In wireless communication systems, nonlinearity is the root cause of many undesired effects. In order to compensate these effects, the nonlinearity must be estimated accurately. This article deals with nonlinearity estimation methods and especially focuses on sequences that are optimized for that purpose. Based on the Cramér-Rao bound of the nonlinearity estimator, we derive an objective function for sequence optimization. An algorithm is proposed to solve this optimization problem while satisfying constraints on the sequence format to produce sequences that are compatible to current wireless LAN standards. The obtained sequences are thoroughly analyzed and compared against sequences defined in IEEE 802.11ax. Their superiority confirms that dedicated sequences for nonlinearity estimation are valuable in future communication standards. Moreover, we provide an analysis of estimation error components to show that an optimal number of estimated polynomial coefficients can be found that minimizes estimation error. To further show the benefits of optimized sequences in the context of an application, a method for digital nonlinear self-interference cancellation suited for frequency-division duplex systems is proposed and evaluated. With that, we demonstrate that cancellation performance is significantly enhanced using optimized estimation sequences.

Nonlinear characteristics in a wireless transmitter can cause various undesired effects like spectral regrowth or signal quality degradation. To compensate these effects, e.g., via digital predistortion, an accurate estimation of the nonlinear characteristic is required. This work focuses on methods and sequences that allow accurate and robust estimation of a non-linearity. Based on the Cramér- Rao bound of the nonlinearity estimator, we derive an objective function for sequence optimization. An algorithm is proposed to solve this optimization problem while satisfying constraints on the sequence format to obtain sequences that are compatible to current wireless LAN standards. The performance of the optimized sequences is evaluated using a power amplifier measurement. Results show that the optimized sequences are superior to conventional estimation sequences defined in the IEEE 802.11ax standard. Thus, dedicated sequences for nonlinearity estimation should be considered in future communication standards. Moreover, we show that depending on the noise power an optimal number of estimated polynomial coefficients can be found that minimizes estimation error.

In Massive MIMO base stations (BSs), the hardware design needs to balance high spectral efficiency (SE) with low complexity. The level of hardware impairments (HWIs) indicates how strong the signal distortion introduced by hardware imperfections is. In particular, the analog-to-digital converters (ADCs) have an important impact on signal distortion and power consumption. This article addresses the fundamental problem of selecting the optimal hardware quality in the Massive MIMO space. In particular, we examine the optimal HWI and ADC bit allocation per BS antenna to maximize the SE. The results show that in co-located arrays with low channel gain variations across antennas, equal ADC bit allocation is optimal. In contrast, cell-free Massive MIMO systems benefit the most from optimizing the ADC bit allocation achieving improvements in the order of 2 [bit-per-channel-use] per user equipment when using regularized zero-forcing (RZF). In addition, when including the impact of power consumption in cell-free Massive MIMO with RZF, allocating low values of mixed ADC bit resolutions across the BS antennas can increase the energy efficiency up to 30% compared to equal ADC bit allocation.

Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It is often assumed that regular pilots (RP), where a fraction of the time-frequency resources is reserved for pilots, suffices to provide high SE. However, the SE is limited by the pilot overhead and pilot contamination. An alternative is superimposed pilots (SP) where all resources are used for pilots and data. This removes the pilot overhead and reduces pilot contamination by using longer pilots. However, SP suffers from data interference that reduces the SE gains. This paper proposes the Massive-MIMO Iterative Channel Estimation and Decoding (MICED) algorithm where partially decoded data is used as side-information to improve the channel estimation and increase SE. We show that users with precise data estimates can help users with poor data estimates to decode. Numerical results with QPSK modulation and LDPC codes show that the MICED algorithm increases the SE and reduces the block-error-rate with RP and SP compared to conventional methods. The MICED algorithm with SP delivers the highest SE and it is especially effective in scenarios with short coherence blocks like high mobility or high frequencies.

Next-generation wireless networks aim at providing substantial improvements in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been proved to be a viable technology to achieve these goals by spatially multiplexing several users using many base station (BS) antennas. A potential limitation of massive MIMO in multicell systems is pilot contamination, which arises in the channel estimation process from the interference caused by reusing pilots in neighboring cells. A standard method to reduce pilot contamination, known as regular pilot (RP), is to adjust the length of pilot sequences while transmitting data and pilot symbols disjointly. An alternative method, called superimposed pilot (SP), sends a superposition of pilot and data symbols. This allows use of longer pilots which, in turn, reduces pilot contamination. We consider the uplink of a multicell massive MIMO network, with i.i.d. Rayleigh fading channels, using maximum ratio combining and compare RP and SP in terms of SE and EE. To this end, we derive rigorous closed-form achievable rates with SP under a practical random BS deployment. We prove that the reduction of pilot contamination with SP is outweighed by the additional coherent and non-coherent interference. Numerical results show that when both methods are optimized, RP achieves comparable SE and EE to SP in practical scenarios.

This work aims to design a cellular network for maximal energy efficiency (EE). In particular, we consider the uplink with multi-antenna base stations and assume that zero- forcing (ZF) combining is used for data detection with imperfect channel state information. Using stochastic geometry and a new lower bound on the average per-user spectral efficiency of the network, we optimize the pilot reuse factor, number of antennas and users per base station. Closed- form expressions are computed from which valuable insights into the interplay between the optimization variables, hardware characteristics, and propagation environment are obtained. Numerical results are used to validate the analysis and make comparisons with a network using maximum ratio (MR) combining. The results show that a Massive MIMO setup arises as the EE-optimal network configuration. In addition, ZF provides higher EE than MR while allowing a smaller pilot reuse factor and a more dense network deployment.

Massive multiple-input multiple-output (MIMO) is a viable technology to improve the spectral efficiency (SE) by spatially multiplexing several users. A potential limitation of Massive MIMO in multicell systems is pilot contamination, which arises from interference in the channel estimation due to the reuse of pilot sequences in neighboring cells. A standard method to reduce pilot contamination, referred to as regular pilot (RP), is to adjust the length of the pilot sequences while transmitting data and pilot symbols disjointly. Alternatively, the superimposed pilot (SP) method sends a superposition of pilot and data symbols, thereby allowing the use of longer pilots which can also reduce pilot contamination. This work considers the uplink of a general multicell Massive MIMO system with SP and maximum ratio combining and derives rigorous closed-form achievable rates, which are used to make comparisons with RP. Numerical results consider a realistic random base station deployment and show that with SP the reduction of pilot contamination is outweighed by the additional coherent and non-coherent interference from the data transmission. Moreover, it turns out that, when the pilot length is optimized, RP provides comparable SE as with SP.

Proximity-based applications are becoming fast growing markets suggesting that device-to-device (D2D) communications is becoming an essential part of the future mobile data networks. We propose scalable admission and power control methods for D2D communications underlay cellular networks to increase the reuse of frequency resources and thus network capacity while maintaining QoS to all users. In practice, as D2D communications will generate a new layer of interference, it is essential to take D2D interference into account in inter-cell interference coordination for multi-cell communications. The aim of the proposed methods is to maximize the number of D2D links under QoS constraints, therefore maximizing network frequency reuse in a practical 5G multi-cell environment. Different schemes are designed for applications that have different levels of complexity and availability of channel state information. Numerical results show that by using D2D and the proposed multi-cell interference coordination and low power transmission method, the network spectral efficiency can be increased by as much as ten times, while low outage probability can be assured to provide QoS for all users.

The continuous rise in wireless data traffic brings forth an increase in power consumption and static users constitute a large fraction of these traffic demands. This work focuses on designing cellular networks to deliver a given data rate per area and user, while minimizing the power consumption. In particular we are interested in optimizing the transmission power, density of access points (APs), number of AP antennas and number of users served in each cell. To this end, we consider a network model based on stochastic geometry and a detailed power consumption model to derive closed form expressions and obtain insights on the interplay of the aforementioned design parameters. The results show that, in contrast with previous works on optimal network design for energy efficiency, having exceedingly high AP density does not bring the most benefits in terms of power savings. Instead the AP density should be chosen according to the area data rate that we want to deliver. In addition numerical results show that the minimum power consumption is obtained in the Massive MIMO regime with many antennas and users per AP.

Self-organized TDMA (STDMA) has been proposed as a medium access control (MAC) protocol for Vehicle-to-Vehicle (V2V) communication. Although it avoids the unbounded channel access delay faced by other CSMA-based protocols, the reliability in high traffic density is still unsatisfactory for critical road safety applications. This paper introduces an enhanced prediction-based STDMA protocol which utilizes the spatial information available in the messages exchanged for road safety applications. Thus it allocates the radio resources more efficiently and minimizes the mutual interference among different vehicles. The results show that the prediction-based STDMA offers a significant improvement in the scalability and coverage of the V2V communication system for road safety applications with strict reliability requirement. The performance enhancement of the proposed MAC protocol is achieved without adding much complexity to the existing STDMA and it has considerable potential for further improvement.