the number of antennas required by the base station without affecting the performance of eliminating the pilot contamination and interference. It is shown that when the number of antennas of a base station tends to be unlimited, the improved LSFP can eliminate pilot contamination and signal interference. The simulation results show that in the same practical scenario, when the base station is equipped with the same number of antennas, the improved method can more effectively improve the anti-contamination and anti-interference performance over conventional LSFP.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

In this paper, we investigate multiuser diversity in interference-limited multiple access underlay cognitive radio systems with line-of-sight (LoS) interference between the secondary and primary networks. Assuming Rician-K interference channels and N secondary users, it is shown that the secondary network sum capacity scales like log((K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> + K)/W(KeK/N)), where W(.) is the Lambert-W function. Thus, LoS interference hinders the achievable multiuser diversity gain experienced in Rayleigh interference channels, where the sum capacity grows like log(N). To overcome this problem, we propose the usage of a single radio Electronically Steerable Parasitic Array Radiator antenna at each of the secondary mobile terminals. These antennas will be used to induce artificial fluctuations in the interference channels to restore the log(N) growth rate by assigning random weights to orthogonal basis patterns. This technique will be referred to as random aerial beamforming (RAB). It is also shown that by using RAB, one can actually exploit LoS interference to improve multiuser interference diversity by boosting the effective number of users with minimal hardware complexity.

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms of BS configurations, cell loading, and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load. Together with energy efficiency (EE) we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size, available bandwidth, output power level of the BS, and maximum output power of the power amplifier (PA) at different cell loading. We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business, residential, street, and highway areas.

Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that can provide the required large system capacity in the future fifth generation (5G) cellular networks. Massive MIMO is characterized by a large number of base station (BS) antennas, which makes it less susceptible to noise and fast fading. Unfortunately, this large antennas number does not enable the massive MIMO to get rid of the inter-cell interference originated from the problem of pilot contamination. In this article, we propose a spatiotemporal scenario that can mitigate the pilot contamination effects by avoiding synchronous pilot transmission in the time domain and following a pilot allocation algorithm in the spatial domain. We show that our proposed scenario provides a reduced level of inter-cell interference, which eventually gives it the ability to enhance the minimum and the mean achievable capacities per terminal.

In this paper, we propose a new space-time modulation scheme using quadrature amplitude modulation division for a two-user massive multiple-input multiple-output (MIMO) uplink system, in which channel state information (CSI) is unavailable at both the transmitter and the receiver. It is proved that our proposed transmission scheme enables both the large scale channel coefficients and the transmitted symbols to be uniquely and explicitly determined in a noise-free case when the number of the receiver antennas tends to infinity. For a noisy channel with a limited number of the receiver antennas, three different detectors including a simple closed-form detector, a minimum Euclidean distance detector, and a minimum Riemannian distance detector, are investigated. In addition, when the distribution of the large scale channel coefficients is known to the transmitter, a power loading algorithm is proposed. Finally, computer simulations demonstrate that our proposed communication scheme performs very well in terms of bit error rate (BER) with a moderate number of base station (BS) antennas.

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

In this article, two effective schemes are proposed for association and pilot assignment to tackle the problem of spectral efficiency (SE) maximization and interference management in a user-centric cell-free (CF) massive multiple-input–multiple-output (mMIMO) system. First, a novel access point (AP)–user equipment (UE) association scheme is proposed, which ensures both scalability and association for all the UEs in a CF mMIMO system. After that, a pilot assignment scheme is also proposed which has an iterative structure that utilizes the auction algorithm to solve symmetrical assignment problems for pilot allocation. Numerical results demonstrate the superior performance of the proposed AP–UE association scheme compared to the existing AP preference-based association scheme. Moreover, the proposed pilot assignment scheme provides up to 30% average SE performance and up to 50% reduction in the average interference to signal power ratio (ISR) over that of several competing alternatives. Numerical simulated results further reveal that the proposed pilot assignment scheme improves the average SE and ISR reduction performance regardless of the number of UEs, APs, and pilot sequences in the system. Finally, the proposed pilot assignment scheme manifests the best balance in the tradeoff among the considered performance criteria relative to its comparatives.

Massive multiple-input multiple-output (MIMO) systems can achieve high data rates due to the large number of antennas at the base station when it serves a small number of users. However, deploying massive MIMO in current mobile networks faces a lot of practical issues such as limited physical space and power consumption. Packing large number of antennas in a limited physical space makes spatial correlation between the base station antennas inevitable. In this paper, we study the effect of correlation and we investigate the effectiveness of increasing the number of antennas given limited physical space. Hybrid analog and digital MIMO decoding is adopted to exploit the large array gain at a lower cost and power consumption. Also, different antenna-array structures are investigated and compared in terms of power consumption versus performance.