Outage analysis of intelligent reflecting surface (IRS) assisted wireless energy transfer to multiple users based on round-robin scheduling strategy under imperfect channel state information (CSI), spatial correlation and optimal phase configuration is presented. For this, we develop a statistical model based on Gamma distribution for a random variable that is a function of reflected channels via IRS. This analysis accounts for the correlation among the IRS elements and also the correlation that results due to common source to IRS channels for different users. Through numerical results, we show the robustness of our system against imperfect CSI, and validate the tightness of our modelling and analysis. We quantify the improvement in outage that can be achieved by systems that employ optimal phase shift configuration at IRS over systems that employs either random or equal phase shift programming. We also show that even under estimation errors, more users can be supported by increasing the number of IRS elements. We also observe that estimation errors induce a marginal degradation in outage performance.

We quantify the impact of channel outdatedness and pilot contamination on the achievable spectral efficiency (SE) of uplink massive multiple input multiple output non-orthogonal multiple access (mMIMO-NOMA) system. Specifically, we compute novel closed-form expressions for achievable SE (i) for both ZF and MR decoders based on outdated channel estimates obtained using pilot sharing-based estimation schemes, namely, Scheme-I and Scheme-S, and (ii) for ZF decoder based on predicted channel obtained using Wiener linear predictor. The analysis accounts for time-variations in channel due to Doppler shift, correlation due to pilot sharing and imperfect successive interference cancellation. We formulate and solve optimization problems for max-min and proportional fairness power control using convex programming. We also study equal and inversion power control strategies. Intriguingly, among equal, inversion and max-min power control under channel aging, the ZF decoder based on Scheme-S (i) gives the highest per-user SE with max-min power control for smaller values of normalized Doppler shift$ (f_{D} T_{S}) $and with inversion power control for larger values of$ f_{D} T_{S}$, and (ii) gives the highest minimum user SE with max-min power control. Proportional fairness provides higher per-user SE than max-min power control while maintaining fairness to a significant extent.

Intelligent reflecting surface (IRS), which is made up of passive reflective elements and can control the phase of the incident signal, and antenna selection (AS) can be combined to yield a cost-and energy-efficient wireless technology for the Internet of Things (IoT) system. For an IRS-assisted IoT system with one fusion node and multiple sensor nodes, we develop a jointly optimal AS and passive beamforming rule that maximizes the sum data rate. In it, the number of required channel estimations increases linearly with the number of sensor nodes. Additional novel contributions include a closed-form AS and passive beamforming rule, which maximizes the sum of absolutes of channel gains while significantly reducing computational complexity. To further simplify, we propose a new channel acquisition procedure for which the number of channel estimations is independent of the number of sensor nodes. Our simulations show that the optimal rule yields up to 13.6 x and 6 x higher rates than the maximum channel gain based and block coordinate descent based algorithms, respectively. Furthermore, they show that the simpler AS rule yields up to 12.4x gain compared to other AS rules in the literature and is robust to estimation errors.

We analyze the uplink (UL) of a cell-free massive multiple input multiple output (mMIMO) based underlay network over limited capacity fronthaul in which the secondary users (SUs) transmit under an interference outage probability (IOP) constraint and the impact of quantization is modeled based on Bussgang decomposition. We first develop novel statistical models for the squared$l_{2}$-norm (SLN) of the true and the quantized channel estimates (QCE) based on Gamma distribution and find the corresponding shape and scale parameters. We then derive new expressions for mean SU transmit power and correlation between SLN of true and QCE. We also develop new expressions for IOP and UL spectral efficiency (SE) of SUs and primary users (PUs) as a function of the Bussgang gain and quantization noise variance which in turn depends on the number of quantization levels. We show that to keep IOP fixed, larger back-off in SU transmit power is required when fewer bits are employed for quantization. Furthermore, the likelihood of PUs or SUs achieving a higher SE increases as number of quantization bits increases and 4 bits are sufficient to obtain same performance as an infinite capacity fronthaul. Impact of spatial correlation on SE is also elucidated.

We investigate the use of cell-free massive multiple input multiple output (MIMO) systems in serving unmanned aerial vehicles (UAVs) and ground user equipments (GUEs) in underlay spectrum access mode by considering UAVs as secondary users and GUEs as primary users. We derive a novel lower bound on complement of interference violation probability (IVP). This result helps determine the power margin required at secondary access points (APs) to meet the IVP constraint. We deduce new analytical expressions for spectral efficiency (SE) and bit error rate (BER) of UAVs and GUEs. This analysis entails finding expectation of secondary AP transmit power based on order statistics of the strongest interference channel between secondary APs and GUEs. We prove that for lower pilot power, a larger power margin is required to keep IVP fixed. We show that for a given IVP, the larger the interference threshold is, the greater (lower) the likelihood for the UAVs (GUEs) to obtain higher SE is. For a given interference threshold, probability that UAVs attain higher SE increases as IVP increases for a fixed number of secondary APs ($N$) or as$N$increases for a given IVP. Impact of interference threshold and height of UAVs on BER is elucidated.

We elucidate feasibility of wireless energy transfer (WET) with the help of an intelligent reflecting surface (IRS). We consider a source equipped with multiple antennas and a single radio-frequency (RF) chain. We propose a low complexity rule that does joint antenna selection (AS) at source and passive beamforming at IRS. We derive new expressions for probability of outage in WET under perfect and estimated channel knowledge and for both single and multiple users. We derive intuitive expressions for outage probability with large number of IRS elements and for line-of-sight scenarios. For a system with$M$antennas at source and$N$passive elements at IRS, we show that diversity order equals$M+N$. Extensions to subset AS, discrete phase-shift design, and performance under limited scattering are also presented. Our numerical results show that the proposed AS rule yields near-optimal performance while requiring only$M+N$pilot transmissions compared to the$M+MN$pilot transmissions required by the optimal AS rule in literature. We elucidate that we can trade-off active RF chains at source with passive elements at IRS to obtain improved performance both in terms of outage probability and power transfer efficiency. And 3-bit IRS is sufficient to obtain good performance at lower complexity.

We investigate the viability of wireless energy transfer (WET) to multiple sensors using massive number of base station (BS) antennas based on estimates obtained through different uplink pilot signaling strategies, namely, orthogonal and shared. For the aforementioned strategies, we derive novel upper bounds on probability of outage in WET referred to as the probability that any sensor node fails to harvest a certain minimum amount of energy Euthat it needs to send uplink pilots plus the energy Epthat it needs to process its main tasks. We show how number of BS antennas scales with the number and position of sensor nodes, array transmit energy, channel estimation errors and nature of pilot signaling strategy employed. We prove when the sensor nodes are all equidistant relative to the BS, shared strategy gives identical outage probability as obtained through orthogonal strategy. However, when they are placed at different distances, shared strategy gives poorer outage performance than orthogonal strategy. To address this, we propose a simple location-dependent clustering and hybrid pilot assignment algorithm and also derive the corresponding probability of outage in WET. The proposed strategy elucidates an interesting trade-off between the outage performance that can be obtained and resources that must be spent on channel estimation.

We investigate the viability of downlink (DL) unicast transmission from a base station (BS) with massive number of antennas jointly to support machine‐centric communication among the internet‐of‐things (IoT) devices and human‐centric communication among the mobile terminals. Specifically, we derive a new expression for the DL sum spectral efficiency (SE) for the IoT devices with maximum ratio precoding when channel estimates are acquired via the proposed distance‐dependent grouping based hybrid pilot assignment strategy. We also derive a new DL sum SE expression for IoT devices based on non‐orthogonal pilot assignment. We show that under statistical channel inversion based power control at the BS, the proposed strategy yields the highest sum SE and can serve the largest number of IoT devices when compared against orthogonal, non‐orthogonal and distance‐independent grouping based hybrid strategies. Furthermore, we also analyse the DL sum SE of mobile terminals in the presence of IoT devices when every mobile is given an orthogonal pilot and prove that the sum SE is independent of the pilot assignment strategy employed by the IoT devices. We also obtain the max–min rate for the IoT devices and prove that it is independent of the pilot assignment strategy used by the devices.

We investigate the viability of wireless energy transfer (WET) to multiple sensor nodes using massive number of base station (BS) antennas based on channel estimates obtained through two different uplink pilot signaling strategies, namely orthogonal and shared. The objective is to ascertain whether every sensor node present in the network can harvest a certain minimum amount of energy Euthat it needs to send uplink pilots plus the energy Epthat it needs to process its main tasks. To this end, for both the schemes, we derive novel upper bounds on the probability of outage in WET referred to as the probability that any sensor node fails to harvest Eu+ Epamount of energy. We show how the number of BS antennas scales with respect to the number of sensor nodes, the position of the sensor nodes relative to the BS, the array transmit energy, the channel estimation errors and the nature of pilot signaling scheme employed while maintaining probability of outage at a fixed level. We prove that when the sensor nodes are placed at equal distances from the BS, shared scheme gives better or at least as good an outage performance as orthogonal scheme, thereby saving resources spent on channel training. However, when they are placed at different distances, then by deploying more antennas in the array, shared gives the same performance as orthogonal. Furthermore, even with multiple sensor nodes and for both the schemes, substantial savings of radiated energy and extension in the range of WET is obtained.

In massive MIMO systems, which rely on uplink pilots to estimate the channel, the time interval between pilot transmissions constrains the length of the downlink. Since switching between up- and downlink takes time, longer downlink blocks increase the effective spectral efficiency. We investigate the use of low-complexity channel models and Kalman filters for channel prediction, to allow for longer intervals between the pilots. Specifically, we quantify how often uplink pilots have to be sent when the downlink rate is allowed to degrade by a certain percentage. To this end, we consider a time-correlated channel aging model, whose spectrum is rectangular, and use autoregressive moving average (ARMA) processes to approximate the time-variations of such channels. We show that ARMA-based predictors can increase the interval between pilots and the spectral efficiency in channels with high Doppler spreads. We also show that Kalman prediction is robust to mismatches in the channel statistics.

We illustrate potential benefits of using massive antenna arrays for wireless energy transfer (WET). Specifically, we analyze probability of outage in WET over fading channels when a base station (BS) with multiple antennas beamforms energy to a wireless sensor node (WSN). Our analytical results show that by using massive antenna arrays, the range of WET can be increased for a given target outage probability. We prove that by using multiple-antenna arrays at the BS, a lower downlink energy is required to get the same outage performance, resulting in savings of radiated energy. We show that for energy levels used in WET, the outage performance with least-squares or minimum mean-square-error channel estimates is the same as that obtained based on perfect channel estimates. We observe that a strong line-of-sight component between the BS and WSN lowers outage probability. Furthermore, by deploying more antennas at the BS, a larger energy can be transferred reliably to the WSN at a given target outage performance for the sensor to be able to perform its main tasks. In our numerical examples, the RF power received at the input of the sensor is assumed to be on the order of a mW, such that the rectenna operates at an efficiency in the order of 50%.

Adapting the power of secondary users (SUs) while adhering to constraints on the interference caused to primary receivers (PRxs) is a critical issue in underlay cognitive radio (CR). This adaptation is driven by the interference and transmit power constraints imposed on the secondary transmitter (STx). Its performance also depends on the quality of channel state information (CSI) available at the STx of the links from the STx to the secondary receiver and to the PRxs. For a system in which an STx is subject to an average interference constraint or an interference outage probability constraint at each of the PRxs, we derive novel symbol error probability (SEP)-optimal, practically motivated binary transmit power control policies. As a reference, we also present the corresponding SEP-optimal continuous transmit power control policies for one PRx. We then analyze the robustness of the optimal policies when the STx knows noisy channel estimates of the links between the SU and the PRxs. Altogether, our work develops a holistic understanding of the critical role played by different transmit and interference constraints in driving power control in underlay CR and the impact of CSI on its performance.

In an underlay cognitive radio (CR) system, a secondary user (SU) can transmit even when the primary is on but under stringent constraints on the interference that it causes at the primary receiver (PRx). The interference constraint fundamentally influences and changes how the SU transmits. We develop a novel and optimal transmit power policy for an SU that minimizes its symbol error probability (SEP) when it is subject to two practically motivated constraints, namely, a peak transmit power constraint and an interference outage probability constraint. We derive new expressions for the SEP of the optimal transmit power policy for MPSK. Our results bring out the impact of several system parameters such as the peak transmit power, the target outage probability, the interference threshold, and the constellation size on the SEP of the CR system.

Transmit antenna selection (AS) has been adopted in contemporary wideband wireless standards such as Long Term Evolution (LTE). We analyze a comprehensive new model for AS that captures several key features about its operation in wideband orthogonal frequency division multiple access (OFDMA) systems. These include the use of channel-aware frequency-domain scheduling (FDS) in conjunction with AS, the hardware constraint that a user must transmit using the same antenna over all its assigned subcarriers, and the scheduling constraint that the subcarriers assigned to a user must be contiguous. The model also captures the novel dual pilot training scheme that is used in LTE, in which a coarse system bandwidth-wide sounding reference signal is used to acquire relatively noisy channel state information (CSI) for AS and FDS, and a dense narrow-band demodulation reference signal is used to acquire accurate CSI for data demodulation. We analyze the symbol error probability when AS is done in conjunction with the channel-unaware, but fair, round-robin scheduling and with channel-aware greedy FDS. Our results quantify how effective joint AS-FDS is in dispersive environments, the interactions between the above features, and the ability of the user to lower SRS power with minimal performance degradation.

In underlay cognitive radio (CR), a secondary user (SU) can transmit concurrently with a primary user (PU) provided that it does not cause excessive interference at the primary receiver (PRx). The interference constraint fundamentally changes how the SU transmits, and makes link adaptation in underlay CR systems different from that in conventional wireless systems. In this paper, we develop a novel, symbol error probability (SEP)-optimal transmit power adaptation policy for an underlay CR system that is subject to two practically motivated constraints, namely, a peak transmit power constraint and an interference outage probability constraint. For the optimal policy, we derive its SEP and a tight upper bound for MPSK and MQAM constellations when the links from the secondary transmitter (STx) to its receiver and to the PRx follow the versatile Nakagami-m fading model. We also characterize the impact of imperfectly estimating the STx-PRx link on the SEP and the interference. Extensive simulation results are presented to validate the analysis and evaluate the impact of the constraints, fading parameters, and imperfect estimates.