The new generation Wireless Sensor Networks (WSN) require a Reconfigurable Intelligent Surface (RIS)-aided spectrum and energy-efficient communication system. This paper proposes a less complex, single-substrate, biasing integrated 4-bit RIS aided communication system operating at 5.8 GHz (ISM band) focused on WSN and Internet of Things (IoT) networks. The unit cell consists of concentric circular loops, cross-dipoles, and a rectangular biasing grid. Varactor diodes are used to achieve a phase range of 337.5° for 4-bit operation. The average return loss at 5.8 GHz is 2.92 dB for both TE and TM polarizations. To experimentally demonstrate the purpose of RIS, an 8 × 8 array is designed and fabricated. The beam steering property of the RIS is verified by simulation and measurement, achieving a beam scanning range of -30° to 30°. RIS is linearly dual polarized with a maximum angular stability of 40°. The increase in the received signal-to-noise ratio (SNR) for normal and oblique incidence is 11 dB and 7 dB, respectively. Channel reciprocity, bit error rate (BER) and image communication with the proposed RIS are verified, confirming it’s suitability for fast-growing networks.

This article proposes a novel hardware-efficient QR algorithm and its VLSI architecture to compute the eigenvalue decomposition (EVD) of symmetric matrices. By replacing the computationally expensive matrix multiplication with matrix transposition, the proposed architecture achieves a significant reduction in latency as well as area compared to the conventional QR architecture. We prove the convergence of the proposed algorithm through mathematical analysis and study its convergence rate through numerical simulations. We implemented both conventional and proposed QR architectures on both field-programmable gate array (FPGA) and application specific integrated circuit (ASIC) platforms. Implementation results on the ZCU104 Ultrascale+ FPGA demonstrate a 51% reduction in computation time and a 50% decrease in DSP usage with the proposed algorithm. Furthermore, ASIC synthesis results also demonstrate a significant reduction in computation time compared to the conventional algorithm.

Intelligent reflecting surfaces (IRSs) can reconfigure the propagation environment by controlling the phases of the reflected signals. Due to the dense arrangement of IRS elements on the surface, they are spatially-correlated. This paper analyzes the diversity order, symbol error probability (SEP), outage probability (OP), and ergodic capacity of IRS-assisted communication link between an access point (AP) and a user under correlated Rayleigh fading. Since it is infeasible to obtain a tractable closed-form expression for the distribution of instantaneous signal-to-noise-ratio (SNR), the analysis is challenging. Novel SNR bounds, which are more amenable to analysis, are proposed to circumvent this problem. We show that the diversity order is upper bounded by the minimum of the ranks of AP-IRS and IRS-user channel correlation matrices, and lower bounded by the cardinality of the subset of IRS elements with full-rank AP-IRS and IRS-user channel correlation matrices. The analyses is extended to the case of a multi-antenna AP having a line-of-sight channel to IRS and we show that the diversity order corresponds to the rank of IRS-user channel covariance matrix. From simulations, we see that the proposed SEP and OP approximations are very accurate, and SEP curve slopes approach the theoretical diversity order as SNR increases.

In user equipment (UE)-centric cell-free massive MIMO (CF-mMIMO), a UE is jointly served by a subset of access points (APs) in the network, which are connected to a central processing unit (CPU). Due to the limited capacity of fronthaul (FH) links between APs and the CPU, FH packets experience delay. The FH network delay for a protocol data unit (PDU) from a UE is the maximum delay across the FH links from all serving APs. This occurs because the CPU can detect the PDU only after receiving the corresponding FH packets from all serving APs. In this paper, we propose a novel analytical framework to study the FH network delays. First, the queuing of FH packets at individual APs is modeled to derive the distributions of delays from the serving APs. Since these delays are correlated, an exact analysis is infeasible and bounds are derived instead. We then extend the analysis to the case when the buffers at the APs are of finite size. The simulation results show that the proposed bounds are tight and that under high network loads, FH network delay is minimized when only a small subset of nearby APs is selected to serve each UE.

Critical machine-type communication (cMTC) supports mission-critical links that must meet ultra-reliable, low-latency (URLLC) requirements, typically carrying short payloads under hard deadlines. Cell-free massive MIMO (CF-mMIMO) is capable of supporting these stringent requirements. In this work, the uplink fronthaul (FH) delays in CF-mMIMO and small cell (SC) networks are analyzed under cMTC traffic. In CF-mMIMO, where each device is served by all access points (APs), the FH delay is governed by the slowest FH link among the serving APs. In contrast, the SC network FH delay depends only on the serving AP. The proposed analytical framework takes into account random protocol data unit (PDU) generation at a cMTC device, random transmission times, and queuing effects over finite-capacity FH links. The interrupted Poisson process (IPP) is employed to capture the alternating active and inactive modes of a cMTC device. Closed-form expressions for the cumulative distribution function of FH delay are derived using the spectral factorization method and validated through simulations. Results show that CF-mMIMO achieves lower FH delay than SC networks due to higher spectral efficiency (SE), even at a higher FH traffic rate. However, under heavy load, SC networks outperform CF-mMIMO despite its SE advantage.

Intelligent reflecting surfaces (IRSs) are a promising technology to enhance communication system performance as they can reflect signals in a manner that the reflected signals combine coherently at the receiver. Since many IRS elements are often closely spaced on the surface, the channel gains between IRS elements are correlated. An exact analysis of diversity order and symbol error probability (SEP) is infeasible as the distribution of the instantaneous signal-to-noise ratio (SNR) has no tractable, closed-form expression. We use SNR bounds to circumvent this issue and show that for an IRS-assisted system with no direct link between the transmitting access point (AP) and receiving user, the diversity order is upper bounded by the minimum rank among the AP-IRS and IRS-user correlation matrices and lower bounded by the maximum number of IRS elements with full-rank correlation matrices for the AP-IRS and IRS-user links. Using the SNR bounds, we also derive SEP bounds. While the SEP lower bound is seen to be tight, the SEP upper bound is tight only when the number of IRS elements is small.

In the uplink of cell-free massive MIMO (CF-mMIMO), the data from user equipment (UE) reaches the central processing unit (CPU) via all the access points (APs), whereas it is only via a single serving AP in a small-cell (SC) network. Due to the finite capacities of fronthaul (FH) links from APs to CPU, packet transfer over them leads to queuing delays. Thus, the FH network delay in CF-mMIMO is the maximum of the FH delays on the links from all the APs to the CPU. However, the FH network delay in the SC network is only the FH delay on the link from the serving AP. We analyze the FH network delays in both the networks by modeling the queuing of FH packets at any AP. This yields tractable, closed-form expressions for the probability distributions of the delays and conditions for the stability of the queues at APs, which in turn yields conditions for the stability of the networks. Through extensive simulations, we validate the analytical results and see that FH delays are significantly lower in CF-mMIMO compared to the SC network. Furthermore, the FH delays in SC networks are impacted more by variations in system parameters compared to those in CF-mMIMO.

Holographic MIMO (HMIMO) is a promising technology for next-generation cellular systems, wherein many antennas are arranged in a compact space at sub-wavelength spacing. We optimize the transmit precoding matrices in the downlink of an HMIMO cellular system using a channel model compliant with electromagnetic wave propagation. Exploiting the structure of the HMIMO channel matrix, we reformulate the sum-rate maximization problem in the wavenumber domain, and is solved using the weighted minimum mean squared error (WMMSE) algorithm. The proposed algorithm achieved the same sum-rate as directly applying the WMMSE algorithm but at much lower computational complexity. A statistical channel state information-based precoding algorithm is also developed. It performed poorly in isotropic scattering environments but achieved sum-rates comparable to the WMMSE algorithm in non-isotropic scattering environments. The sum-rates of all the algorithms increased as antenna spacing decreased for a fixed antenna array form factor, which demonstrates the potential of HMIMO.

In orthogonal time frequency space (OTFS) modulation, information symbols are multiplexed at the transmitter and coherently detected at the receiver in the delay-Doppler (DD) domain. The receiver requires DD domain channel gains for coherent detection and to enable channel estimation, a pilot symbol is transmitted embedded among the information symbols with sufficient guard intervals along the delay and Doppler axes. We show that the DD domain channel estimation problem can be transformed into a direction-of-arrival (DoA) estimation problem using a uniform linear array. This allows us to use state-of-the-art algorithms for DoA estimation such as off-grid sparse Bayesian inference (OG-SBI) and multiple signal classification (MUSIC) algorithms. We show that OG-SBI achieves lower mean square error and symbol error probability compared to the existing least-squares, SBI, and correlation algorithms. MUSIC is seen to work well when the pilot power is high. These are also the case when interference due to reduced guard interval is present.

Latency is the one of the critical performance metrics for 5G and beyond mobile networks, particularly for ultra-reliable and low-latency communications (URLLC). In URLLC applications, it is required that the transmitted packets reach the destination within a certain time and the packets that are unable to meet this strict latency requirement will be discarded. In this paper, we compute the waiting time in the packetized fronthaul at the Ethernet switch and compute packet loss rate (PLR) incurred due to the inability of the transmitted packets to meet the FH latency threshold. In addition, we derive the tractable closed-form solution for the waiting time distribution and verify it with simulation results. Our results show that PLR is affected mainly by packet size, spectral efficiency, switch speed and arrival rate.

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies for 5G as they improve radio performance while lowering the cost of operation. In a C-RAN system with massive MIMO-based remote radio units (RRUs), fronthaul is often the bottleneck in practice due to its finite capacity. To reduce the capacity requirements on fronthaul, precoding is done at the RRUs. In this paper, we maximize the energy efficiency (EE) of such a system by optimizing the transmit powers while explicitly incorporating the capacity constraints on fronthaul. We develop a successive convex approximation (SCA) algorithm, which is guaranteed to converge to a local optimum. Towards this, we propose novel bounds on the user rate function, which facilitates a convex approximation of the EE maximization problem. The convex problem is solved in each SCA iteration through Dinkel-bach's algorithm and dual decomposition. Numerical results show that the proposed algorithm significantly improves EE compared to the case with no power control and outperforms the weighted minimum mean square error algorithm.

Cell-free massive multiple-input multiple-output (MIMO) is a promising cellular technology in which a large number of distributed access points (APs) jointly serve a small number of user equipments (UEs). In this work, we investigate the impact of the spatial distribution of APs on the performance of a cell-free massive MIMO system, considering different downlink power control policies. Further, we analyze the performance for the case where only subsets of all APs serve the individual UEs: this scenario has lower backhaul requirements and associated CAPEX/OPEX costs. In this framework, we first propose a novel, tractable approximation for the average spectral efficiency (SE) of the transmission to a UE conditioned on the estimated channel gains. This approximation is then used to develop different downlink power control policies. Further, we extend the policies to the scenario when power control is coordinated only among subsets of APs and not across the subsets. Through extensive system-level simulations, we evaluate the improvement in SE by spreading out the APs and the SE loss when a subset of all APs serve a UE and coordination across subsets is absent.

With the emergence of cloud radio access network (C-RAN) architecture, latency in fronthaul (FH) network is a critical performance metric especially for ultra-reliable and low-latency communication applications. The stringent FH capacity and latency requirements of C-RAN can be relaxed by offloading some baseband functionalities to remote radio unit (RRU), referred to as functional splitting. This allows packetized FH network solutions such as ubiquitous Ethernet. In this paper, we calculate the FH latency in the uplink of a C-RAN system with massive MIMO-based RRUs and 3GPP functional Split 7, wherein MIMO equalization is done at the RRU. We derive tractable, closed-form expressions for the steady-state probabilities of queue length and sojourn time distribution at the output port of an Ethernet switch in the FH network. We first present these results for Poisson file arrivals from users in the network and exponential file size distribution. We then extend the results to general file size distribution. The numerical results show that the file size and spectral efficiency of the users are critical in determining the FH latency. Further, results show that switch speed can be decreased without incurring significant increase in FH latency revealing the possibility for statistical multiplexing gains.

Fifth generation (5G) of mobile networks are expected to serve multiple heterogeneous use cases. These use cases are extremely diverse in terms of service requirements and bundling them in a single monolithic network is a challenge. Network slicing is identified as one of the main enablers of 5G systems, where multiple logical End-to-End (E2E) networks share the resources of a single physical network. Radio Resource Management (RRM) in a sliced network should be able to simultaneously fulfill the required services of slices, dynamically share the network and assure the independence of slices so that slices cannot affect each other negatively. In this paper, we study the existing mechanisms that provide similar features in legacy networks and demonstrate the contributions and shortcomings of such existing RRM mechanisms in a sliced network. Thereafter, we argue the need for a new entity that will complement the existing RRM mechanisms to be slice-aware. With the aid of system-level simulations, we compare different slicing schemes and illustrate the drawbacks of legacy networks in fulfilling the objectives of a fully sliced network. Moreover, we illustrate the capabilities of the slice-aware RRM in steering the network's Key Performance Indicators (KPIs).

In orthogonal frequency division multiplexing systems, reduced feedback schemes provide essential channel state information from the users to the base station (BS) without overwhelming the uplink. For the practically important best-M scheme, in which each user feeds back only its M strongest subchannels and their indices to the BS, we derive a novel, throughput-optimal scheduling and rate adaptation policy that enables the BS to schedule the best user and its data rate for all the subchannels. The policy exploits the structure of the information fed back by the best-M scheme and the correlation among subchannel gains. We present it in closed-form for the widely studied exponential correlation model. Using insights gleaned from the optimal policy, we propose a novel, low-complexity two subchannel reduction approach, which is seen empirically to be near-optimal and easily handles practically important general channel correlation models, quantized feedback, and co-channel interference in multi-cell scenarios. Compared with several ad hoc approaches, the proposed approaches improve the cell throughput without any additional feedback. A modified gradient-based opportunistic scheduler is also proposed to ensure user fairness.

In rate-adaptive orthogonal frequency division multiplexing (OFDM) systems, limited feedback schemes are essential to reduce the number of subchannels for which the channel state information is fed back by the users. For the practically important best-M scheme, in which each user feeds back only its M strongest subchannels and their indices to the base station (BS), we derive a throughput-optimal rate adaptation policy that enables the BS to assign rates to the subchannels of every user. We present it in closed-form for the widely used exponential correlation model. The novelty of the policy lies in its exploitation of the structure of the information fed back by the best-M scheme and the correlation among subchannel gains. We also present a near-optimal, lower computational complexity approach. In effect, our approach facilitates rate adaptation and scheduling by the BS even on subchannels that are not fed back by a user due to feedback constraints. For various schedulers, we show that it improves the downlink throughput compared to several conventional approaches, without requiring any additional feedback.

Time-variations in wireless channels affect opportunistic selection in a multi-node wireless system in two ways. First, the selected node can become sub-optimal by the time data transmission commences. Second, the channel changes during data transmission. We develop a comprehensive and tractable analytical framework that accurately accounts for both these effects. It differs from the extensive existing literature that primarily focuses on time-variations until the data transmission starts. We first develop a novel concept of a time-invariant effective signal-to-noise ratio (TIESNR), which tractably and accurately captures the time-variations during the data transmission phase with partial channel state information available at the receiver. Thereafter, we model the joint distribution of the signal-to-noise ratio at the time of selection and TIESNR during the data transmission and analyze the average packet error rate (PER). Extensive numerical results verify the accuracy of each step of our approach and show that ignoring the correlated time-variations during the data transmission phase can significantly underestimate the average PER.

Reduced feedback schemes play a critical role in orthogonal frequency division multiplexing-based cellular systems because they facilitate scheduling and rate adaptation by the base station (BS) while reducing the number of subchannels for which channel state information is fed back by the users. We address the problem of reliable transmission even on subchannels that are fed back by a few users due to feedback constraints. For the practically relevant best-M feedback scheme, in which each user reports only its M strongest subchannels to the BS, we derive a nonlinear constrained minimum mean square error estimator that enables the BS to estimate the signal-to-noise-ratios of all the subchannels of every user. We then propose two lower computational complexity approaches that incur a negligible loss in performance. The novelty of these approaches lies in their exploitation of the structure of the best-M feedback information and the correlation among subchannel gains. Applications to general channel models and to quantized feedback are also shown. In terms of system level impact, the proposed approaches improve the cell throughput compared to several conventional approaches - without requiring any additional feedback - for uncorrelated and correlated subchannels, and for various schedulers.

Managing inter-cell interference is one of the main challenges in current and next generation wireless systems that aggressively reuse the frequency. Cooperation between interfering cells has been sought to mitigate interference. In this paper, we address the problem of jointly optimizing the transmit powers, user scheduling, and user association in a cellular network to maximize the weighted sum rate (WSR). To this end, we develop a distributed interference penalty algorithm in which the cells update their transmit powers and user schedule to maximize its utility minus an interference cost. The proposed algorithm involves only limited exchange of information via backhaul and has convergence guarantees. Furthermore, we propose a sub-optimal algorithm with lower computational and backhaul overhead. In it, the users are first associated to the base stations (BSs) based on their signal-to-interference-plus-noise-ratios (SINRs). It is then followed by joint optimization of BS transmit powers and user scheduling, for which we develop an interference penalty algorithm as well. We show that the proposed algorithms outperform the computationally complex weighted minimum mean squared error (WMMSE) algorithm.

In contemporary orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE), LTE-Advanced, and WiMAX, a codeword is transmitted over a group of subcarriers. Since different subcarriers see different channel gains in frequency-selective channels, the modulation and coding scheme (MCS) of the codeword must be selected based on the vector of signal-to-noise-ratios (SNRs) of these subcarriers. Exponential effective SNR mapping (EESM) maps the vector of SNRs into an equivalent flat-fading SNR, and is widely used to simplify this problem. We develop a new analytical framework to characterize the throughput of EESM-based rate adaptation in such wideband channels in the presence of feedback delays. We derive a novel accurate approximation for the throughput as a function of feedback delay. We also propose a novel bivariate gamma distribution to model the time evolution of EESM between the times of estimation and data transmission, which facilitates the analysis. These are then generalized to a multi-cell, multi-user scenario with various frequency-domain schedulers. Unlike prior work, most of which is simulation-based, our framework encompasses both correlated and independent subcarriers and various multiple antenna diversity modes; it is accurate over a wide range of delays.

In orthogonal frequency division multiplexing (OFDM) systems, such as long term evolution (LTE) and WiMAX, a codeword is transmitted over a group of subcarriers. Since the subcarriers see different channel gains in a frequency-selective channel, the modulation and coding scheme (MCS) of the codeword must be selected based on the vector of signal-to-noise-ratios (SNRs) of these subcarriers. Exponential effective SNR mapping (EESM) simplifies this problem by mapping the vector of SNRs into a single, equivalent flat-fading SNR. We develop a new analytical framework to characterize the throughput of EESM-based rate adaptation in such wideband channels in the presence of feedback delays, which make the choice of the MCS partially outdated by the time data transmission takes place. To this end, we first propose a novel bivariate gamma distribution to model the joint statistics of EESM at the times of estimation and data transmission. We then derive a novel expression for the throughput as a function of feedback delay. Our framework works for both correlated and independent subcarriers and for various multiple antenna diversity modes, and is accurate over a wide range of delays.

In contemporary wideband orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE) and WiMAX, different subcarriers over which a codeword is transmitted may experience different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems is driven by a vector of subcarrier SNRs experienced by the codeword, and is more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping this vector into a single equivalent flat-fading SNR. Analysis of AMC using EESM is challenging owing to its non-linear nature and its dependence on the modulation and coding scheme. We first propose a novel statistical model for the EESM, which is based on the Beta distribution. It is motivated by the central limit approximation for random variables with a finite support. It is simpler and as accurate as the more involved ad hoc models proposed earlier. Using it, we develop novel expressions for the throughput of a point-to-point OFDM link with multi-antenna diversity that uses EESM for AMC. We then analyze a general, multi-cell OFDM deployment with co-channel interference for various frequency-domain schedulers. Extensive results based on LTE and WiMAX are presented to verify the model and analysis, and gain new insights.

In orthogonal frequency division multiplexing systems, such as Long Term Evolution (LTE) and WiMAX, the different subcarriers over which a codeword is transmitted may see different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems must be based on a vector of subcarrier SNRs seen by the codeword, and is considerably more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping the vector of SNRs into a single equivalent flat-fading SNR. However, the analysis of AMC using EESM is challenging owing to its non-linear nature and because it uses an SNR scaling parameter that depends on the modulation and coding scheme. We first propose a novel statistical model for EESM based on the Beta distribution, which is motivated by the central limit approximation for the sum of random variables with finite support. Unlike several ad hoc statistical models, which require three or more parameters to be computed numerically, the proposed model requires only two parameters, for which closed-form expressions are derived for both correlated and uncorrelated subcarrier SNRs. Despite its simplicity, it is as accurate as the ad hoc models. We then present a novel, tight upper bound and an accurate approximation in closed-form for the throughput of a frequency-selective system that uses EESM for AMC.