Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.

Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.

The femtocell has evolved as a great solution for improving coverage and traffic offloading from the current LTE cellular networks, and it accomplishes the dreams of the high data rate for indoor mobile users. However, the exponentially expanding LTE femtocells cause interference in the network, as they share the same licensed spectrum with a macrocell. To tackle this issue, numerous interference mitigation techniques have been proposed in the literature. In this paper, we proposed an Active Power Control (APC) technique, which not only reduces Inter-Cell Interference (ICI) in a Macro User Equipment (MUE), generated from the downlink transmission power of an inadequately deployed femtocell, but also reduces unnecessary power consumption to achieve a green femtocell network. The simulation results show that the proposed APC technique effectively reduces ICI and optimizes the throughput performance of the MUE. Compared with the existing power control techniques, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) of the Femto User Equipment (FUE) and reducing ICI to the victim MUE existing in the close proximity of the femtocell.

The overlay integration of low-power femtocells over macrocells in a heterogeneous network (HetNet) plays an important role in dealing with the increasing demand of spectral efficiency, coverage and higher data rates, at a nominal cost to network operators. However, the downlink (DL) transmission power of an inadequately deployed femtocell causes inter-cell interference (ICI), which leads to severe degradation and sometimes link failure for nearby macrocell users. In this paper, we propose an adaptive network sensing (ANS) technique for downlink power control to obviate the ICI. The simulation results have shown that the ANS power control technique successfully decreases the cell-edge macro user’s interference and enhances the throughput performance of macro users, while also optimizing the coverage and capacity of the femtocell. When compared with the Femto User Equipment (FUE)-assisted and Macro User Equipment (MUE)-assisted power control technique, the proposed technique offers a good tradeoff in reducing interference to macro users, while maintaining the quality of service (QoS) requirement of the femtocell users.

Hybrid precoding has been a promising technique in millimeter wave (mmWave) communications, providing a balanced tradeoff between system performance and hardware complexity. Existing hybrid precoding schemes either require full channel state information (CSI) or use codebook-based design, causing large feedback overhead or degraded system performance, respectively. In this paper, we propose a balanced scheme with limited feedback CSI and element-level quantization. Our key idea is to maximize the system sum rate by using an adaptive baseband precoder and an eigenbeam radio frequency (RF) precoder. The adaptive baseband precoder can balance the effective channel gain and the multiuser interference according to the transmitted power. The eigenbeam RF precoder is optimized with only requiring a limited length of feedback vector. Considering the practical constraints on RF precoders, we then propose a quantization algorithm that minimizes the subspace distortion between the optimized eigenbeam RF precoder and the quantized one. The quantization algorithm also works for the case when RF chains are more than users. Extensive simulation results are provided and validate the effectiveness of the proposed hybrid precoding scheme.

This correspondence paper proposes a novel low-complexity radio-frequency (RF) precoding and combining scheme for wideband multiuser millimeter wave hybrid-array systems, targeting at maximizing the system energy efficiency. We first derive a nearly-optimal fully-connected RF precoder, via minimizing the correlation across different users and subcarriers. We then extend the optimization solution to subarray-based architectures by exploiting the unitary matrix feature of subarrays. With the obtained phase values of the precoder, we optimize the power allocation on each subcarrier of the baseband precoder. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding scheme.