This paper presents the system design, calibration, and example measurements of a novel ultra-wideband, low-cost, real-time channel sounder. The channel sounder operates in the frequency range from 3 to 18 GHz with a 15-GHz bandwidth thus providing a Fourier delay resolution of 66.7 ps. It sequentially measures overlapping sub-bands of 1-GHz bandwidth to cover the whole frequency range. By using a waveform generator and a digitizer with relatively lower sample rates, the design significantly lowers the cost compared with sampling the whole band simultaneously. With the transmitting power of 40 dBm, the maximum measurable path loss is 132 dB without accounting for possible antenna gains. In this paper, we also describe the calibration procedure along with the method to stitch sub-bands into a combined channel response across 15 GHz of bandwidth. The channel sounder performance is compared with a vector network analyzer (VNA) measurements acquired in the same static channels. In contrast to VNAs, however, our channel sounder can measure the whole band in 6 ms and can, thus, operate in dynamic environments with coherence time more than 6 ms. Additionally, we present sample results from a measurement campaign performed in an indoor corridor investigating the delay spreads statistics over the range of 3–18 GHz.

Fifth generation cellular systems will operate in a wide range of frequencies, covering both the traditional cellular bands, and mm-wave frequency bands, either selecting a specific band or operating in multiple bands simultaneously. For the design of such systems, a detailed understanding of the frequency dependence of the propagation channel is essential. While many channel measurements exist in either microwave bands (20 GHz), the results are not easily comparable, and furthermore the transition between these bands is not well studied. This paper aims to bridge the gap by providing the results from a channel measurement campaign conducted in urban macro and micro-cellular environments, over the continuous frequency band of 3–18 GHz. We characterize the pathloss, shadow fading, root-mean-square (RMS) delay spreads, Ricean factor and coherence bandwidth in urban macro-cellular and urban micro-cellular environments. Measurements were taken in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments. The pathloss exponents and the RMS delay spreads increase with the base station height in NLOS environment; but not much dependency is observed in the LOS environment. By dividing the wideband channel transfer function into subbands of 1 GHz each, we study the frequency dependence of pathloss, shadow fading, Ricean factor, delay spread, and coherence bandwidth in the 3–18 GHz band. The pathloss exponents vary significantly with frequency, but not-monotonically. The shadow fading and the Ricean factor increase with frequency. The RMS delay spreads decrease with frequency in the LOS environments, but they do not change significantly in the NLOS environments. The coherence bandwidth values do not change significantly with frequency in either environment.

Jamming, either intentional or unintentional, is a big, and increasingly prevalent, problem for many wireless communications systems. A well-known countermeasure is the application of spread spectrum techniques that spread the information over a bandwidth possibly larger than that of the jammer thereby exploiting processing gain for jammer suppression; an especially attractive spreading technique is multi-carrier code division multiple access (MC-CDMA), due to its flexibility to shape transmit spectra, resilience to inter-symbol interference, and ease of implementation. Such spreading needs to be combined with spectral notching at the receiver RF frontend, to avoid saturation of the low-noise amplifiers. Such notching, in turn, requires the receiver to sense the band over which the jammer is operating. In this paper, we consider an MC-CDMA transceiver that is being jammed by a wideband non-stationary jammer. For a Rayleigh fading channel, we derive the symbol error rate for the case that the jammer parameters are available at the receiver. We derive the Maximum-Likelihood-based and Log-Likelihood Ratio-based jammer parameter estimators for both the case where channel state information is available during jammer sensing and when it is not. The performance of various jammer parameter estimation schemes is studied using Monte Carlo simulations and with the channel impulse responses collected from the wireless propagation measurements conducted in the 10-12 GHz band.

Multidimensional characterization of outdoor urban macrocellular propagation channels is essential for the analysis and design of next-generation (5G and beyond) cellular massive MIMO (multiple-input-multiple-output) systems. Since most massive MIMO arrays will extend in two or three dimensions, an understanding of 3-D parameters (i.e., azimuth and elevation) of the multipath components (MPCs) is required. This paper presents an extensive measurement campaign for 3-D outdoor propagation channels in an urban macrocellular environment. Measurements were performed with a 20 MHz wideband polarimetric MIMO channel sounder centered at 2.53 GHz and MPCs were extracted using RIMAX-an iterative maximum likelihood algorithm. The physical propagation mechanisms of the observed discrete MPCs are explained in terms of waveguiding, over-the-rooftop propagation, and scattering by far-away objects. MPCs exhibit clustering in the temporal and spatial domains; both intra- and inter-cluster parameters and their relevant statistics are provided. We also extract diffuse MPCs, show that they can comprise a moderate portion of the overall energy, and provide a statistical characterization.

In the real-world environments, multipath components (MPCs) of wireless channels are generally distributed as groups, i.e., clusters. Modeling the clustered MPCs is important and necessary for channel modeling and an automatic clustering algorithm is thus required. This paper proposes a novel Kernel-power-density (KPD) based algorithm for MPC clustering. It uses the Kernel density to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. The proposed algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. Simulations validate the KPD algorithm and almost no performance degradation is found even with a large number of clusters and large cluster angular spread. The KPD algorithm enables applications with no prior knowledge about the clusters such as number and initial locations. It can be used for the cluster based channel modeling for 4G#x002F;5G communications.

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.

The 3-D multiple-input multiple-output (3-D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. In this paper, we present a low-cost test equipment (channel sounder) and post-processing algorithms suitable for investigating the 3-D MIMO channels as well as the results from a measurement campaign for obtaining elevation and azimuth characteristics in an outdoor-to-indoor (O2I) environment. Due to limitations in available antenna switches, our channel sounder consists of a hybrid switched/virtual cylindrical array with effectively 480 antenna elements at the base station. The virtual setup increased the overall MIMO measurement duration, thereby introducing phase drift errors in the measurements. Using reference antenna measurements, we estimate and correct for the phase errors during post-processing. We provide the elevation and azimuth angular spreads for the measurements done in an urban macro-cellular and urban micro-cellular environments, and study their dependence on the user equipment (UE) height. Based on the measurements done with UE placed on different floors, we study the feasibility of separating users in the elevation domain. The measured channel impulse responses are also used to study the channel hardening aspects of the massive MIMO and the optimality of the maximum ratio combining receiver.

3-dimensional Multiple-Input Multiple-Output (3D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. For 3D MIMO system design, it is important to have full characterization of the 3D MIMO propagation channel, i.e., characterize both the elevation and azimuth characteristics of the wireless propagation channel, especially at the base station end. In this paper, we present first results from a measurement campaign for obtaining these characteristics. For those measurements we use a hybrid switched/virtual cylindrical array with 480 antenna elements at the base station (BS), and a switched array with 24 antenna elements at the user equipment (UE). We perform outdoor-to-indoor (O2I) channel measurements in an urban macro-cellular (UMa) and a micro-cellular (UMi) environments. We provide the elevation and azimuth angular spreads at the transmitter and receiver, and study their dependence on the UE height. With the increase in the UE height, the BS elevation spreads decreased from 1.24 deg to 1 deg and 1.15 deg to 0.78 deg respectively for UMa and UMi; the azimuth spreads remained approximately the same (7.5 deg). Based on the measurements done with the UE placed on different floors, we study the feasibility of separating users in the elevation domain. Users were separable in 44% and 54.17% scenarios respectively for the UMa and UMi environments.

This paper presents a detailed description of a propagation channel measurement campaign performed in a warehouse environment and provide a comprehensive channel model for this environment. Using a vector network analyzer, we explored both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios over a 2-8 GHz frequency range. We extracted both small-scale and large-scale channel parameters such as distance-dependent path loss exponent, frequency-dependent path loss exponent, shadowing variance, and amplitude fading statistics of the channel. We also provide the clustering analysis of the channel impulse responses by using a modified Saleh-Valenzuela approach. Our model is validated by comparing the distributions of the root-mean-square (RMS) delay spread obtained from our model and measurement data, respectively. The model developed can be used for realistic performance evaluations of ultrawideband (UWB) communications and localization systems in warehouse environments.

Ultra-wideband (UWB) technology is a good candidate to provide accurate position information indoors and in dense urban environments where Global Positioning System (GPS) is usually not reliable. This paper provides the results of a UWB ranging measurement campaign carried out in a dense urban environment. Measurements were taken with two different antenna heights in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. It is observed that the ranging errors increases when the antennas are closer to the ground, and is more significant in NLOS conditions. Also, in NLOS conditions ranging errors of more than 10 meters were observed when the LOS component is completely blocked by a building. Errors of such magnitude are typically not captured by IEEE 802.15.4a CM6 (outdoor NLOS) channel model. Since the conventional thresholding schemes provide bad ranging accuracy in presence of multiuser interference (MUI), we propose a nonlinear processing scheme of time-hopping impulse radio (TH-IR) and apply it to our measurements to show that it gives much better ranging accuracy.

Roundtrip time-of-arrival (ToA) measurements employing ultra-wideband (UWB) signals can provide high-precision ranging information. However, the accuracy is degraded by multiuser interference (MUI), in particular in the presence of multipath propagation. While the processing gain of time-hopping impulse radio (TH-IR) can be used to suppress the MUI, this is often insufficient. We propose instead a nonlinear processing scheme of TH-IR that effectively suppresses MUI without requiring knowledge of the time-hopping sequences of the interfering users. The principle is that multipath components (MPCs) of interferers do not align closely, for the majority of transmission frames, with the MPCs of the desired signal. Through a judicious choice of algorithm parameters we show that our algorithm is superior to existing (realizable) thresholding and median filter algorithms, and in some cases can even beat genie-aided thresholding algorithms. The performance is robust to both strength and number of the interferers. The results are validated with both standardized 802.15.4a channel models and measured outdoor UWB channels.

Single receive antenna selection (AS) is a popular method for obtaining diversity benefits without the additional costs of multiple radio receiver chains. Since only one antenna receives at any time, the transmitter sends a pilot multiple times to enable the receiver to estimate the channel gains of its N antennas to the transmitter and select an antenna. In time-varying channels, the channel estimates of different antennas are outdated to different extents. We analyze the symbol error probability (SEP) in time-varying channels of the N-pilot and (N + 1)-pilot AS training schemes. In the former, the transmitter sends one pilot for each receive antenna. In the latter, the transmitter sends one additional pilot that helps sample the channel fading process of the selected antenna twice. We present several new results about the SEP, optimal energy allocation across pilots and data, and optimal selection rule in time-varying channels for the two schemes. We show that due to the unique nature of AS, the (N +1)-pilot scheme, despite its longer training duration, is much more energy-efficient than the conventional N-pilot scheme. An extension to a practical scenario where all data symbols of a packet are received by the same antenna is also investigated.

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We then present new "void filling" methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed form equations for the symbol error probability with and without void-filling are also developed.

Antenna selection (AS) provides most of the benefits of multiple-antenna systems at drastically reduced hardware costs. In receive AS, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. The "best" subset to be used for data reception is determined by means of channel estimates acquired using training sequences. Due to the nature of AS, the channel estimates at different antennas are obtained from different transmissions of the pilot sequence, and are, thus, outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We present a "void-filling" method for fully exploiting these voids, which essentially provides more accurate training for some antennas, and derive the optimal subset selection rule for any void-filling method. We also derive new closed-form equations for the performance of receive AS with optimal subset selection.

Receive antenna selection (AS), in which only a subset of antennas receive simultaneously at any time, requires the transmitter to send pilots multiple times so that the receiver can acquire channel state of all antennas and select the best subset. In conventional AS training, sensitivity of coherent reception to channel estimation errors forces the transmitter to boost the energy allocated to all pilots to ensure accurate channel estimates. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS. Using one extra pilot symbol, accurate channel estimates get generated for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. Other pilots, which now primarily help in subset selection, get allocated less energy. We derive closed-form expressions for the proposed scheme's symbol error probability, and optimize the energy allocated to pilot and data symbols. We show that the optimal solution achieves full diversity; it is provably unique and strictly better than the conventional model.

Hardware constraints, which motivate receive antenna selection, also require that various antenna elements at the receiver be sounded sequentially to obtain estimates required for selecting the 'best' antenna and for coherently demodulating data thereafter. Consequently, the channel state information at different antennas is outdated by different amounts and corrupted by noise. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, a preferable strategy is to linearly weight the channel estimates of different antennas differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then characterize explicitly the optimal selection weights that minimize the SEP. We also consider packet reception, in which multiple symbols of a packet are received by the same antenna. New suboptimal, but computationally efficient weighted selection schemes are proposed for reducing the packet error rate. The benefits of weighted selection are also demonstrated using a practical channel code used in third generation cellular systems. Our results show that optimal weighted selection yields a significant performance gain over conventional unweighted selection.

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the "best" antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.