The integration of sensing, communication, and power transfer (ISCPT) has emerged as a promising paradigm for energy- and spectrum-efficient 6G networks. Recent studies have revealed that sensing accuracy, achievable rate, and harvested energy inherently exhibit conflicting design requirements and form a nontrivial trade-off region. However, existing integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) schemes typically optimize at most two of these functionalities and lack a unified resource-allocation framework that can flexibly balance all three under stringent power budgets. Motivated by this gap, we consider a reconfigurable intelligent surface (RIS)-aided ISCPT system that employs non-orthogonal multiple access (NOMA) to support multi-user connectivity. In the proposed design, the RIS reshapes the wireless propagation environment in an energy-efficient manner to enhance both sensing and power transfer, while NOMA provides power-domain multiplexing to improve spectral efficiency and user scalability. We formulate a total transmit power minimization problem by jointly optimizing the base-station beamforming, RIS phase shifts, power splitting (PS) ratios, and NOMA decoding order under quality-of-service (QoS), Cramér–Rao-bound-based sensing accuracy, and energy-harvesting constraints. The resulting problem is highly non-convex due to the coupling among the design variables. To solve it efficiently, we develop a block coordinate descent (BCD)-based algorithm that leverages semidefinite relaxation (SDR), successive convex approximation (SCA), and the alternating direction method of multipliers (ADMM). Simulation results verify that the proposed RIS-aided NOMA-ISCPT framework significantly reduces the base-station transmit power while achieving favorable trade-offs among communication reliability, sensing precision, and energy-transfer efficiency.

The effective use of Generative Artificial Intelligence (GenAI) in education is rapidly increasing at all levels and educators are exploring this technology to make the best use of it. This paper proposes exploiting GenAI to help engineering students perform their laboratory tasks effectively. In Transnational Education (TNE) programmes based on block teaching, students may face uneven intensive study load with nonconsistent face-to-face student-teacher contact throughout the term. There is a post-COVID impact even after lifting restrictions and bringing a change in students' behaviour of more reliance on online resources rather than in-class lectures. To address these concerns, this work aims to develop a GenAI-based tool that leverages student reliance on self-study while filling the gap between teacher-student interaction during non-teaching weeks. The AI mentor will be informed/trained with all the lab-related material for the module and is expected to support students during the lab sessions. In this paper, the authors present a pilot study and engage third-year engineering students to evaluate their willingness to use existing GenAI based code assistants in solving lab tasks that involve programming exercises. Results indicate students use GenAI tools either occasionally or frequently, to assist with programming-related assignments. When applied to lab tasks, majority of the students find GenAI code assistant quite helpful for understanding concepts and solving the programming tasks. Additionally, students utilize these tools for diverse purposes, including writing code, solving errors, and answering questions. Findings reveal high enthusiasm among students for incorporating GenAI based code assistants into labs. In the future, this work aims to do refinements in further studies and engage second-year engineering students as we believe engaging them will provide a broader perspective on the adoption and use of these technologies. As engineering graduates are generally expected to swiftly adapt to new technologies and digital environments, developing this ability pre-graduation is crucial for their skillset and their professional readiness.

In massive MIMO systems, traditional channel estimation techniques often suffer from noise sensitivity and high computational complexity. Recently proposed deep supervised learning–based estimators have improved accuracy yet require large labeled datasets and exhibit poor generalization in dynamic channel conditions. Consequently, self-supervised methods have emerged, avoiding extensive label collection and enabling immediate online deployment. However, existing self-supervised frameworks typically rely on large networks with long run times, demanding substantial computational resources. In this work, we present a lightweight self-supervised channel estimation framework, Meta-ZSN2N. It first leverages a traditional estimator, then applies a specialized downsampling step, and finally refines the results via a lightweight two-layer neural network, resulting in a significantly simplified model and a substantially reduced runtime. To further accelerate online inference and boost generalization, we integrate a Meta-SGD module into our design. Simulation results indicate that our proposed lightweight method not only surpasses traditional estimators but also outperforms large learning networks with millions of parameters in terms of efficiency and adaptability.

Low Earth orbit (LEO) satellite networks have become pivotal in modern communication systems, offering ubiquitous connectivity for direct-to-satellite (DtS) service in remote and underserved regions. To overcome the limited power of handheld devices, cooperative communication offers an opportunity to enhance connectivity, e.g., coverage probability, by leveraging multiple satellites to mitigate interference and improve signal quality. To this end, this paper proposes a stochastic geometry-based framework to analyze the performance of large-scale cooperative satellite networks. We model the satellites and interfering ground users as Poisson Point Processes (PPPs) and consider a typical user served by its closest subset of visible satellites. The received uplink signals at the serving satellites are combined using the maximum ratio combining (MRC) technique. We derive the coverage probability, accounting for the correlation among serving distances and interference from neighboring devices. Our analytical results, validated via Monte Carlo simulations, demonstrate a 33% increase in coverage probability when the number of cooperating devices doubles at an SINR threshold of -10 dB, offering key design insights for optimizing cooperative LEO satellite networks.

This paper investigates the performance of adaptive LoRa networks with dynamic SF allocation accounting for Duty Cycle (DC) restrictions and quantifying the imperfect orthogonality of Spreading Factor (SF)s. The study presents a novel spatiotemporal model that combines stochastic geometry and queuing theory where LoRa devices are perceived as interacting two-dimensional DTMCs. Each chain jointly tracks the number of packets in the buffer and the node’s protocol state. Numerical simulations are carried out to validate the accuracy of the proposed model. The network performance is studied in terms of Pareto frontiers under different orthogonality assumptions and adaptation settings, showcasing the ranges of sensing applications that LoRa can accommodate without compromising the network stability. The evolution of SFs activity distribution, coverage probability and average latency is examined against different network parameters. The results show that activating SF adaptation with higher cardinality is not always advantageous and evince the existence of an adaptation cardinality that minimises the delay. The study also identifies regimes where SF adaptation is advantageous for the network scalability and reveals ‘SF-Up’ and ‘SF-Down’ rates that maximise the coverage or minimise the delay. Comparing dynamic to static SF allocations, the results highlight a tradeoff between coverage and latency yielding valuable insights into scenarios where either of the allocation strategies would be more beneficial to the network.

An analytical model joining stochastic geometry and queuing theory is devised to study the performance of adaptive LoRa networks with dynamic Spreading Factor (SF) allocation. LoRa devices are perceived as interacting two-dimensional Discrete Time Markov Chains (DTMC)s. Each chain jointly tracks the number of packets in the buffer and the node's protocol state while accounting for Duty Cycle (DC) restrictions and quantifying the imperfect orthogonality of SFs. The network performance is characterised in terms of coverage, delay and Pareto frontiers under different orthogonality assumptions and for various adaptation settings highlighting insights useful for the design of application-aware decentralised or semi-decentralised SF adaptation schemes.

In this article, we provide, for the first time, a comprehensive understanding of long-range (LoRa) waveform theory in order to quantify its orthogonality. We present LoRa waveform expressions in continuous- and discrete-time domains, and analyze measures of orthogonality between different LoRa spreading factors (SFs) through cross-correlation functions. The cross-correlation functions are analytically expressed in a general form and they account for diverse configuration parameters (bandwidth, SF, etc.) and different cases of signal displacements (time delay shift, frequency shift, etc.). We quantify their mean and maximum in all time domains. We highlight the impact of the temporal displacement and different bandwidths. The general result is that LoRa modulation is nonorthogonal. First, we observe that for same bandwidths, the largest maximum cross-correlation happens for same SF and is equal to 100% due to same symbols; whereas for different bandwidths, the largest maximum cross-correlation is no longer observed at the same SF. Second, the maximum cross-correlation is less than 26% between different SFs, is higher for closer SFs, and decreases as the difference between SFs increases. After downchirping, the maximum cross-correlation increases and the mean decreases compared to those before downchirping. Moreover, the maximum cross-correlation is insignificantly impacted by the temporal delay, which makes it valid to adopt for the performance analysis of both synchronous and asynchronous systems. Finally, we analyze by simulating the bit error probability statistics for different bandwidth ratios and highlighting their correlated behavior with the insights obtained from the maximum cross-correlation expressions.

Age of Information (AoI), a metric measuring the information freshness, has drawn increased attention due to its importance in monitoring applications in which nodes send timestamped status updates to interested recipients, and timely updates about phenomena are important. In this work, we consider the AoI minimization scheduling problem in multihop energy harvesting (EH) wireless sensor networks (WSNs). We design the generation time of updates for nodes and develop transmission schedules under both protocol and physical interference models, aiming at achieving minimum peak AoI and average AoI among all nodes for a given time duration. We prove that it is an NP-Hard problem and propose an energy-adaptive, distributed algorithm called the minimizing AoI scheduling algorithm for general network (MAoIG). We derive its theoretical upper bounds for the peak and average AoI and a lower bound for peak AoI. The numerical results validate that MAoIG outperforms all of the baseline schemes in all scenarios and that the experimental results tightly track the theoretical upper bound optimal solutions while the lower bound tightness decreases with the number of nodes.

Physical-layer security (PLS) schemes use the randomness of the channel parameters, namely, channel state information (CSI) and received signal strength indicator (RSSI) to generate the secret keys. There has been limited work in PLS schemes in long-range (LoRa) wide-area networks (LoRaWANs) which hinder their widespread application. Limitations observed in existing studies include the requirement of high correlation between channel parameter measurements for secret key generation in the proposed schemes and the evaluation of the schemes has only been done in either fully indoor or outdoor environments. The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements thus making the current PLS schemes inappropriate for these systems. By considering the limitations found in existing PLS schemes, this article proposes LoRA-LiSK, a practical and efficient shared secret key generation scheme for LoRa networks. Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov–Smirnov tests, and a Savitzky–Golay filter), multilevel quantization, information reconciliation using Bose–Chaudhuri–Hocquenghem (BCH) codes, and finally, privacy amplification using secure hash algorithm SHA-2. The LoRa-LiSK scheme is extensively evaluated on real WSN/IoT devices in practical application scenarios: 1) indoor to outdoor and 2) LoRa static and mobile outdoor links. It outperforms existing schemes by generating keys with channel parameter measurements of low correlation values (0.2–0.6), while still essentially achieving high key generation rates, and low key disagreement rates (10%–20%). The scheme updates a key in approximately 1 h using an application profile with high transmission rate compared to 3 h reported by existing works while still respecting the duty cycle regulation. It also incurs less communication overhead compared to the existing works.

In this paper, we consider the uplink transmissions of non-orthogonal multiple access (NOMA)-based low-power wide-area (LPWA) networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the ”harvest-then-transmit” protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the transmission times either fairly or unfairly between LPWA nodes, we optimize the EH times using a one-dimensional search method, and optimize the transmit powers using concave-convex procedure (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a 15% performance improvement when using NOMA.

Long range (LoRa) demonstrates high potential in supporting massive Internet-of-Things (IoT) applications. In this paper, we study the resource allocation in energy harvesting (EH)-enabled LoRa networks with imperfect spreading factor (SF) orthogonality. We maximize the user fairness in terms of the minimum time-averaged throughput while jointly optimizing the SF assignment, the EH time duration, and the transmit power of all LoRa users. First, we provide a general expression of the packet collision time between LoRa users which depends on the SFs and EH duration requirements of each user. Then, we develop two SF allocation schemes that either assure fairness or not for the LoRa users. Within this, we optimize the EH time and the power allocation for single and multiple uplink transmission attempts. For the single uplink transmission attempt, the optimal power allocation is obtained using bisection method. For the multiple uplink transmission attempts, the suboptimal power allocation is derived using concave-convex procedure (CCCP). Our results unearth new findings. Firstly, we demonstrate that the unfair SF allocation algorithm outperforms the others in terms of the minimum data rate. Additionally, we observe that co-SF interference is the main limitation in the throughput performance, and not really energy scarcity.

Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA) networks are essential for large scale dense Internet-of-Things (IoT) deployments. As Long Range (LoRa) is currently one of the most prominent LPWA technologies, we propose in this paper a stochastic-geometry-based framework to analyse the uplink transmission performance of a multi-gateway LoRa network modelled by a Matern Cluster Process (MCP). The proposed model is first to consider all together the multi-cell topology, imperfect spreading factor (SF) orthogonality, random start times, and geometric data arrival rates. Accounting for all of these factors, we initially develop the SF-dependent collision overlap time function for any start time distribution. We, then analyse the Laplace transforms of intra-cluster and inter-cluster interference and formulate the uplink transmission success probability. Through simulation results, we highlight the vulnerability of each SF to interference, illustrate the impact of parameters such as the network density and the power allocation scheme on the network performance. Uniquely, our results shed light on when it is better to activate adaptive power mechanisms, as we show that an SF-based power allocation that approximates LoRa Adaptive Data Rate (ADR) negatively impacts nodes near the cluster head. Moreover, we show that the interfering SFs degrading the performance the most depend on the decoding threshold range and the power allocation scheme.

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

In this paper, we investigate the resource allocation for uplink non-orthogonal multiple access (NOMA) enabled low-power wide-area (LPWA) networks to support the massive connectivity of users/nodes. Here, LPWA nodes communicate with a central gateway through resource blocks like channels, transmission times, bandwidths, etc. The nodes sharing the same resource blocks suffer from intra-cluster interference and possibly inter-cluster interference, which makes current LPWA networks unable to support the massive connectivity. Using the minimum transmission rate metric to highlight the interference reduction that results from the addition of NOMA, and while assuring user throughput fairness, we decompose the minimum rate maximization optimization problem into three sub- problems. First, a low-complexity sub-optimal nodes clustering scheme is proposed assigning nodes to channels based on their normalized channel gains. Then, two types of transmission time allocation algorithms are proposed that either assure fair or unfair transmission time allocation between LPWA nodes sharing the same channel. For a given channel and transmission time allocation, we further propose an optimal power allocation scheme. Simulation evaluations demonstrate approximately 100dB improvement of the selected metric for a single network with 4000 active nodes.

In this paper, we investigate the uplink transmissions in low-power wide-area networks (LPWAN) where the users are self-powered by the energy harvested from the ambient environment. Demonstrating their potential in supporting diverse Internet-of-Things (IoT) applications, we focus on long range (LoRa) networks where the LoRa users are using the harvested energy to transmit data to a gateway via different spreading codes. Precisely, we study the throughput fairness optimization problem for LoRa users by jointly optimizing the spreading factor (SF) assignment, energy harvesting (EH) time duration, and the transmit power of LoRa users. First, through examination of the various permutations of collisions among users, we derive a general expression of the packet collision time between LoRa users, which depends on the SFs and EH duration requirements. Then, after reviewing prior SF allocation work, we develop two types of algorithms that either assure fair SF assignment indeed purposefully `unfair' allocation schemes for the LoRa users. Our results unearth three new findings. Firstly, we demonstrate that, to maximize the minimum rate, the unfair SF allocation algorithm outperforms the other approaches. Secondly, considering the derived expression of packet collision between simultaneous users, we are now able to improve the performance of the minimum rate of LoRa users and show that it is protected from inter-SF interference which occurs between users with different SFs. That is, imperfect SF orthogonality has no impact on minimum rate performance. Finally, we have observed that co-SF interference is the main limitation in the throughput performance, and not the energy scarcity.

In this paper, we aim to maximize the end-to-end achievable rate of multiple-input multiple-output (MIMO) decode-and-forward (DF) where the relay is an energy harvesting (EH) node using the time switching (TS) scheme. The relay first harvests the energy from the source, then uses its harvested energy to forward the information carrying signal from the source to the destination. The EH model at the relay is a nonlinear model. Also, we assume that the channel knowledge is imperfect at the relay and destination. We propose the structure of the optimal covariance matrices at the source (during EH and information decoding periods), the optimal covariance matrix at the relay and the optimal EH time ratio. Through the simulation results, we compare between different linear/nonlinear EH models and we show the gain/loss performance of the linear model compared to other nonlinear EH models.

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modeled via a two- dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which defines the network parameters that ensure stable network operation and finite packet delay. The results provide several insights to design self-sustainable IoT networks.

Simultaneous wireless power and information transfer (SWIPT) in a cognitive radio (CR) network is considered where a multiple antenna energy harvesting (EH) secondary receiver (SR) harvests the energy using the antenna switching (AS) technique. In fact, the AS technique selects a subset of the SR antennas to decode the information (namely the information decoding (ID) antennas) and the rest to harvest the energy (namely the EH antennas). In this context, we propose a thresholding-based antenna selection strategy, termed as the prioritizing data selection (PDS) scheme, which selects the ID antennas such that the received power from the secondary transmitter (ST) at these antennas is above a certain threshold. For this scheme, we derive the analytic expressions of the probability mass function (PMF) of the selected ID antennas, the average harvested energy, and the outage probability. In the simulation results, we illustrate the performance of the PDS scheme and we compare it to the prioritizing energy selection (PES) scheme which selects the EH antennas such that the received power from ST at these antennas is above a certain threshold. For both schemes, we show that there is a tradeoff between the outage probability and the average harvested energy.

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) decode-and-forward (DF) relay broadcasting channel (BC) with single source, multiple energy harvesting (EH) relays, and multiple destinations. All the nodes are equipped with multiple antennas. The EH and information decoding tasks at the relays and destinations are separated over the time, which is termed as the time switching scheme. As optimal solutions for the sum-rate maximization problems of BC channels and the MIMO interference channels are hard to obtain, the end-to-end sum rate maximization problem of a multiuser MIMO DF relay BC channel is even harder. In this paper, we propose to tackle a simplified problem, where we employ the block diagonalization (BD) procedure at the source, and we mitigate the interference between the relay-destination channels using an algorithm similar to the BD method. In order to show the relevance of our low complex proposed solution, we compare it with the minimum mean-square error (mmse) solution that was shown in the literature to be equivalent to the solution of the sum-rate maximization in the MIMO broadcasting interfering channels. We also investigate the time division multiple access (TDMA) solution, which separates all the information transmissions from the source to the relays and from the relays to the destinations over time. We provide the numerical results to show the relevance of our proposed solution, in comparison with the no co-channel interference case, the TDMA-based solution, and the mmse-based solution.

In this paper, we investigate the simultaneous wireless information and power transfer in a multiple-input multiple-output decode-and-forward relay system, where the relay is an energy harvesting (EH) multi-antenna node equipped with an EH receiver and an information decoding (ID) receiver. The relay harvests the energy from the radio frequency signals sent by the source and uses it to forward the signals to the destination. The main objective in this paper is to maximize the achievable transmission rate of the overall link by optimizing the source/relay precoders. First, we study an upper bound on the maximum achievable rate where we assume that the EH and ID receivers operate simultaneously and have access to the whole power of the received signals. Afterward, we study two practical schemes, which are the power splitting and time switching schemes, where the ID and EH receivers have partial access to the power or duration of the received signals. For each scheme, we have studied the complexity and the performance comparison. In addition, we considered the case of the imperfect channel estimation error and we have observed its impact on the achievable end-to-end rate and the harvested energy at the relay.

In this paper, we investigate two-hop Multiple- Input Multiple-Output (MIMO) Amplify-and-Forward (AF) relay communication systems with simultaneous wireless information and power transfer (SWIPT) at the multi-antenna energy harvesting relay. We derive the optimal source and relay covariance matrices to characterize the achievable region between the source-destination rate and the harvested energy at the relay, namely Rate-Energy (R-E) region. In this context, we consider the ideal scenario where the energy harvester (EH) receiver and the information decoder (ID) receiver at the relay can simultaneously decode the information and harvest the energy at the relay. This scheme provides an outer bound for the achievable R-E region since practical energy harvesting circuits are not yet able to harvest the energy and decode the information simultaneously. Then, we consider more practical schemes which are the power splitting (PS) and the time switching (TS) proposed in [1] and which separate the EH and ID transfer over the power domain and the time domain, respectively. In our study, we derive the boundary of the achievable R- E region and we show the effect of the source transmit power, the relay transmit power and the position of the relay between the source and the destination on the achievable R-E region for the ideal scenario and the two practical schemes.

The probability density function of the sum of Log-normally distributed random variables (RVs) is a well-known challenging problem. For instance, an analytical closed-form expression of the Log-normal sum distribution does not exist and is still an open problem. A crude Monte Carlo (MC) simulation is of course an alternative approach. However, this technique is computationally expensive especially when dealing with rare events (i.e. events with very small probabilities). Importance Sampling (IS) is a method that improves the computational efficiency of MC simulations. In this paper, we develop an efficient IS method for the estimation of the Complementary Cumulative Distribution Function (CCDF) of the sum of independent and not identically distributed Log-normal RVs. This technique is based on constructing a sampling distribution via twisting the hazard rate of the original probability measure. Our main result is that the estimation of the CCDF is asymptotically optimal using the proposed IS hazard rate twisting technique. We also offer some selected simulation results illustrating the considerable computational gain of the IS method compared to the naive MC simulation approach.

In this paper, we investigate the simultaneous wireless information and power transfer (SWIPT) for a decode-and-forward (DF) multiple-input multiple-output (MIMO) relay system where the relay is an energy harvesting node. We consider the ideal scenario where both the energy harvesting (EH) receiver and information decoding (ID) receiver at the relay have access to the whole received signal and its energy. The relay harvests the energy while receiving the signal from the source and uses the harvested power to forward the signal to the destination. We obtain the optimal precoders at the source and the relay to maximize the achievable throughput rate of the overall link. In the numerical results, the effect of the transmit power at the source and the position of the relay between the source and the destination on the maximum achievable rate are investigated.

In this paper, we present a simple way to compute the ergodic capacity of cascaded channels with perfect channel state information at both the transmitter and the receiver. We apply our generic results to the Rayleigh-double fading channel, and to the free-space optical channel in the presence of pointing errors and we express their low signal-to-noise ratio capacities. We mainly focus on the low signal-to-noise ratio range.

The capacity of Multiple Input Multiple Output (MIMO) Rayleigh fading channels with full knowledge of channel state information (CSI) at both the transmitter and the receiver (CSI-TR) has been shown recently to scale at low Signal-to-Noise Ratio (SNR) essentially as SNR log(1=SNR), independently of the number of transmit and receive antennas. In this paper, we investigate the ergodic capacity of MIMO Rayleigh fading channel with estimated channel state information at the transmitter (CSI-T) and possibly imperfect channel state information at the receiver (CSI-R). Our framework can be seen as a generalization of previous works as it can capture the perfect CSI-TR as a special case when the estimation error variance goes to zero. In our work, we mainly focus on the low SNR regime and we show that the capacity scales as (1-α) SNR log(1=SNR), where α is the estimation error variance. This characterization shows the loss of performance due to error estimation over the perfect channel state information at both the transmitter and the receiver. As a by-product of our new analysis, we show that our framework can also be extended to characterize the capacity of MIMO Rician fading channels at low SNR with possibly imperfect CSI-T and CSI-R.

The capacity of multiple-input multiple-output (MIMO) Rayleigh fading channels with full knowledge of channel state information (CSI) at both the transmitter and the receiver (CSI-TR) has been shown recently to scale at low signal-to-noise ratio (SNR) essentially as SNR log(1/SNR), independently of the number of transmit and receive antennas. In this paper, we investigate the ergodic capacity of MIMO Rayleigh fading channel with estimated channel state information at the transmitter (CSI-T) and possibly imperfect channel state information at the receiver (CSI-R). Our framework can be seen as a generalization of previous works as it can capture the perfect CSI-TR as a special case when the estimation error variance goes to zero. In this paper, we mainly focus on the low SNR regime, and we show that the capacity scales as (1 - α) SNR log(1/SNR), where α is the estimation error variance. This characterization shows the loss of performance due to error estimation over the perfect channel state information at both the transmitter and the receiver. As a by-product of our new analysis, we show that our framework can be also extended to characterize the capacity of MIMO Rician fading channels at low SNR with possibly imperfect CSI-T and CSI-R.

The effective capacity have been introduced by Wu and Neji as a link-layer model supporting statistical delay QoS requirements. In this paper, we propose to study the effective capacity of a Nakagami-m fading channel with full channel state information (CSI) at both the transmitter and at the receiver. We focus on the low Signal-to-Noise Ratio (SNR) regime. We show that the effective capacity for any arbitrary but finite statistically delay Quality of Service (QoS) exponent θ, scales essentially as S NRlog(1/SNR) exactly as the ergodic capacity, independently of any QoS constraint. We also characterize the minimum energy required for reliable communication, and the wideband slope to show that our results are in agreement with results established recently by Gursoy et al. We also propose an on-off power control scheme that achieves the capacity asymptotically using only one bit CSI feedback at the transmitter. Finally, some numerical results are presented to show the accuracy of our asymptotic results.