Considering an reconfigurable intelligent surface (RIS)-assisted underlay spectrum sharing network, our goal is to minimize an average symbol error probability (SEP) of a secondary user while adhering to an interference-outage constraint imposed by a primary user. We first derive an optimal rule for on-off power control at a secondary source and passive beamforming at the RIS to minimize the average SEP at a secondary destination. We derive novel analytical expressions for the probability density functions of the effective interference channel power gains at the primary receiver and primary interference-outage probability. Building upon those analyses, we subsequently propose two simpler, yet near-optimal rules for on-off power control and RIS passive beamforming with lower complexity. Finally, simulation results corroborate the efficacy of the proposed framework and show the impact of different system parameters on the SEP.

The codebook-based scheme for intelligent reflecting surfaces (IRSs) provides flexibility in controlling the training overhead. In it, the reflection pattern with the largest received signal strength is selected from a pre-specified codebook and configured at the IRS. We analyze a training scheme that exploits a novel trade-off between the powers allocated for selection pilots, which are used to select the reflection pattern, and the demodulation pilot, which is used to estimate the channel for demodulation. We develop a novel selection-aware estimator of the beamforming gain of the selected reflection pattern. We derive a tight bound for the achievable rate and an elegant closed-form expression for the beamforming gain. These account for the impact of imperfect channel estimates on the selection of the reflection pattern and the coherent demodulation of the data symbols. The proposed scheme achieves a higher rate than conventional schemes by allocating substantially different powers to the selection and demodulation pilots and data symbols.

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.

The codebook-based scheme for intelligent reflecting surfaces (IRSs) decouples the training and control signaling overheads from the number of IRS elements by selecting the IRS reflection pattern from a pre-specified codebook. We analyze the performance of a training scheme that exploits a novel trade-off between the powers allocated for selection pilots, which are used to select the reflection pattern, and the demodulation pilot, which is used for estimating the channel for demodulation. We develop a selection-aware linear minimum mean-square error estimator of the effective channel gain of the selected reflection pattern. When the direct link is blocked, we derive an elegant closed-form expression for the beamforming gain. When the direct link is present, which requires a different analysis, we derive a novel upper bound and insightful asymptotic expressions for the beamforming gain. We then present a novel expression for the achievable rate that accounts for the impact of noisy channel estimates on both selection of the reflection pattern and demodulation of data. We optimize the pilot and data powers and the codebook size. Our approach yields a significantly better rate than conventional schemes, and establishes the advantages of allocating substantially different powers to the selection and demodulation pilots and data.

In distributed massive multiple-input multiple-output (MIMO) systems, multiple geographically separated access points (APs) communicate simultaneously with a user, leveraging the benefits of multi-antenna coherent MIMO processing and macro-diversity gains from the distributed setups. However, time and frequency synchronization of the multiple APs is crucial to achieve good performance and enable joint precoding. In this paper, we analyze the synchronization requirement among multiple APs from a reciprocity perspective, taking into account the multiplicative impairments caused by mismatches in radio frequency (RF) hardware. We demonstrate that a phase calibration of reciprocity-calibrated APs is sufficient for the joint coherent transmission of data to the user. To achieve synchronization, we propose a novel over-the-air synchronization protocol, named BeamSync, to calibrate the geographically separated APs without sending any measurements to the central processing unit (CPU) through fronthaul. We show that sending the synchronization signal in the dominant direction of the channel between APs is optimal. Additionally, we derive the optimal phase and frequency offset estimators. Simulation results indicate that the proposed BeamSync method enhances performance by 3 dB when the number of antennas at the APs is doubled. Moreover, the method performs well compared to traditional beamforming techniques.

An intelligent reflecting surface (IRS) is a cost and energy-efficient solution to improve wireless system performance. Transmit antenna selection (AS) harnesses the benefits of multiple antennas with a smaller number of radio frequency (RF) chains. We focus on joint optimization of antenna subset and transmit beamforming at the transmitter (Tx) and passive beamforming at the IRS to maximize the receive signal power. We derive a closed-form optimal AS rule for a Tx and receiver (Rx) equipped with single RF chain each and ideal IRS. We analyze its performance with a correlated channel model and then extend it to non-ideal IRS. We also propose a simpler rule that significantly reduces the number of computations and pilots. For an Rx that performs maximal ratio combining, we propose a manifold optimization algorithm and a low-complexity selection rule. For a Tx with multiple RF chains, we propose a subset selection algorithm that yields a locally optimal solution and an alternating optimization algorithm that reduces complexity. Our simulations study the impact of estimation errors, discrete phase shifts, and channel correlation on the proposed selection rules, which perform better than the existing AS rules. They also show that the proposed low-complexity rules are near-optimal.

RadioWeaves network operates a large number of distributed antennas using cell-free architecture to provide high data rates and support a large number of users. Operating this network in an energy-efficient manner in the limited available spectrum is crucial. Therefore, we consider energy efficiency (EE) maximization of a RadioWeaves network that shares spectrum with a collocated primary network in underlay mode. To simplify the problem, we lower bound the non-convex EE objective function to form a convex problem. We then propose a downlink power allocation policy that maximizes the EE of the secondary RadioWeaves network subject to power constraint at each access point and interference constraint at each primary user. Our numerical results investigate the secondary system’s performance in interference, power, and EE constrained regimes with correlated fading channels. Furthermore, they show that the proposed power allocation scheme performs significantly better than the simpler equal power allocation scheme.

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signal-to-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.

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.

Intelligent reflecting surface (IRS), which uses passive reflective elements instead of active radio frequency (RF) chains, is a cost and energy-efficient solution to improve the wireless system performance. With a similar objective, transmit antenna selection (AS) reduces the number of RF chains at the base station while harnessing the benefits of multiple antennas. In our work, we focus on joint optimization of antenna subset and transmit beamforming at the base station, and passive beamforming at the IRS to maximize the receive signal power. For single AS, we first derive a closed-form optimal rule. We then propose a simpler AS rule, which significantly reduces the computational complexity and the number of pilot transmissions required. For a system with Nt antennas at the base station and N IRS elements, the optimal AS rule requires Nt+ NtN pilots. However, the proposed simpler rule requires only 2Nt+N pilots. For subset AS, we develop a manifold optimization based algorithm. To reduce its subset search complexity, which is exponential in the number of RF chains at the base station, we propose an alternating optimization based iterative algorithm. Our numerical results show that the proposed simpler AS rules are near optimal.

In underlay spectrum sharing, transmit antenna selection (TAS) improves the performance of a secondary system and helps it control the interference it causes to a primary system. TAS does so with a hardware complexity and cost comparable to a single antenna system. We present a novel and optimal joint TAS and continuous power adaptation rule for a practically relevant, less explored model in which the secondary transmitter knows only the statistics of channel gains from itself to one or more primary receivers. The rule minimizes the average symbol error probability (SEP) of the secondary system for an entire class of stochastic interference constraints. This general class subsumes the average interference constraint and its novel generalization, and the interference-outage constraint. We derive closed-form expressions for the transmit power and selected antenna. We then develop a general analysis of the optimal average SEP that applies to several widely-used fading models. We also present computationally-efficient approaches to determine the parameters that specify the optimal rule. Our comprehensive numerical results characterize the very different impacts of the interference constraint on both secondary and primary systems. They show that the optimal rule reduces the average SEP by two orders of magnitude compared to conventional approaches.

Cooperative relaying and multiple-input multiple-output (MIMO) transmission technologies exploit spatial diversity to improve the performance of the secondary users in an underlay cognitive radio network. We consider a MIMO cognitive relay network in which a secondary source and multiple relays have imperfect channel state information (CSI) of the interference links to the primary receiver. They sufficiently back-off their transmit powers on the basis of such CSI in order to adhere to an interference outage constraint. We propose an optimal relay and antenna selection scheme, which jointly selects a relay between the source and destination, a transmit antenna at the source, and a receive antenna at the destination to maximize the end-to-end signal-to-interference-plus-noise ratio (SINR) at the destination. To demonstrate the advantages of our proposed framework, we derive closed-form expression for the outage probability of the secondary network under non-identically distributed Rayleigh fading channels. We also derive an insightful expression for the asymptotic outage probability for high SINR and show that the diversity gain is lost when the interference power constraint is fixed. We then consider a practical scenario where the secondary users have only the mean channel power gains of the interference links. Under such CSI, we also derive an expression for the outage probability, and show that this can be used as a better performance/complexity tradeoff for high SINR.

For underlay spectrum sharing, transmit antenna selection is a low hardware complexity technique that can help the secondary system overcome the performance limitations imposed by the constraints on the interference it causes to a primary system. However, its efficacy depends on the channel state information (CSI) available to the secondary transmitter. We consider a practically appealing model in which the secondary transmitter has only statistical CSI about the channel gains from itself to the primary receiver and is subject to a general class of stochastic interference constraints. We derive an optimal and novel joint antenna selection and continuous power adaptation rule for it that minimizes the average symbol error probability (SEP) of the secondary system. We show that it has an intuitively appealing separable structure. We then analyze its average SEP. Our numerical results evaluate the impact of the interference constraint on both secondary and primary systems, and show that a judicious choice of the interference constraint and its parameters is needed as its impact on the secondary and primary systems can be very different.

In underlay spectrum sharing, the interference constraint limits transmissions by the secondary transmitter, which concurrently accesses the spectrum, to protect the primary user from excessive interference. Transmit antenna selection enables a secondary user to overcome the limitations imposed by the interference constraint using low-complexity hardware. We develop an optimal and novel joint antenna selection and power adaptation rule that minimizes the average symbol error probability (SEP) of a secondary user that is subject to two practically well-motivated constraints. The first is the less-studied but general interference-outage constraint, which limits the probability that the interference power at the primary receiver exceeds a threshold. The second constraint limits the peak transmit power of the secondary transmitter. We show that the optimal rule for the interference-outage constraint has a novel structure that is markedly different from the rules considered in the literature. We then present an insightful geometric interpretation of its structure. Using this, we also propose a practically amenable and near-optimal variant of the optimal rule called the linear rule, and analyze its performance. Our numerical results show that the optimal rule reduces the average SEP by one to two orders of magnitude compared to the rules in the literature.

Underlay spectrum sharing improves spectral utilization by allowing a secondary user to transmit concurrently with a primary user. However, the secondary user's performance is limited by the interference constraint that is imposed on it to protect the primary user. Transmit antenna selection overcomes this limitation with a hardware complexity comparable to a single-antenna system. We present a novel and optimal joint antenna selection and power adaptation rule for a secondary system that is subject to the practically motivated interference-outage constraint, which is more general than the widely studied peak interference constraint. The rule provably minimizes the average symbol error probability (SEP) of the secondary user. We show that it has a fundamentally different and novel structure compared to the ones studied in the literature. We present key geometric insights about its novel structure. We use these to propose a simpler, linearized, and near-optimal variant. Compared to the rules considered in the literature, the proposed rules reduce the average SEP by an order of magnitude.

Transmit antenna selection is a low-complexity multiple-antenna technique that exploits spatial diversity using only one radio frequency chain. We investigate it for an underlay cognitive radio system that operates in the presence of multiple primary receivers and is subject to a constraint on the interference outage it causes at any of the primary receivers. The selection is based on a practically motivated and general partial channel state information (CSI) model in which the secondary transmitter (STx) only knows the channel power gains to a subset of the primary receivers. We derive a novel and general antenna selection rule that provably minimizes the symbol error probability (SEP) of the secondary system. We also derive insightful analytical expressions for its average SEP and interference-outage probability. These apply to a general class of channel fading models and any number of transmit and receive antennas, and include the special cases in which the STx knows channel power gains of all or none of the primary receivers. Our numerical results bring out a new insensitivity of the average SEP of the optimal rule to the interference power threshold when the CSI available is partial.

Transmit antenna selection (TAS) is a technique that achieves better performance than a single antenna system while using the same number of radio frequency chains. We propose a novel TAS rule called the λ-weighted interference indicator rule (LWIIR). We prove that for the general class of fading models with continuous cumulative distribution functions, LWIIR achieves the lowest average symbol error probability (SEP) among all TAS rules for an underlay cognitive radio system that employs binary power control and is subject to the interferen-ceoutage constraint. This constraint imposes a limit on the probability that the interference power at the primary exceeds a threshold. It is a generalization of the widely studied peak interference constraint. We then derive the average SEP of LWIIR. The insightful performance analysis applies to any number of transmit and receive antennas and to many constellations. We also analyze the practical scenario in which the secondary transmitter has imperfect information of the channel gains from itself to the secondary and primary receivers. We show that the imperfections in these two sets of channel gains have different impacts on the system. Our benchmarking shows that LWIIR outperforms many selection rules considered in the literature.

Transmit antenna selection (TAS) is a low hardware complexity multiple antenna technique that exploits spatial diversity to improve the performance of an interference-constrained cognitive radio (CR) system. In the underlay access mode of CR, the choice of the transmit antenna depends on the link between the secondary transmitter (STx) and its receiver, the interference link from the STx to the primary receiver (PRx), and also the interference constraint imposed on the CR system. We propose a novel selection rule called the lambda-weighted interference indicator rule for an underlay CR system that is subject to the interference-outage probability constraint, which constrains the probability that the interference power at the PRx exceeds a threshold. This general constraint also encompasses the widely studied peak interference power constraint. We prove that the proposed rule minimizes the average symbol error probability (SEP) of the CR system. It applies to a general class of fading models with continuous probability distributions and many constellations, and outperforms the many selection rules studied in the literature. We analyze its SEP, and present several insights about its novel structure and behavior.

The performance of an underlay cognitive radio (CR) system, which can transmit when the primary is on, is curtailed by tight constraints on the interference it can cause to the primary receiver. Transmit antenna selection (AS) improves the performance of underlay CR by exploiting spatial diversity but with less hardware. However, the selected antenna and its transmit power now both depend on the channel gains to the secondary and primary receivers. We develop a novel Chernoff-bound based optimal AS and power adaptation (CBBOASPA) policy that minimizes an upper bound on the symbol error probability (SEP) at the secondary receiver, subject to constraints on the average transmit power and the average interference to the primary. The optimal antenna and its power are presented in an insightful closed form in terms of the channel gains. We then analyze the SEP of CBBOASPA. Extensive benchmarking shows that the SEP of CBBOASPA for both MPSK and MQAM is one to two orders of magnitude lower than several ad hoc AS policies and even optimal AS with on-off power control.

Transmit antenna selection (AS) is a popular, low hardware complexity technique that improves the performance of an underlay cognitive radio system, in which a secondary transmitter can transmit when the primary is on but under tight constraints on the interference it causes to the primary. The underlay interference constraint fundamentally changes the criterion used to select the antenna because the channel gains to the secondary and primary receivers must be both taken into account. We develop a novel and optimal joint AS and transmit power adaptation policy that minimizes a Chernoff upper bound on the symbol error probability (SEP) at the secondary receiver subject to an average transmit power constraint and an average primary interference constraint. Explicit expressions for the optimal antenna and power are provided in terms of the channel gains to the primary and secondary receivers. The SEP of the optimal policy is at least an order of magnitude lower than that achieved by several ad hoc selection rules proposed in the literature and even the optimal antenna selection rule for the case where the transmit power is either zero or a fixed value.

In the underlay mode of cognitive radio, secondary users are allowed to transmit when the primary is transmitting, but under tight interference constraints that protect the primary. However, these constraints limit the secondary system performance. Antenna selection (AS)-based multiple antenna techniques, which exploit spatial diversity with less hardware, help improve secondary system performance. We develop a novel and optimal transmit AS rule that minimizes the symbol error probability (SEP) of an average interference-constrained multiple-input-single-output secondary system that operates in the underlay mode. We show that the optimal rule is a non-linear function of the power gain of the channel from the secondary transmit antenna to the primary receiver and from the secondary transmit antenna to the secondary receive antenna. We also propose a simpler, tractable variant of the optimal rule that performs as well as the optimal rule. We then analyze its SEP with \tL transmit antennas, and extensively benchmark it with several heuristic selection rules proposed in the literature. We also enhance these rules in order to provide a fair comparison, and derive new expressions for their SEPs. The results bring out new inter-relationships between the various rules, and show that the optimal rule can significantly reduce the SEP.

In the underlay mode of cognitive radio, secondary users can transmit when the primary is transmitting, but under tight interference constraints, which limit the secondary system performance. Antenna selection (AS)-based multiple antenna techniques, which require less hardware and yet exploit spatial diversity, help improve the secondary system performance. In this paper, we develop the optimal transmit AS rule that minimizes the symbol error probability (SEP) of an average interference-constrained secondary system that operates in the underlay mode. We show that the optimal rule is a non-linear function of the power gains of the channels from secondary transmit antenna to primary receiver and secondary transmit antenna to secondary receive antenna. The optimal rule is different from the several ad hoc rules that have been proposed in the literature. We also propose a closed-form, tractable variant of the optimal rule and analyze its SEP. Several results are presented to compare the performance of the closed-form rule with the ad hoc rules, and interesting inter-relationships among them are brought out.