Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.

http://apps.fcc.gov/ecfs/document/view;NEWECFSSESSION=ThTmVg1SGvsFqgxGKYfckQC26BmBGYLH2jpL5z0dMms1j21m5YTC!-1110365353!-349327291?id=60001013324

Millimeter Wave Channel Modeling and Cellular Capacity Evaluation

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.

Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore entails larger size and higher cost than would otherwise be required for the same power output. An alternative solution, which is gaining widespread popularity, is to employ digital baseband predistortion ahead of the amplifier to compensate for the nonlinearity effects, hence allowing it to run closer to its maximum output power while maintaining low spectral regrowth. Recent improvements to the technique have included memory effects in the predistortion model, which are essential as the bandwidth increases. In this paper, we relate the general Volterra representation to the classical Wiener, Hammerstein, Wiener-Hammerstein, and parallel Wiener structures, and go on to describe some state-of-the-art predistortion models based on memory polynomials. We then propose a new generalized memory polynomial that achieves the best performance to date, as demonstrated herein with experimental results obtained from a testbed using an actual 30-W, 2-GHz power amplifier

A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

We introduce the modified Cramer-Rao bound (CRB) which, like the true CRB, is a lower bound to the error variance of any parameter estimator. The modified CRB proves useful when, in addition to the parameter to be estimated, the observed data also depend on other unwanted parameters. The relationship between the modified and true CRB is established and applications are discussed regarding the estimation of carrier-frequency offset, carrier phase, and timing epoch in linearly modulated signals. Modified CRBs for phase and timing estimation have been already discussed in previous works where it is shown that several practical carrier-phase and clock recovery circuits do attain such bounds. Frequency discrimination, instead, is not so well-represented in the literature and a significant contribution of this paper is the calculation of the modified CRB for frequency estimation. This bound is compared with the performance of some frequency detectors and it is concluded that further work is needed in search of more efficient frequency discrimination methods. >

The modified Cramer-Rao bound and its application to synchronization problems

Reliable data detection in time division multiple access (TDMA) communication systems strictly depends on the availability of accurate estimates of the synchronization parameters of the received signal, ie, carrier frequency/phase and symbol timing, which must be derived from the burst preamble The authors focus on the carrier frequency estimation aspect, and present a fast, open-loop, all-digital frequency offset estimation technique, whose performance is assessed in two different communication scenarios: a TDMA satellite link employing standard modulation and burst formats, and a mobile cellular terrestrial radio system with signal and channel characteristics obeying the pan-European Group Special Mobile (GSM) recommendations The use of the algorithm as a frequency error detector (discriminator) in a recursive ("closed-loop") frequency offset estimator is also discussed, and some results concerning both the transient and the steady-state behavior of such a scheme are presented Finally, the impact of the algorithm on the receiver BER is briefly analyzed >

Carrier frequency recovery in all-digital modems for burst-mode transmissions

We present and analyze a technique for fast acquisition and accurate tracking of the carrier frequency in orthogonal frequency division multiplexing (OFDM) receivers. The scheme is based on a data-aided frequency estimation algorithm. The presence of known symbol sequences periodically inserted in the OFDM frame allows the data demodulator to rapidly lock onto the carrier frequency during the acquisition phase, even in the presence of frequency offsets up to a few tenths of the overall signaling rate. Once acquisition is over, the circuit switches to a decision-directed mode to perform fine frequency tracking for reliable data demodulation. The algorithm performance is analyzed in terms of width of the lock-in frequency range and of lock-in probability in the acquisition mode, and of mean-square frequency estimation error in the tracking mode. Since OFDM is known to be extremely sensitive to carrier frequency errors, the impact of the carrier frequency synchronizer on the receiver error rate is also investigated.

Carrier frequency acquisition and tracking for OFDM systems

In this paper we extend the scalar modified Cramer-Rao bound (MCRB) to the estimation of a vector of nonrandom parameters in the presence of nuisance parameters. The resulting bound is denoted with the acronym MCRVB, where "V" stands for "vector". As with the scalar bound, the MCRVB is generally looser than the conventional CRVB, but the two bounds are shown to coincide in some situations of practical interest. The MCRVB is applied to the joint estimation of carrier frequency, phase, and symbol epoch of a linearly modulated waveform corrupted by correlated impulsive noise (encompassing white Gaussian noise as a particular case), wherein data symbols and noise power are regarded as nuisance parameters. In this situation, calculation of the conventional CRVB is infeasible, while application of the MCRVB leads to simple useful expressions with moderate analytical effort. When specialized to the case of white Gaussian noise, the MCRVB yields results already available in the literature in fragmentary form and simplified contexts.

The modified Cramer-Rao bound in vector parameter estimation

This paper presents a novel technique for power amplifier linearization in digital microwave radio systems. The proposed technique is based on the use of a predistortion circuit, whose AM/AM and AM/PM responses are separately implemented as polynomial approximations of the respective responses of the ideal linearizer. The proposed scheme is shown to attain superior performance in comparison with other well-known predistortion structures, such as those based on the cancellation of third or fifth order distortion, with no substantial aggravation in implementation complexity.

Ruggero Reggiannini

Papers

The modified Cramer-Rao bound and its application to synchronization problems

Carrier frequency recovery in all-digital modems for burst-mode transmissions

Carrier frequency acquisition and tracking for OFDM systems

The modified Cramer-Rao bound in vector parameter estimation

RF power amplifier linearization through amplitude and phase predistortion