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). Due to the B-SP, we argue that 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.

We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the IRS phase shifters fail to form a beam at the desired user equipment (UE) over the complete bandwidth (BW) allotted to that UE, in turn reducing the system throughput. This paper proposes a distributed IRS design that aids in naturally combating the SW/BSP effects by parallelizing the spatial delays across the multiple IRSs. Specifically, we propose to split a single large IRS into multiple smaller IRSs and distribute them over the geographical area. Then, by specifying the maximum permissible number of elements at each IRS, we ensure that the B-SP remains within a specified tolerance level. Finally, we derive the sum-rate and show that the proposed solution yields a nonzero rate over the whole BW of operation and circumvents the SW/B-SP effects. We verify our findings numerically and illustrate the low complexity of the proposed method compared to state-of-the-art techniques.

Intelligent reflecting surfaces (IRSs) are envisioned to enhance the performance of mmWave wireless systems. In practice, multiple mobile operators (MO) coexist in an area and provide simultaneous and independent services to user-equipments (UEs) on different frequency bands. Then, if each MO deploys an IRS to enhance its performance, the IRSs also alter the channels of UEs of other MOs. In this context, this paper addresses the following questions: can an MO still continue to control its IRS independently of other MOs and IRSs? Is joint optimization of IRSs deployed by different MOs and inter-MO cooperation needed? To that end, by considering the mmWave bands, we first derive the ergodic sum spectral efficiency (SE) in a 2-MO system for the following schemes: 1) joint optimization of an overall phase angle of the IRSs with MO cooperation, 2) MO cooperation via time-sharing, and 3) no cooperation between the MOs. We find that even with no cooperation between the MOs, the performance of a given MO is not degraded by the presence of an out-of-band (OOB) MO deploying and independently controlling its own IRS. On the other hand, the SE gain obtained at a given MO using joint optimization and cooperation over the no-cooperation scheme decreases inversely with the number of elements in the IRS deployed by the other MO. We generalize our results to a multiple MO setup and show that the gain in the sum-SE over the no-cooperation case increases at least linearly with the number of OOB MOs. Finally, we numerically verify our findings and conclude that every MO can independently operate and tune its IRS; cooperation via optimizing an overall phase only brings marginal benefits in practice.

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 (UEs), does it degrade the performance of UEs served by operator Y? We answer this by analyzing the average and instantaneous performances of the OOB operator considering both sub-6 GHz and mmWave bands. Specifically, we derive the ergodic sum-SE achieved by the operators under round-robin scheduling. We also derive the outage probability and analyze the change in the SNR caused by the IRS at an OOB UE, 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 UE and providing diversity benefits. Finally, we show that the use of opportunistic scheduling schemes can further enhance the benefit of the uncontrolled IRS at OOB UEs. We numerically illustrate our findings and conclude that an IRS is always beneficial to every operator, even when the IRS is deployed & controlled by only one operator.

In this paper, we consider intelligent reflecting surface (IRS) assisted opportunistic communications (OC), and present a comparative analysis of the system throughput over independent and identically distributed (i.i.d.) and line-of-sight (LoS) channels. In the system we consider, the phase configuration of an N-element IRS is set randomly and independently over time. Then, with high probability, at least one of the K users in the system will see an effective channel with IRS that is close to the beamforming configuration. The BS opportunistically schedules this user for data transmission, thereby avoiding deep fade events. We analyze the convergence rate of the opportunistic throughput to the beamforming throughput as a function of N and K, and derive the rate scaling laws in both i.i.d. and LoS channels. We show that i.i.d. channels require a much larger K compared to LoS channels in order to achieve a performance comparable to the beamforming configuration. Further, the average SNR of the scheduled user under i.i.d. channels scales as $\mathcal{O}\left( N \right)$, while that under LoS channels scales as $\mathcal{O}\left( {{N^2}} \right)$. We corroborate our analysis, and provide interesting insights, via numerical simulations.

Intelligent reflecting surfaces (IRSs) are envisioned as a key enabler for next generation wireless communications due to their capability to boost system performance without requiring additional bandwidth or transmit power. However, estimating the cascaded channels between the user equipment (UE) through the IRS to the base station (BS) is a major bottleneck in IRS-assisted systems. In this work, we present a novel method to estimate all the cascaded IRS channels in a multiple-IRS (called as distributed IRS in this paper) aided mmWave massive MIMO system. We exploit the inherent structure in mmWave channels to reformulate the channel estimation problem as one of direction of arrival (DoA) and departure (DoD) estimation in the cascaded channel. In turn, this allows us to use subspace based methods from array processing to develop a joint ESPRIT-MUSIC algorithm for estimating the DoA at the BS and the DoD from the UE. An attractive feature of the scheme is its low pilot overhead requirement: unlike existing methods, the number of pilot symbols does not scale with number of IRS elements or the number of antennas at the BS; it only depends on the number of IRSs deployed and the number of antennas at the UE. We compare our method against state-of-the-art methods, and numerically illustrate its superior performance and robustness to the number of IRSs in the system.