A Probably Approximately Correct Analysis of Group Testing Algorithms

We consider the problem of identifying the defectives from a population of items via a non-adaptive group testing framework with a random pooling-matrix design. We analyze the sufficient number of tests needed for approximate set identification, i.e., for identifying almost all the defective and non-defective items with high confidence. To this end, we view the group testing problem as a function learning problem and develop our analysis using the probably approximately correct (PAC) framework. Using this formulation, we derive sufficiency bounds on the number of tests for three popular binary group testing algorithms: column matching, combinatorial basis pursuit, and definite defectives. We compare the derived bounds with the existing ones in the literature for exact recovery theoretically and using simulations. Finally, we contrast the three group testing algorithms under consideration in terms of the sufficient testing rate surface and the sufficient number of tests contours across the range of the approximation and confidence levels.

Exploiting Beam-Split in IRS-aided Systems via OFDMA

In wideband systems operating at mmWave frequencies, intelligent reflecting surfaces (IRSs) equipped with many passive elements can compensate for channel propagation losses. Then, a phenomenon known as the beam-split (B-SP) occurs in which the phase shifters at the IRS elements fail to beamform at a desired user equipment (UE) over the total allotted bandwidth (BW). Although B-SP is usually seen as an impairment, in this paper, we take an optimistic view and exploit the B-SP effect to enhance the system performance via an orthogonal frequency division multiple access (OFDMA). We argue that due to the B-SP, when an IRS is tuned to beamform at a particular angle on one frequency, it also forms beams in different directions on other frequencies. Then, by opportunistically scheduling different UEs on different subcarriers (SCs), we show that, almost surely, the optimal array gain that scales quadratically in the number of IRS elements can be achieved on all SCs in the system. We derive the achievable throughput of the proposed scheme and deduce that the system also enjoys additional multi-user diversity benefits on top of the optimal beamforming gain over the full BW. Finally, we verify our findings via numerical simulations.

Distributed IRSs Always Benefit Every Mobile Operator

We investigate the impact of multiple distributed intelligent reflecting surfaces (IRSs), which are deployed and optimized by a mobile operator (MO), on the performance of user equipments (UEs) served by other co-existing out-of-band (OOB) MOs that do not control the IRSs. We show that, under round-robin scheduling, in mmWave frequencies, the ergodic sum spectral efficiency (SE) of an OOB MO is monotonic in the total number of IRS elements with a pre-log factor that depends on the channel properties of the OOB UE. We further show that the maximum achievable SE of OOB MO scales log-linearly in IRS elements. Then, by specifying the minimum number of IRSs as a function of the channel parameters, we design a distributed IRS system in which an OOB MO almost surely obtains the maximum SE. Finally, we prove that the outage probability at an OOB UE decreases exponentially in the number of IRSs, even though they are randomly configured from the UE's viewpoint. We numerically verify our theory and conclude that distributed IRSs always help every MO, but the MO controlling the IRSs benefits the most.

Joint State and Input Estimation for Linear Dynamical Systems with Sparse Control

Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and {Bayesian learning-based estimation}. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.

Half-Duplex APs with Dynamic TDD vs. Full-Duplex APs in Cell-Free Systems

In this paper, we present a comparative study of half-duplex (HD) access points (APs) with dynamic time-division duplex (DTDD) and full-duplex (FD) APs in cell-free (CF) systems. Although both DTDD and FD CF systems support concurrent downlink transmission and uplink reception capability, the sum spectral efficiency (SE) is limited by various cross-link interferences. We first present a novel pilot allocation scheme that minimizes the pilot length required to ensure no pilot contamination among the user equipments (UEs) served by at least one common AP. Then, we derive the sum SE in closed form, considering zero-forcing combining and precoding along with the signal-to-interference plus noise ratio optimal weighting at the central processing unit. We also present a provably convergent algorithm for joint uplink-downlink power allocation and uplink/downlink mode scheduling of the APs (for DTDD) to maximize the sum SE. Our numerical results illustrate the superiority of the proposed algorithms over several benchmarks and show that the sum SE with DTDD can outperform an FD CF system with similar antenna density. Thus, DTDD combined with CF is a promising alternative to FD that attains the same performance using HD APs, while obviating the burden of intra-AP interference cancellation.

On the Impact of an IRS on the Out-of-Band Performance in Sub-6 GHz & mmWave Frequencies

Intelligent reflecting surfaces (IRSs) were introduced to enhance the performance of wireless communication systems. However, from a service provider's viewpoint, a concern with the use of an IRS is its effect on out-of-band (OOB) quality of service. Specifically, if two operators, say X and Y, provide services in a given geographical area using non-overlapping frequency bands, and if operator X uses an IRS to enhance the spectral efficiency (SE) of its users, does it degrade the performance of users served by operator Y? We answer this question by analyzing the average and instantaneous performances of the OOB operator considering both sub-6 GHz and mmWave bands, accounting for their corresponding channel characteristics. Specifically, we derive the ergodic sum-spectral efficiency achieved by the operators under round-robin scheduling. We also derive the outage probability and analyze the change in the SNR witnessed by an OOB user in the presence of the IRS using stochastic dominance theory. Surprisingly, even though the IRS is randomly configured from operator Y's point of view, the OOB operator still benefits from the presence of the IRS, witnessing a performance enhancement for free, in both sub-6 GHz and mmWave bands. This is because the IRS introduces additional paths between the transmitter and receiver, increasing the overall signal power arriving at the receiver and providing diversity benefits. We numerically illustrate our findings and conclude that an IRS is always beneficial to every operator, even when the IRS is deployed and controlled by only one operator to serve its own users.

Channel State Information Based User Censoring in Irregular Repetition Slotted Aloha

Irregular repetition slotted aloha (IRSA) is a massive random access protocol which can be used to serve a large number of users while achieving a packet loss rate (PLR) close to zero. However, if the number of users is too high, then the system is interference limited and the PLR is close to one. In this paper, we propose a variant of IRSA in the interference limited regime, namely Censored-IRSA (C-IRSA), wherein users with poor channel states censor themselves from transmitting their packets. We theoretically analyze the throughput performance of C-IRSA via density evolution. Using this, we derive closed-form expressions for the optimal choice of the censor threshold which maximizes the throughput while achieving zero PLR among uncensored users. Through extensive numerical simulations, we show that C-IRSA can achieve a 4$\times$ improvement in the peak throughput compared to conventional IRSA.

Does an IRS Degrade Out-of-Band Performance?

Intelligent reflecting surfaces (IRSs) were introduced in the literature in order to enhance the performance of the wireless systems. However, from a cellular service provider's point of view, a concern with the use of an IRS is its effect on out-of-band (OOB) quality of service. Specifically, if there are two operators, say X and Y, providing services in a given geographical area using non-overlapping frequency bands, and if operator X uses an IRS to optimally enhance the throughput of it's users, does the IRS degrade the performance of operator Y? We study this scenario by analyzing the ergodic sum-rates achieved by both the operators under round-robin scheduling. We also derive the complementary cumulative distribution function of the change in the effective channel gain at an OOB user with and without the IRS, which provides deeper insights into the effect of the IRS on the overall channel quality. Surprisingly, we find that even though the IRS is randomly configured from operator Y's point of view, the OOB operator still benefits from the presence of the IRS, witnessing a performance enhancement for free. This happens because the IRS introduces additional paths between the transmitter and receiver, increasing the overall signal power arriving at the receiver and providing diversity benefits. We verify our findings via numerical simulations, and conclude that an IRS is always beneficial to every operator, even when the IRS is deployed to optimally serve only one operator in the system.

Multi-Carrier Wideband OCDM-Based THz Automotive Radar

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a stand-alone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multi-carrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different range/bandwidths by assigning weights based on the Cramer-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates using our proposed combined estimation from multiple varied-attenuation THz frequencies.

Impact of Mobility on Downlink Cell-Free Massive MIMO Systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.