Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

Long Range Wide Area Network (LoRaWAN) deployments are limited by the protocol’s native star topology. In obstructed environments, such as underground, dense urban, or industrial sites, coverage can be unreliable, causing data loss. Adding gateways improves coverage but is often cost-prohibitive. To address this, the LoRa Alliance introduced the relay functionality. This paper analyzes the energy impact on relays and end-devices using Time-on-Air (ToA)-based estimates, comparing LoRaWAN relay performance with the Long Preamble method and emphasizing trade-offs and oscillator accuracy. Our analysis reveals that the tolerance of the crystal oscillator plays a major role in the end device’s energy consumption. Specifically, using a 10 PPM crystal instead of a 40 PPM crystal, the standardized mode provides a 29% energy reduction for SF7 values compared to the Long Preamble approach for a one-hour link periodicity.

This study addresses the challenge of robust human pose recognition in complex indoor environments by proposing a system that generalizes to unseen user locations using only Angle of Arrival (AoA) data from Bluetooth Low Energy (BLE) 5.1 devices. A comparative evaluation was performed, assessing a Random Forest model alongside three neural network architectures— CNN, LSTM, and hybrid models—for identifying four physical states: standing, sitting, lying down, and walking. Data were collected in a real indoor environment using smart Personal Protective Equipment (PPE) equipped with BLE transmitters placed on a helmet, upper and lower shirt regions, and a boot. The preprocessing pipeline included filtering, interpolation, feature extraction, and application of the Fast Fourier Transform (FFT). Models were evaluated under two scenarios: (i) known positions (used in training) and (ii) unknown positions (never seen before). The best overall performance on unknown positions was achieved by theAttention-based BiLSTM–CNN,reaching an accuracy ofA= 94.89% and a macro averaged F1-score ofF1= 95.59%, demonstrating strong generalization capabilities. In the scenario with known positions, the highest performance was obtained by theRandom Forest, withA= 99.95% andF1= 99.95%, followed by theDeep CNN,withA= 97.27% andF1= 97.28%, closely followed by theAttention-based BiLSTM–CNN,which also showed robust classification accuracy across all posture classes. The analysis confirmed that using four wearable devices led to the highest performance, and sensor combinations significantly affected accuracy. The study also provides a publicly available dataset to support reproducibility and benchmarking.

Federated learning (FL) faces significant challenges in Internet of Things (IoT) networks due to device limitations in energy and communication resources, especially when considering the large size of FL models. From an energy perspective, the challenge is aggravated if devices rely on energy harvesting (EH), as energy availability can vary significantly over time, influencing the average number of participating users in each iteration. Additionally, the transmission of large model updates is more susceptible to interference from uncorrelated background traffic in shared wireless environments. As an alternative, federated distillation (FD) reduces communication overhead and energy consumption by transmitting local model outputs, which are typically much smaller than the entire model used in FL. However, this comes at the cost of reduced model accuracy. Therefore, in this paper, we propose FL-distillation alternation (FLDA). In FLDA, devices alternate between FD and FL phases, balancing model information with lower communication overhead and energy consumption per iteration. We consider a multichannel slotted-ALOHA EH-IoT network subject to background traffic/interference and compared FLDA to FL, FD, and SplitFed. In such a scenario, FLDA demonstrates higher model accuracy than both FL and FD, and achieves faster convergence than both FL and SplitFed. While SplitFed achieves a similar accuracy level to FLDA with no interference, the method is highly affected by interference, which also affects FL. Moreover, FLDA achieves target accuracies saving up to 98.02% in energy consumption relative to FL and up to 99.85% relative to SplitFed.

This paper investigates the problem of energy beamforming for wireless power transfer (WPT) using dynamic metasurface antennas (DMAs).We propose a novel solution based on the bat algorithm (BA) to efficiently optimize the beamforming process. The proposed BA-based scheme enables simultaneous charging of multiple devices while avoiding power transmission in specific directions, such as areas where people or animals may be present. Our approach provides a robust and computationally efficient solution, considering key system constraints, including power transfer efficiency, antenna configurations, and DMA characteristics. Simulation results demonstrate that the BA-based method outperforms existing techniques in the literature, particularly those relying on alternating optimization, by achieving lower total power consumption and reduced computational complexity. These findings highlight the potential of the proposed method as a promising solution for future WPT systems employing DMAs.

Contemporary wireless communication systems rely on Multi-User Multiple-Input Multiple-Output (MU-MIMO) techniques. In such systems, each Access Point (AP) is equipped with multiple antenna elements and serves multiple devices simultaneously. Notably, traditional systems utilize fixed antennas, i.e., antennas without any movement capabilities, while the idea of movable antennas has recently gained traction among the research community. By moving in a confined region, movable antennas are able to exploit the wireless channel variation in the continuous domain. This additional degree of freedom may enhance the quality of the wireless links, and consequently the communication performance. However, movable antennas for MU-MIMO proposed in the literature are complex, bulky, expensive and present a high power consumption. In this paper, we propose an alternative to such systems that has lower complexity and lower cost. More specifically, we propose the incorporation of rotation capabilities to APs equipped with Uniform Linear Arrays (ULAs) of antennas. We consider the uplink of an indoor scenario where the AP serves multiple devices simultaneously. The optimal rotation of the ULA is computed based on estimates of the positions of the active devices and aiming at maximizing the per-user mean achievable Spectral Efficiency (SE). Adopting a spatially correlated Rician channel model, our numerical results show that the rotation capabilities of the AP can bring substantial improvements in the SE in scenarios where the line-of-sight component of the channel vectors is strong. Moreover, our proposed system is robust against imperfect positioning estimates.

This work aims at improving indoor positioning systems (IPS) by integrating multiple radio frequency techniques, namely received signal strength indiction (RSSI), Angle of Arrival (AoA), and a combination of both, within the bluetooth low energy (BLE) 5.1 framework. While AoA stands out for its precision, low energy consumption, and cost-effectiveness, RSSI is characterized by its simplicity and widespread availability. By resorting to a database of real RSSI and AoA measurements from a BLE 5.1 target node in a$14\times 8$-m environment, our work employs the Kalman filter (KF) to improve the accuracy of multilateration, AoA combined with RSSI, and AoA-only algorithms. Moreover, we consider one more step in our IPS where the aforementioned KF-filtered outputs are then fused through a track fusion model. Results demonstrate that the proposed scheme, which we refer to as angle-RSSI fusion localization (ARFL), significantly improves localization accuracy compared to other techniques. In particular, it reduces up to 81.61% in the average position error when compared to multilateration with KF. This advanced IPS offers a cost-effective and precise solution suitable for various applications in industries, such as healthcare, commerce, and logistics.

In this letter, we investigate the Age of Information (AoI) of a power domain nonorthogonal multiple access (NOMA) network, where multiple Internet of Things (IoT) devices transmit to a common gateway in a grant-free random fashion. More specifically, we consider a framed setup composed of multiple time slots, and resort to the Q-learning algorithm to properly define, in a distributed manner, the time slot and the power level each IoT device transmits within a frame. In the proposed AoI-QL-NOMA scheme, the Q-learning reward is adapted with the aim of minimizing the average AoI of the network, while only requiring a single feedback bit per time slot, in a frame basis. Our results show that AoI-QL-NOMA significantly improves the AoI performance compared to some recently proposed schemes, without significantly reducing the network throughput.

This paper introduces an enhancement to the Long-Range Frequency-Hopping Spread Spectrum (LR-FHSS), a modulation within the LoRaWAN specification, particularly useful in dense networks supported by satellite gateways. LR-FHSS replicates headers and payload fragments across different physical channels, but the distinct transmission strategies for headers and payloads can lead to uneven success rates and degrading performance. To address this, we propose a scheme that allocates orthogonal physical channels for headers and payloads. Through analytical and simulation results, considering factors such as payload size and the number of users, we identify the optimal distribution of channels between headers and payloads to maximize success probability. The proposed scheme significantly improves performance, supporting up to 50% more users at the same reliability level as the standard LR-FHSS.

This paper compares distributed massive Multiple-Input Multiple-Output (mMIMO) and centralized MIMO in the context of over-the-air federated learning, taking into account a detailed wireless channel model that includes spatially uncorrelated Rician fading to represent the Line of Sight (LoS) component. Our simulation results provide valuable insight into how a distributed architecture can maintain a lower Mean Squared Error (MSE) in scenarios that require extensive coverage. Furthermore, our research highlights the importance of considering the impact of the LoS component and power limitations when analyzing over-the-air aggregation, as these characteristics can substantially influence aggregation performance. Adopting a distributed architecture allows devices to conserve energy while simultaneously achieving a lower MSE in the aggregation process compared to the centralized architecture. Specifically, when considering four Access Points (APs) and 200 devices, the average transmit power is reduced by approximately 31% while achieving 16% lower aggregation MSE compared to a centralized approach.

We propose and evaluate age of information (AoI)-aware multiple access mechanisms for the Internet of Things (IoT) in multi-relay two-hop networks. The network considered comprises end devices (EDs) communicating with a set of relays in ALOHA fashion, with new information packets to be potentially transmitted every time slot. The relays, in turn, forward the collected packets to an access point (AP), the final destination of the information generated by the EDs. More specifically, in this work we investigate the performance of four age-aware algorithms that prioritize older packets to be transmitted, namely max-age matching (MAM), iterative max-age scheduling (IMAS), age-based delayed request (ABDR), and buffered ABDR (B-ABDR). The former two algorithms are adapted into the multi-relay setup from previous research, and achieve satisfactory average AoI and average peak AoI performance, at the expense of a significant amount of information exchange between the relays and the AP. The latter two algorithms are newly proposed to let relays decide which one(s) will transmit in a given time slot, requiring less signaling than the former algorithms. We provide an analytical formulation for the AoI lower bound performance, compare the performance of all algorithms in this set-up, and show that they approach the lower bound. The latter holds especially true for B-ABDR, which approaches the lower bound the most closely, tilting the scale in its favor, as it also requires far less signaling than MAM and IMAS.

Wireless communications systems must increasingly support a multitude of machine-type communications devices, thus calling for advanced strategies for active user detection (AUD). Recent literature has investigated AUD techniques based on compressed sensing, highlighting the critical role of signal sparsity. This study examines the relationship between frequency diversity and signal sparsity in the AUD problem. Single-antenna users transmit multiple copies of non-orthogonal pilots across multiple frequency channels and the base station independently performs AUD in each channel using the orthogonal matching pursuit algorithm. We note that, although frequency diversity may improve the likelihood of successful reception of the signals, it may also damage the channel sparsity level, leading to important trade-offs. We show that a sparser signal significantly benefits AUD, surpassing the advantages brought by frequency diversity in scenarios with limited temporal resources and/or high numbers of receive antennas. Conversely, with longer pilots and fewer receive antennas, investing in frequency diversity becomes more impactful, resulting in a tenfold AUD performance improvement.

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a “sweet spot” on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area.

The increasing number of connected devices in machine-type applications, such as the Internet of Things (IoT), leads to the coexistence of heterogeneous services in Beyond 5G (B5G) networks, where users with diverse requirements share physical resources. In this article, we apply a channel allocation method to assign grant-based (GB) users to channels. This method, named max-matching diversity (MMD), leads to a frequency diversity gain that equals the number of available channels, reducing the outage of GB users. In the same network resource that serves a GB user, one grant-free (GF) user is allocated through a distributed contention protocol that considers the Quality-of-Service (QoS) of the GB user, performing the pairing that causes the minimum interference, so that the presence of a GF user is transparent to the GB user. Both users admitted to the same physical resource operate in either nonorthogonal multiple access (NOMA) or rate-splitting multiple access (RSMA), depending on the channel gains. Exact expressions for the outage probability of this semi-grant-free (SGF) system are provided and compared to simulation results, showing that the SGF transmissions with MMD-aided GB users strategy reduces the outage and increases the achievable rate of both users.

In this work, we apply an asynchronous contention resolution-aided ALOHA protocol to the recently proposed long-range frequency hopping spread spectrum (LR-FHSS) scheme, which is an amendment of widespread chirp spread spectrum (CSS)-based long-range (LoRa) modulation patented by Semtech. More specifically, we modify the so-called asynchronous contention resolution diversity ALOHA (ACRDA) protocol to comply with LR-FHSS requirements and improve LR-FHSS performance against collisions. In the proposed scheme, only the gateway requires modifications, while the ACRDA operation is transparent from the end-devices perspective, which follows the regular LR-FHSS operation. Our results, obtained using a discrete event simulator, show that the proposed scheme outperforms regular LR-FHSS in various aspects, such as average network success (up to 60% improvement in the scenarios considered), goodput (2× better) and number of supported devices for a target reliability level (more than 2×). Finally, we also evaluate the impact on the performance of the proposed scheme of parameters such as the window and the step sizes, showing that a relatively small window size (~2× the time-on-air of a single message) is sufficient to approach the asymptotic maximum network success achieved by an infinite window.

Grant-free protocols exploiting compressed sensing multi-user detection (MUD) are appealing for solving the random access problem in massive Internet of Things (IoT) networks with sporadic device activity. Such protocols would greatly benefit from prior deterministic knowledge of the sparsity level, i.e., the instantaneous number of simultaneously active devicesK. Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPTs) for detectingK. Specifically, the proposed CPT mechanism includes a downlink (DL) phase for channel state information acquisition that resolves fading uncertainty in the uplink (UL) transmission phase using shared UL pilot symbols for channel compensation. We propose a signal sparsity level detector and analytically assess its accuracy when network channels are subject to Rayleigh fading. We show that the variance of the estimator increases withK, and its distribution approximates that of the sum of a Student’stand Gaussian random variable. The numerical results evince the need for carefully configuring the duration of the DL and UL phases. Indeed, we show that relatively short DL phases are preferable in highly sparse networks given the total CPT duration is fixed. Finally, we discuss and exemplify with some early results the potential of the proposed CPT framework for MUD, and highlight relevant research directions.

In this work, we address a novel replication scheme for Internet-of-Things (IoT) low power wide area networks (LPWAN), considering the ability of a gateway to recover, through successive interference cancellation (SIC), superposed signals in the power domain. We show that the introduced mathematical framework matches the Monte Carlo simulations. The proposed scheme outperforms typical transmission schemes, at least from 1 to 2 orders of magnitude, in terms of outage probability. We also show that the scheme fits well with previous replication schemes, outperforming their versions without non-orthogonal replications. Moreover, we show that the proposed scheme can be robust to cases with high SIC imperfection, presenting slight performance losses even in very harsh scenarios, as long as the constraints are met. Finally, we conclude that the proposed scheme can be more energy efficient than previous replication schemes and achieve better reliability in traffic-loaded networks.

The worldwide adoption of the Internet of Things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEOs) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In this article, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate the applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present, here, a novel and computationally efficient network size estimator in DtS-IoT: our optimistic collision information (OCI)-based estimator. We evaluate OCI’s effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame-slotted Aloha-based DtS-IoT network is boosted eightfold, serving up to$4 \times 10^{3}$devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.

We evaluate the outage probability and the energy efficiency (EE) of a long range (LoRa) network when the end devices (EDs) are capable of exchanging messages with a device-to-device (D2D) technology. Our proposal is to assess the performance of the network when operating under the concept of network-coded cooperation (NCC), where the EDs are capable of transmitting linear combinations of more than one frame (including frames of another ED), performed over a nonbinary finite field. We consider both connection and collision probabilities when modeling the outage probability of an ED, as well as a realistic power consumption model to evaluate the EE. Our results indicate that the proposed scheme, which we refer to as NCC-LoRa, provides considerable gains in terms of both outage probability and EE when compared to a regular LoRa network, even when considering the additional consumption of D2D communication.

Radio-frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multiantenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this article, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario, where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as$0.85\sqrt {M}$, where$M$is the number of PB’s antenna elements. Moreover, a rotation-specific power control mechanism was proposed to: 1) fairly optimize the WET process if devices’ positioning information is available and/or 2) avoid hazards to human health in terms of specific absorption rate (SAR), which is an RF exposure metric that quantifies the absorbed power in a unit mass of human tissue. We show that RAB performance even approaches quickly (or surpasses, for scenarios with a sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI-based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight (LOS) conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long short-term memory (LSTM)-based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in the idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being a false-alarm and miss-detection probabilities, respectively, below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism by up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability.

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in high user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array, since its large dimension is of the same order of the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL-MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides of seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot (PDP) resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum-rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes’ transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.

In this work, we study the coexistence in the same Radio Access Network (RAN) of two use cases present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.

The 5G systems feature three generic services: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of these services in terms of data-rates, number of connected devices, latency and reliability can lead to a sub-optimal use of the 5G network, thus network slicing is proposed as a solution that creates customized slices of the network specifically designed to meet the requirements of each service. Under the network slicing, the radio resources can be shared in orthogonal and non-orthogonal schemes. Motivated by Industrial Internet of Things (IIoT) scenarios where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose the use of Non-Orthogonal Multiple Access (NOMA) to improve the number of URLLC devices that are connected in the uplink to the same base station (BS), for both orthogonal and non-orthogonal network slicing with eMBB devices. The multiple URLLC devices transmit simultaneously and across multiple frequency channels. We set the reliability requirements for the two services and evaluate the pairs of achievable sum rates. We show that, even with overlapping transmissions from multiple eMBB and URLLC devices, the use of NOMA techniques allows us to guarantee the reliability requirements for both services.

Abstract The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.

In this article, we investigate the secure spectral efficiency of an ultrareliable low-latency communication system, where communications occur with short packets due to delay constraints, so that a finite blocklength formulation is considered. In addition, we assume that no feedback channel is available to implement automatic repeat request schemes, so that packet replication (PR) and interface diversity (ID) strategies are used to improve performance, which are then compared in terms of physical-layer security while considering a Nakagami-$m$fading channel. Furthermore, we assume no knowledge of the instantaneous channel state information at Alice, neither with respect to Bob nor Eves, while the position of multiple colluding eavesdroppers are specified according to a Poisson point process. Numerical results show that the joint optimization of the blocklength, the transmit power, and the amount of information bits per codeword are crucial to maximize the secure spectral efficiency. In addition, we also show that ID outperforms the PR strategy in most scenarios when the number of replications/interfaces increases.

LoRaWAN is a remarkable network protocol to support the deployment of low-power wide-area network applications for the Internet of Things. Its modulation scheme, LoRa, uses different transmission parameters to allow long-range bidirectional communication by trading-off range for Time on Air (ToA). The LoRaWAN specification suggests using an adaptive data rate (ADR) algorithm to assign transmission parameters to nodes dynamically. The ADR algorithm used by The Things Network, a global open network, selects transmission parameters for communication without disconnection while lowering ToA and energy consumption. However, its often optimistic link quality estimate drives the assignment of inadequate parameters, especially in lossy channels. More precise channel quality estimates would improve resource allocation at the cost of increased complexity at the network server (NS). Through simulations, we observe that we can use the ADR link margin parameter to compensate for inaccurate link quality estimates, providing better service to nodes and maintaining low levels of energy consumption. In this work, we propose a modification to ADR that allows the selection of the link margin at runtime, increasing the network performance without the need of any prior knowledge and with no increase in the NS computational complexity.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Radio-frequency wireless energy transfer (RF-WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze channel state information (CSI)free multiantenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AASS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be π-phase shifted for optimum average energy performance under AA-SS. Our numerical results evidence that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or the number of antennas is not always beneficial under such a scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS, and/or a large number of antennas. Finally, AA-SS (SA and AA-IS) is (are) preferable when devices are (are not) clustered in specific spatial directions.

Low-power wide-area networks (LPWANs) are wireless connectivity solutions for Internet-of-Things applications, including industrial automation. Among the several LPWAN technologies, LoRaWAN has been extensively addressed by the research community and the industry. However, the reliability and scalability of LoRaWAN are still uncertain. One of the techniques to increase the reliability of LoRaWAN is message replication, which exploits time diversity. This article proposes a novel hybrid coded message replication scheme that interleaves simple repetition and a recently proposed coded replication method. We analyze the optimization of the proposed scheme under minimum reliability requirements and show that it enhances the network performance without requiring additional transmit power compared to the competing replication techniques.

This paper investigates the performance of non-orthogonal multiple access (NOMA) in a 2-user uplink millimeter-wave (mmWave) network. In particular, we consider a scenario where the Base Station (BS) is equipped with an analog beamformer with a single radio-frequency chain and serves two single-antenna NOMA users, without knowledge of the channel state information (CSI) at the BS. As the power allocation inherently intertwines with Initial Access (IA), in this paper, a joint IA and power allocation problem is considered to optimize the achievable sum rate with minimum rate constraint for each user. To solve this non-convex problem, we first solve the IA problem to obtain a sub-optimal beamforming. Then, we achieve the sub-optimal power allocation for a 2-user uplink NOMA network. These optimization problems are solved using Particle Swarm Optimization (PSO). Extensive performance evaluations are conducted to compare the achievable sum rates of the NOMA and OMA systems. Results show that the performance of the NOMA system with the proposed sub-optimal solution is better than the OMA system and achieves near-ideal sum rate performance.

In URLLC the performance of short message communications highly depends on the training sequence length due to the stringent latency and reliability requirements. In this paper, we study the performance of cooperative and non-cooperative transmissions under imperfect channel estimation and Rayleigh fading for URLLC. We assume a peak power constraint on pilot symbols in addition to the average power constraint which is used for comparison purposes. We obtain the optimal training length as a function of blocklength and power constraint factor to meet the URLLC requirements. Moreover, the simulation results show the impact of pilot overhead on reliability, latency, and goodput of cooperative communications compared to point-to-point transmission.

Wireless energy transfer (WET) is emerging as a potential solution for powering small energy-efficient devices. We propose strategies that use multiple antennas at a power station, which wirelessly charges a large set of single-antenna devices. The proposed strategies operate without channel state information (CSI), and we attain the distribution and main statistics of the harvested energy under Rician fading channels with sensitivity and saturation energy-harvesting (EH) impairments. A switching antenna strategy, where a single antenna with full power transmits at a time, provides the most predictable energy source, and it is particularly suitable for powering sensor nodes with highly sensitive EH hardware operating under non-LOS (NLOS) conditions while other WET schemes perform alike or better in terms of the average harvested energy. While switching antennas is the best under NLOS, transmitting simultaneously with equal power in all antennas is the most beneficial as LOS increases. Moreover, spatial correlation is not beneficial unless the power station transmits simultaneously through all antennas, raising a tradeoff between average and variance of the harvested energy since both metrics increase with the spatial correlation. Moreover, the performance gap between CSI-free and CSI-based strategies decreases quickly as the number of devices increases.

With the advent of network densification and the development of other radio interface technologies, the major bottleneck of future cellular networks is shifting from the radio access network to the backhaul. The future networks are expected to handle a wide range of applications and users with different requirements. In order to tackle the problem of downlink user-cell association, and allocate users to the best cell, an intelligent solution based on reinforcement learning is proposed. A distributed solution based on Q-Learning is developed in order to determine the best cell range extension offsets (CREOs) for each small cell (SC) and the best weights of each user requirement to efficiently allocate users to the most appropriate SC, based on both backhaul constraints and user demands. By optimizing both CREOs and user weights, a user-specific allocation can be achieved, resulting in a better overall quality of service. The results show that the proposed algorithm outperforms current solutions by achieving better user satisfaction, mitigating the total number of users in outage, and minimizing user dissatisfaction when satisfaction is not possible.

We consider a two-phase Wireless-Powered Communication Network under Nakagami-m fading, where a wireless energy transfer process first powers a sensor node that then uses such energy to transmit its data in the wireless information transmission phase. We explore a fixed transmit rate scheme designed to cope with the reliability and delay constraints of the system while attaining closed-form approximations for the optimum wireless energy transfer and wireless information transmission blocklength. Then, a more-elaborate rate control strategy exploiting the readily available battery charge information is proposed and the results evidence its outstanding performance when compared with the fixed transmit rate, for which no battery charge information is available. It even reaches an average rate performance close to that of an ideal scheme requiring full Channel State Information at transmitter side. Numerical results show the positive impact of a greater number of antennas at the destination, and evidence that the greater the reliability constraints, the smaller the message sizes on average, and the smaller the optimum information blocklengths. Finally, we corroborate the appropriateness of using the asymptotic blocklength formulation as an approximation of the non-asymptotic finite blocklength results.

Currently, there is a wide variety of low-cost radio technologies being used to enable wireless communication in some important emerging applications such as smart grids, smart cities and the Internet of Things (IoT). However, easy access to these new radio technologies brings a security problem due to the fact that it is very easy for a malicious user to perform passive wireless signal scanning on these networks and use this information to launch identity-based attacks. In this paper, we propose a new machine learning based strategy to detect spoofing attacks in wireless sensor networks (WSNs). Based on detailed analytical models for the mobile radio channel, the proposed algorithm combines two classifiers to process and analyze the instant samples of received signal strength to detect attacks. The algorithm is optimized for scenarios where the legitimate node and the attacking node are at the same distance or at a very close distance from each other in relation to the landmark, what is the worst case scenario. The results show that the proposed strategy improves the performance of the attack detection in about 10% regarding a similar approach in the literature.

The coexistence of different types technologies supported by the same infrastructure introduces unwanted interference that affects negatively the communication system. In this manuscript, recently proposed Nyquist-I pulses are evaluated in terms of bit error rate (BER) considering first, the effect of cochannel interference (CCI) and later, inter-symbol interference (ISI) and CCI simultaneously, under the effects of time jitter. The results indicate that considering a fixed interference power, as the number of interfering signals increases, the effect of the CCI also increases. Additionally, when the effects of CCI and ISI are considered simultaneously, both the magnitude of the main lobe and the magnitudes of the lateral side-lobes of the impulse response are preponderant in the calculation of the BER.

In this paper we investigate the secure throughput of selective decode-and-forward relaying where the legitimate transmitter has no knowledge of the instantaneous channel state information with respect to neither the legitimate receiver nor the eavesdropper. Moreover, we assume a delay-critical scenario, such as in some applications of machine- to-machine communications, so that users communicate with a finite and short blocklength. The performance of direct and selective decode- and-forward cooperative communication strategies are compared. Our results show that the optimization of the blocklength, subject to a maximum length imposed by the particular application, yields important gains in terms of the secure throughput, while cooperative communications is shown to outperform direct non- cooperative transmission.

Wireless networks are becoming extremely pervasive while traffic demand is ever increasing. In order to cope with the forecast increase in traffic volume for the upcoming years, as well as the number of connected devices, new technologies, practices and spectrum rearrangements are required. In this context, a key question arises: how to provide extensive backhaul connectivity and capacity for pervasive ultra dense networks? The answer is rather complex, if feasible. To shed some light into this issue we overview potential technologies, either wired or wireless, and identify technical challenges. Moreover, we evaluate an illustrative scenario of a ultra-dense network that operates with hybrid wired-wireless backhaul. We assume multiple radio access technologies at small and macro base stations (BSs), and we discuss optimal traffic splitting and routing solutions for different topologies and traffic profiles.

We analyze a wireless communication system with finite block length and finite battery energy, under quasi-static Nakagami-m fading. Wireless energy transfer is carried out in the downlink while information transfer occurs in the uplink. Transmission strategies for scenarios with/without energy accumulation between transmission rounds are characterized in terms of error probability and energy consumption. A power control protocol for the energy accumulation scenario is proposed and results show the enormous impact on improving the system performance, in terms of error probability and energy consumption. The numerical results corroborate the existence and uniqueness of an optimum target error probability, while showing that a relatively small battery could be a limiting factor for some setups, especially when using the energy accumulation strategy.

We study the cross-layer energy efficiency of cooperative MIMO networks. At the physical layer, the transmit power determines the outage probability and the number of contending nodes per unit area. At the medium access control (MAC) layer, accessing the channel takes additional time and energy, which impacts the throughput and the energy efficiency. Then, we propose a new cooperative MAC protocol based on IEEE 802.11 in order to coordinate cooperation and to improve the energy efficiency. Moreover, we focus on slow fading scenarios and we analyze the performance of antenna selection (AS), singular value decomposition (SVD) diversity beamforming, and spatial multiplexing (SM) transmission schemes. Results show that SVD and SM have smaller delay and, consequently, larger throughput; however, AS is more energy efficient.

In this paper, we consider a scenario where two multiple-aperture legitimate nodes (Alice and Bob) communicate by means of free-space optical communication in the presence of a multiple-aperture eavesdropper (Eve), which is subject to pointing errors. Two different schemes are considered depending on the availability of channel state information (CSI) at Alice: 1) the adaptive scheme, where Alice possesses the instantaneous CSI with respect to Bob and 2) the fixed-rate scheme, where such information is not available at Alice. The performance of the aforementioned schemes is evaluated in terms of a recently proposed metric named effective secrecy throughput (EST), which encompasses both the reliability and secrecy constraints. By constraining the system to operate below a given maximum allowed secrecy outage probability, we evaluate the EST analytically and through numerical results, showing that the use of multiple apertures at Alice is very important toward achieving the optimal EST.

We analyze the area energy efficiency (AEE) of spatial multiplexing (SM) and transmit antenna selection (TAS), considering a realistic power consumption model for small base stations (BSs), which includes the power consumed by the backhaul as well as different interference attenuation levels. Our results show an optimum number of BSs for each technique that maximizes the AEE. Moreover, we also show that TAS has a larger AEE than SM when the demand for system capacity is low, while SM becomes more energy efficient when the demanded capacity is larger. Additionally, when the capacity demand and the area to be covered are fixed, the number of BSs needed to be deployed is smaller for SM than for the other techniques. Finally, the system performance in terms of AEE is shown to be strongly dependent on the amount of interference, which in turn depends on the employed interference-mitigation scheme, and on the employed power consumption model.

We investigate the use of discrete-time analog joint source channel coding (JSCC) in an amplify-and-forward two-hop wireless network subject to Rayleigh fading channels. The discrete-time analog source is compressed using either 2:1, 3:1 or 4:1 low delay dimension-compression parametric and non-parametric analog JSCC. Our results show that both, non-parametric and parametric, analog schemes outperform an ideal fully digital system based on scalar quantization, while the performance obtained with non parametric mappings is superior to that achieved with parametric systems.

We consider a dual-hop wireless network where an energy constrained relay node first harvests energy through the received radio-frequency signal from the source, and then uses the harvested energy to forward the source's information to the destination node. The throughput and delay metrics are investigated for a decode-and-forward relaying mechanism at finite blocklength regime and delay-limited transmission mode. We consider ultra-reliable communication scenarios under discussion for the next fifth-generation of wireless systems, with error and latency constraints. The impact on these metrics of the blocklength, information bits, and relay position is investigated.

We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design.

In this letter, we investigate the secure energy efficiency (SEE) in a cooperative scenario where all nodes are equipped with multiple antennas. Moreover, we employ secrecy rate and power allocation at Alice and at the relay in order to maximize the SEE, subjected to a constraint in terms of a minimal required secrecy outage probability. Only the channel state information (CSI) with respect to the legitimate nodes is available. Then, we compare the artificial-noise (AN) scheme with CSI-aided decode-and-forward (CSI-DF), which exploits the CSI to choose the best communication path (direct or cooperative). Our results show that CSI-DF outperforms AN in terms of SEE in most scenarios, except when eavesdropper (Eve) is closer to the relay or with the increase of antennas at Eve. Additionally, we also show that the maximization of SEE implies in an optimal number of antennas to be used at each node, which is due to the tradeoff between secure throughput and power consumption.

We analyze and optimize a wireless system with energy transfer in the downlink and information transfer in the uplink, under quasi-static Nakagami-m fading. We consider ultrareliable communication scenarios representative of the fifth generation of wireless systems, with strict error and latency requirements. The error probability and delay are investigated, and an approximation for the former is given and validated through simulations. The numerical results demonstrate that there are optimum numbers of channels uses for both energy and information transfer for a given message length.

The Advanced Metering Infrastructure (AMI) is a key component of the Smart Grid architecture. The Neighborhood Area Network (NAN) is the portion of the AMI that enables two-way communication between electric, gas and water meters and City Utilities. Many companies are currently deploying wireless NAN architectures based on the IEEE 802.15.4g technology while the Routing Protocol for Low-Power and Lossy Networks (RPL) has been considered as the mesh routing protocol. In this paper, we investigate the dynamics of RPL for routing path maintenance in the presence of jamming attacks. A precise interference model is implemented and tested under a fully compliant RPL draft implementation. The quality of the survivors routing paths in terms of ETX metric is investigated for different density of gateways. The results provide insights for an efficient NAN design in order to minimize the impacts of jamming attacks in the RPL performance.

The Advanced Metering Infrastructure (AMI) is a fundamental component of the Smart Grid architecture. The AMI consists of a collection of Neighborhood Area Networks (NANs), which interconnects the smart meters to the utility company. In this paper, we address two important performance metrics regarding the NAN design, the topology's resilience and the network capacity. We propose an analysis methodology in order to determine the appropriate transmission power and the required number of gateways for wireless-enabled mesh-connected architectures. We employ a graph-theoretic approach for the analysis. Furthermore, we assume wireless NANs based on the new IEEE 802.15.4g standard. A planning tool has been implemented using software Mathematica in order to automate our approach. Simulation results show interesting tradeoffs between the performance metrics and the network design parameters, thus providing useful insights for the NAN designer.

In this paper we study the energy efficiency of multiple antennas (MIMO) networks considering both the physical (PHY) and the medium access control (MAC) layers. At the PHY layer, the transmit power determines the communication rate and the area containing nodes that contend for channel access. In turn, accessing the channel takes additional time and energy at the MAC layer, which impacts the throughput and the energy efficiency. Moreover, we focus in slow fading scenarios, so that we remodel former cross-layer models in order to be representative of quasi-static MIMO networks, in which we employ the e-capacity as the system metric. In addition, we analyze the impact of antenna selection (AS) and singular value decomposition (SVD) beamforming MIMO diversity transmission schemes in terms of throughput and energy efficiency. Results show that SVD has smaller delay and higher throughput; however, this scheme is significantly outperformed in terms of energy efficiency by AS.

Many applications of wireless sensor networks require that nodes, besides monitoring a given phenomenon, must be able to detect and communicate asynchronous events (e.g. alarms), implying that they have to often listen to the medium in idle mode, which is inherently energy wasteful. In such a scenario time synchronization is crucial to efficiently operate in duty-cycles and minimize energy consumption. In this work we assess the impact of the trade-off between spending energy with more frequent synchronizations and in return saving it by reducing the idle listening window necessary for the desired reliability of the communication. The optimal frequency of time synchronizations is obtained analytically and corroborated by numerical results, showing that several times less overall energy may be spent with a finer synchronization when compared with maintaining the minimum clock precision required by the phenomenon being monitored, greatly extending the life-span of the network.

In this paper, we consider a vehicular network and propose the application of directional antennas to a recently proposed information-theoretic location verification system (LVS), aiming at increasing the system capability in correctly detecting malicious nodes. In the proposed LVS with directional antennas (LVS-DA), we adopt a more realistic dual-slope large-scale fading model that is suitable to vehicular networks. The performance of the proposed scheme is evaluated analytically for some specific number of directional antennas and confirmed by numerical results, showing that the proposed LVS-DA scheme is capable of considerably increasing the system capability in detecting malicious nodes when compared with other recently proposed schemes, under different scenarios.

Retransmissions due to decoding errors have a big impact on the energy budget of low-power wireless communication devices, which can be reduced by using hybrid automatic repeat request (HARQ) techniques. Nevertheless, this reduction comes at the cost of extra energy consumption introduced by the added computational load. No complete analysis of the tradeoff between retransmissions reduction and baseband consumption of low-power communications over fading channels has been reported so far. In this paper, we study the energy efficiency achievable by HARQ schemes when the code rate of the error-correcting code is optimized. For this purpose, we develop an energy consumption model that focuses on simple HARQ (S-HARQ) and Chase combining (HARQ-CC) transmissions, which are studied under fast-fading and block-fading scenarios with Nakagami-m fading. The retransmission statistics are analyzed, and expressions for the expected number of transmission trials are derived. Using this framework, it is shown that transmission schemes with high diversity gain are the most efficient choice for long range transmissions, which in our case correspond to HARQ-CC and codes with low code rate. On the other hand, schemes with good multiplexing capabilities are optimal for short link distances, which in our analysis correspond to S-HARQ and high code rates. It is also shown that HARQ-CC can effectively extend the transmission range of a low-power communication device.

Saving energy is vital in real-world underwater acoustic networks, since a lot of energy is spent to transmit acoustic waves and battery replacement is highly undesirable. In this paper, we study how the energy consumption of underwater acoustic communications can be reduced in order to extend the lifetime of underwater sensor nodes. We analyze the effect of selecting the best frequency within a given operating range for various link distances. We also consider the use of error correcting codes, and optimize the code rate and the signal to noise ratio for each link distance and frequency. Our results show that energy consumption can be greatly reduced by optimizing code rate. In particular, it is shown that uncoded transmissions can be more energy efficient for a considerable range of link distances than fixed rate convolutional codes which are popular in commercial devices. Results also show that the optimal number of average retransmissions is very small (less than one) for all studied link distances. This is an encouraging result, as acoustic communication channels impose much longer delays than RF channels and therefore schemes which require several retransmissions impose unfeasible delay constrains.

This paper investigates the performance of dual-hop full-duplex relaying schemes subject to co-channel interference (CCI) and noise. In our analysis, two main scenarios are considered: in the first scenario, the link between the source and destination is seen as interference and, in the second one, it is seen as useful information. In both schemes, the effect of self-interference at the relay is taken into account. Accurate, closed- form expressions for the outage probability are derived for the general case, in which CCI and noise are assumed at both the relay and destination. The derived expressions allow for independent non-identically distributed Nakagami-m fading. Based on these expressions, special cases (but, yet new) assuming CCI only at the relay and assuming CCI only at the destination are examined. It is shown that CCI at the relay is more harmful for the system performance than CCI at the destination.

We assume a full-duplex (FD) cooperative network subject to hostile attacks and undergoing composite fading channels. We focus on two scenarios: a) the transmitter has full CSI, for which we derive closed-form expressions for the average secrecy rate; and b) the transmitter only knows the CSI of the legitimate nodes, for which we obtain closed-form expressions for the secrecy outage probability. We show that secure FD relaying is feasible, even under strong self-interference and in the presence of sophisticated multiple antenna eavesdropper.

This letter deals with the secrecy capacity of the radio channel in interference-limited regime. We assume that interferers are uniformly scattered over the network area according to a Point Poisson Process and the channel model consists of path-loss, log-normal shadowing and Nakagami-m fading. Both the probability of non-zero secrecy capacity and the secrecy outage probability are then derived in closed-form expressions using tools of stochastic geometry and higher-order statistics. Our numerical results show how the secrecy metrics are affected by the disposition of the desired receiver, the eavesdropper and the legitimate transmitter.

We evaluate the effective secrecy throughput of the multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel with antenna selection at the legitimate transmitter. The optimal target secrecy rate that maximizes the effective secrecy throughput is obtained and confirmed by numerical results. Differently from previous works, we constrain the maximum allowed secrecy outage probability (SOP) and show that, the single-input single-output multi-antenna eavesdropper (SISOME) scheme recently proposed in the literature presents a much lower resilience to the decrease of the maximum allowed SOP than that obtained by MIMOME scheme.

In this paper we investigate the physical layer security in a cooperative scenario where the source and the eavesdropper are equipped with multiple antennas, while the relay and the destination are single-antenna devices. We consider that no channel state information (CSI) is available at the transmitter, besides the index of the selected transmit antenna. Closed-form expressions for the secrecy outage probability of the selective decode-and-forward (SDF) and incremental decode-and-forward (IDF) schemes are derived. In addition, we also conduct an asymptotic analysis to get further insights on the performance of each scheme, which predicts a performance floor inherent to the scenario without CSI at the transmitter. Moreover, we also analyze the schemes in terms of the secrecy throughput, which shows that the performance floor that appear in the outage analysis only affects the secrecy throughput if the floor is too high, as is the case of the non-cooperative transmission. Our analysis show that IDF can considerably outperform the SDF and the non-cooperative transmission both in terms of secrecy outage probability and secrecy throughput.

This paper proposes an iterative power allocation algorithm that maximizes the energy efficiency of a multiple-relay cooperative multiple antenna (MIMO) network. Energy efficiency is defined as the achievable spectral efficiency normalized by the total power consumption, which also accounts for the RF circuitry consumption, modeled in a wireless sensor network. Moreover, relay selection is performed using the distributed opportunistic selection protocol, while we derive closed-form expressions for outage probability and energy efficiency for a single antenna (SISO) and two MIMO scenarios: 1) based on antenna selection (AS) and 2) based on single value decomposition (SVD) beamforming. In order to evaluate the performance of the proposed algorithm, we compare it to an exhaustive search optimal power allocation method. Results show that the proposed algorithm performs very close to an exhaustive search approach and that, although MIMO SVD has the best outage performance, AS has the best energy efficiency for low and moderate source-destination distances and spectral efficiencies.

Presents corrections made to the paper, “Energy Efficiency-Spectral Efficiency Trade-Off of Transmit Antenna Selection” (Rayel, O.K., et al; IEEE Trans. Commun., vol. 62, no. 12, pp. 4293–4303, Dec. 2014).

We propose a method for planning large mesh-based WSN with multiple sinks. The relationship between the WSN capacity, sensor power transmission and the outage probability of the path between sensor nodes and sinks is very complex. In order to reduce the problem complexity, we use a multistage approach, where sensor node clustering, sink position selection, sensor node power and channel assignment are solved as independent problems. The multistage approach is used in accordance to two different strategies, named Geometry-Based Clustering (GBC) and Path-Based Clustering (PBC). In the first strategy, the clustering of sensor nodes and the sink selection is based exclusively on the sensor nodes geometric disposition. The second strategy solves the same problem by determining the sink positions by a search method that optimizes the WSN capacity under outage probability constraints.

In this paper we investigate the performance of a cooperative network in the presence of an eavesdropper (Eve). Alice and Bob communicate with the help of a relay, which can operate either in half-duplex (HD) or full-duplex (FD) mode. We account for the self interference at the relay when operating under FD mode. Our analysis focus in the case that the CSI of Eve is not available at Alice. Thus, we derive closed-form expressions for secrecy outage probability. Our results allow us to compare the performance of FD and HD cooperative scenarios under secrecy constraints and, despite the additional interference at the relay, show the advantages of FD relaying over HD. In addition, we also show that a cooperative network is more vulnerable if Eve is closer to Alice than to the relay.

We evaluate the secrecy outage probability of a two-user network-coded cooperative network in the presence of an eavesdropper. We show through theoretic and numerical analyses that the secrecy can be increased through the use of network coding when compared to the direct transmission and traditional cooperative techniques.

In this paper we analyze the energy efficiency of multi-hop communication in wireless sensor networks when packet transmissions between adjacent nodes employ Hybrid ARQ (HARQ) with Chase combining (CC) technique under Rayleigh block-fading channels. The energy analysis can be constrained by either a target outage probability or end-to-end delay. Closed form expressions for the end-to-end outage probability and energy consumption are derived. Results show how to determine the optimal number of retransmissions of the HARQ-CC scheme for a given number of relays between the source and destination nodes, in order to achieve the target constraints. The impact of the path loss environment conditions on the overall energy consumption is also investigated. Finally, we analyze the energy consumption assuming a visual sensor network application with multiple cameras and different outage and delay constraints.

Error correcting coding is a well-known technique for reducing the required signal-to-noise ratio (SNR) needed to attain a given bit error-rate. Nevertheless, this reduction comes at the cost of extra energy consumption introduced by the baseband processing required for encoding and decoding the data. No complete analysis of the trade-off between coding gain and baseband consumption of communications over wireless channels has been reported so far. In this paper, we study the energy-consumption of BCH codes with various code rates over AWGN and Rayleigh fading channels. Our results show that codes with low code rate are optimal for performing long-range communications, while is better to use less coding redundancy when the transmission distance is short. Our results also show that the transmission range of a low-power communication device can be increased up to 25% for AWGN channels and up to 300% for Rayleigh fading channels by using an optimized BCH code.

We investigate the energy efficiency-spectral efficiency (EE-SE) trade-off of transmit antenna selection/maximum ratio combining (TAS) scheme. A realistic power consumption model (PCM) is considered, and it is shown that using TAS can provide significant energy savings when compared to multiple-input multiple-output (MIMO) in the low to medium SE region, regardless the number of antennas, as well as outperform transmit beamforming scheme (MRT) for the entire SE range. For a fixed number of receive antennas, our results also show that the EE gain of TAS over MIMO becomes even greater as the number of transmit antennas increases. The optimal value of SE that maximizes the EE is obtained analytically, and confirmed by numerical results. Moreover, the influence of receiver correlation is also evaluated and it is shown that considering a non-realistic PCM can lead to mistakes when comparing TAS and MIMO.

In this paper we compare the energy efficiency of different multiple antenna transmission schemes for long-range wireless networks, assuming a realistic power consumption model. We consider the downlink, between a base station and a mobile station, in which the Alamouti scheme, transmit beamforming, receive diversity, spatial multiplexing, and transmit antenna selection are compared. Our analysis shows that, for different types of base stations, outage probability requirements and spectral efficiencies, the transmit antenna selection scheme is in general the most energy efficient option. Although antenna selection is not the best in terms of outage probability, it becomes the most efficient in terms of overall power consumption as it requires a single radio-frequency chain to obtain spatial diversity.

In this paper, we apply nonbinary network coding to a multiple access cooperative vehicular ad hoc network (VANET), where the vehicles have independent information to transmit to a common destination. We consider that all the users in the network are provided with multiple antennas (MIMO), so that the benefits of both space-time codes and cooperative communication are used to increase the network diversity order and consequently provide more reliable communication. A Nakagami-m fading model is considered, aiming to evaluate the reliability of the proposed scheme mainly in scenarios where the channel condition is critical (sub-Rayleigh scenario with m<;1). The network outage probability is obtained analytically, and confirmed by numerical results.

In this letter, we evaluate the energy efficiency of a wireless network-coded cooperative system where multiple nodes cooperatively transmit their information to a common destination. The energy consumption of the transmission and reception circuits is taken into account. It is shown that network coding can provide significant energy savings compared to direct transmission and traditional cooperation techniques. We consider a Nakagami- m fading model, so that the influence of a line-of-sight is also investigated. The optimal number of users that minimizes the energy consumption is obtained analytically, and confirmed by numerical results. Using this number to organize the nodes in clusters can provide considerable energy savings and decrease the network encoding/decoding complexity.

Location awareness allows to define a concurrent transmission region where a primary and a secondary networks can coexist. We investigate the impact of joint rate and power control on the performance of a cognitive radio ad hoc network overlaying a primary system. The proposed strategies adequately adjust the secondary user transmit power to increase the concurrent transmission probability while respecting a given constraint on the achievable primary rate. Two power control strategies are described: Rate-Efficient Power Control to maximize the secondary capacity, and Energy-Efficient Power Control to minimize the secondary energy consumption. Numerical results show that the proposed schemes expand the concurrent transmission region and that it is possible to increase the average rate and save energy.

We consider the use of spatial diversity to improve the performance of analog joint source-channel coding in wireless fading channels. The communication system analyzed in this paper consists of discrete-time all-analog-processing joint source-channel coding where Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are employed. By assuming a fast-fading Rayleigh channel, we show that MMSE performs much better than ML at high Channel Signal-to-Noise Ratios (CSNR) in single-antenna wireless systems. However, such performance gap can be significantly reduced by using multiple receive antennas, thus making low complexity ML decoding very attractive in the case of receive diversity. Moreover, we show that the analog scheme can be considerably robust to imperfect channel estimation. In addition, as an alternative to multiple antennas, we also consider spatial diversity through cooperative communications, and show that the application of the Amplify-and-Forward (AF) protocol with single antenna nodes leads to similar results than when two antennas are available at the receiver and Maximal Ratio Combining (MRC) is applied. Finally, we show that the MMSE implementation of the analog scheme performs very close to the unconstrained capacity of digital schemes using scalar quantization, while its complexity is much lower than that of capacity-approaching digital systems.

The performance and complexity of turbo decoding using rate k/n constituent codes are investigated. The conventional, minimal and sectionalized trellis modules of the constituent convolutional codes are utilized. The performance metric is the bit error rate (BER), while complexity is analyzed based on the number of multiplications, summations and comparisons required by the max-log-MAP decoding algorithm. Our results show that the performance depends on how the systematic bits are grouped in a trellis module. The best performance is achieved when the k systematic bits are grouped together in the same section of the module, so that the log-likelihood ratio (LLR) of the k-bit vector is calculated at once. This is a characteristic of the conventional trellis module and of some of the sectionalizations of the minimal trellis module. Moreover, we show that it is possible to considerably reduce the decoding complexity with respect to the conventional trellis if a particular sectionalization of the minimal trellis module is utilized. In some cases, this sectionalization is found within the best performing group, while in some other cases a small performance loss can be traded off for a large complexity reduction.

This paper compares the energy efficiency of some cooperative MIMO schemes in wireless networks. By energy efficiency we denote the spectral efficiency seen at the receiver normalized by the total energy consumption, which includes the circuitry, the efficiency of the power amplifier, and the transmission rate. We focus on transmit antenna selection (TAS) and switch and stay combining (SSC) at the receiver. The performance of TAS+SSC is compared to that of TAS and maximal ratio combining (MRC), and to that of transmit/receive beamforming using a singular value decomposition (SVD) technique. We derive closed-form outage probability expressions, and analyze the effect of selecting the antenna from the source to optimize the communication with the relay or with the destination. Our results show that selecting the antennas with respect to the destination is in general a better option when the energy consumption is accounted. Moreover, some power allocation strategies are described and the comparison of the schemes in terms of energy efficiency reveals a considerable improvement with the use of TAS+SSC for low to moderate spectral efficiency, while beamforming outperforms the other schemes for high spectral efficiency when the number of antennas at each node is small.

An important design goal for wireless local area networks (WLANs) is the performance. It is well known that the major issue challenge to the performance in WLANs is the wireless interference. This leads to an ambience that depends on the channel assignments among interfering access points (AP). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if an unappropriate spectrum planning is considered. In our study we considered WLANs scenarios where APs belong to different administrative domains, a common situation in dense urban deployments. In such environment the use of centralized algorithms is not appropriate and the already proposed distributed methods do no guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new cooperative channel allocation strategy using the distributed pseudo tree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.

In this work we compare full-duplex (FD) and half-duplex (HD) relaying in terms of outage probability and throughput. We consider a practical FD relay model where the loop interference between transmitted and received signals is taken into account. We analyze two modes for FD transmission, block Markov encoding and multi-hop transmission without interference cancellation, and two different modes for HD transmission, which are based on selective and incremental decode-and-forward protocols. Results show that there is a tradeoff between SNR and information rate in which each scheme becomes more suitable.

We analyze the energy efficiency of reactive and proactive relay selection algorithms under the incremental decode-and-forward protocol. By taking into account the consumption of the RF circuitry, the transmit power, and a nonlinear model for the battery, we show that a large number of available relays can actually compromise the energy efficiency of the system. Our results show that, depending on the source to destination distance, it may be preferable to consider only a small subset of nodes and select the relay among these nodes.

In this paper we propose an energy efficient image transmission strategy for wireless sensor networks, which combines wavelet-based image decomposition and cooperative communication. In the proposed scheme we use selective decode-and-forward (SDF) cooperation, so that a relay node collaborates with the source by forwarding only a lower-resolution version of the original image, obtained via discrete wavelet transform (DWT). We show that the proposed SDF-DWT strategy is more energy efficient than non-cooperative single-hop and multi-hop, also outperforming the regular SDF scheme. In addition, we show that our method can achieve the energy efficiency of incremental DF (IDF), without the need of a feedback channel.

The performance of a wireless local area network depends on the channel assignments among neighboring access points (APs). The limited number of non-overlapping channels may lead to severe interference scenarios if no appropriated spectrum planning is employed. Additionally, in dense urban areas it is usual to find wireless networks scenarios with interfering APs belonging to different administrative domains. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods do not guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem and propose a new cooperative channel allocation strategy using the distributed pseudotree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.

We analyze the physical layer (PHY) security of a communication scheme consisting of a multiple antenna transmitter with a single radio frequency (RF) chain using transmit antenna selection (TAS) and a single antenna receiver, in the presence of a sophisticated multiple antenna eavesdropper. We develop closed-form expressions for the analysis of the secrecy outage probability, and we show that the PHY security can be considerably enhanced when multiple antennas are available at the legitimate transmitter. Moreover, a single RF chain multiple antenna transmitter reduces cost, complexity, size and power consumption at the expense of a slight loss in performance with respect to a multiple RF chain transmitter.

We consider discrete-time all-analog-processing joint source-channel coding, using non-linear spiral-like curves. We assume a Rayleigh channel, where the receiver may employ or not multiple antennas. Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are considered. Our results show that MMSE performs much better than ML in high CSNR in single-antenna wireless systems, while diversity combining is able to significantly reduce such performance gap, therefore making the low complexity ML decoding very attractive in the case of multiple receive antennas.

We consider high-rate systematic recursive convolutional encoders to be adopted as constituent encoders in turbo schemes. It has been shown by Douillard and Berrou that the construction of high-rate turbo codes by means of high-rate constituent encoders offers several advantages over the typical construction based on the puncturing of rate-1/2 constituent encoders. To reduce the decoding complexity associated with high-rate codes, we adopt the “minimal” trellis representation of convolutional codes introduced by McEliece and Lin. While in the literature this trellis has been obtained for nonrecursive nonsystematic generator matrices, we herein introduce the construction of the “minimal” trellis for a systematic recursive convolutional encoding matrix. We also derive expressions for the arithmetic decoding complexity when the max-log-MAP algorithm is applied over the conventional and the “minimal” trellises. Examples are provided, which show that significant savings in decoding complexity are obtained, while keeping the same error performance of conventional schemes, when the minimal trellis is used. Finally, a code search is conducted and examples are provided which indicate that a refinement in terms of decoding complexity-error performance trade-off is obtained.

In this paper we analyze the energy efficiency of single-hop, multi-hop and incremental decode-and-forward in wireless sensor networks composed of multiple nodes. The energy efficiency analysis is constrained by a target outage probability and an end-to-end throughput, while the Nakagami-m distribution is used to model the small scale fading. Our results show that in most scenarios the cooperative incremental decode-and-forward scheme is more energy efficient than the other methods.

Truncated Type-I and Type-II HARQ schemes are compared, in terms of throughput, when incremental decode and forward relaying is used. The comparison is based on the outage analysis of both schemes. Two different scenarios are considered: ad-hoc relaying, where all users are at ground level, and infra-structured relaying, where the relay and the destination antennas are at a higher height than the user. Moreover, we also consider the effect of rate adaptation. Results show that Type-II (incremental redundancy) only significantly outperforms Type-I schemes (selection and Chase combining) in the case of ad-hoc relaying without rate adaptation, and in the low SNR region.

We propose a partial retransmission (PR) scheme to be used in block fading channels. The proposed method sends only a fraction of the original transmitted codeword at each retransmission, and makes use of an appropriate power allocation and of symbol by symbol combining at the receiver. The performance is investigated by means of the outage probability and the throughput. The proposed PR scheme is shown to outperform both the incremental redundancy (IR) and Chase combining (CC) HARQ methods, for some signal to noise ratio values and for the scenarios considered in this paper.

We compare the energy efficiency of some transmission schemes in wireless sensor networks. By constraining the system to end-to-end throughput requirements, we assume that the nodes in multi-hop and cooperative schemes operate at a spectral efficiency which is twice that of the single-hop transmission. By taking into account the energy consumption of the RF circuitry, we show that cooperation may be considerably more energy efficient than non-cooperative schemes even in small transmission ranges, specially if a feedback channel is available.

In this paper we present a novel Hybrid Automatic Repeat reQuest (HARQ) scheme for cooperative communication systems, called Coded Partial Retransmission (CPR). The method is based on the retransmission of only a fraction of the original codeword by the cooperating nodes, applying an appropriate power allocation. Numerical results using LDPC codes and Type-I HARQ with Chase Combining show that CPR outperforms regular cooperative HARQ techniques considering both amplify-and-forward (AAF) and selective decode-and-forward (SDF) protocols. Additionally, we show that the CPR throughput, using a single relay, may be considerably larger than the throughput of some regular cooperative HARQ schemes using multiple relays.

A receiver for a MIMO OFDM communication system using direct conversion transceivers is proposed. The novel receiver takes advantage of the I/Q imbalance inherent to the direct conversion operation. It can be shown that the I/Q imbalance in multicarrier communication systems creates an additional frequency diversity. By adequately exploiting such diversity at the receiver, the overall performance can be considerably improved. Simulation results considering a MIMO OFDM system using V-BLAST show that, when exploiting the I/Q imbalance, gains of up to 5 dB at a BER of 10-4can be obtained with respect to the ideal system, without I/Q imbalance.

In this paper we extend the overlay cognitive radio model by introducing multiple secondaries. The new model allows for multiple concurrent transmissions in a wireless network. We apply the proposed scheme for the communication between nodes in a wireless mesh network. Numerical results show that considerable capacity gains are achieved when using the proposed overlay cognitive radio model with multiple secondaries.

We conduct a code search, restricted to the recently introduced class of generalized punctured convolutional codes, under the minimal trellis complexity measure defined by McEliece and Lin. For the same decoding complexity and the same code rate, new codes are compared to well-known existing classes of convolutional codes. Some of the best convolutional codes (in a distance spectrum sense) of existing and new trellis complexities are tabulated.

We propose a hybrid ARQ scheme using recursive convolutional codes, the turbo principle and combining diversity. While spending the same energy for transmission, the proposed scheme presents a better throughput for most of the SNR range and a smaller decoding complexity than another well-known method.

We propose the application of the overlay cognitive radio model to the communication of links within a wireless mesh networks. Based on the communication paradigm proposed by Jovicic and Viswanath, it is possible to have two concurrent transmissions in a given interference region. As as result, considerable network capacity gains are achieved. For the simple case of a network with a chain topology, the gains are as high as 33%.

We propose a hybrid ARQ scheme using recursive convolutional codes and the turbo principle. In the first transmission the system encodes the data through a systematic recursive convolutional encoder. In the second transmission data is interleaved prior to being encoded by a recursive convolutional encoder. Then, from the second transmission on the receiver operates as a turbo decoder. The proposed method is compared with a popular scheme known in the literature. It is shown that, while spending the same energy for transmission, the proposed scheme presents both smaller FER and decoding complexity.

Nonuniform sources can be found in real world applications as uncompressed speech, text and medical images. In this paper we compare the performance of three different methods for the transmission of such sources over AWGN and Rayleigh channels. One of the methods is the classical one considering separation between source and channel coding. The two other methods are based on source-controlled channel decoding, where data is not compressed prior to transmission and redundancy is exploited at receiver. The three methods make use of turbo codes as the channel code. Simulation results show that, in some cases and in terms of bit error rate, it may be more advantageous not to compress data prior to transmission.

A hybrid ARQ scheme for multiple transmit antennas systems is presented. Space-diversity is achieved through the use of random signal mapping (RSM). The use of RSM instead of other typical space-time coding schemes, as space-time trellis codes or space-time block codes, allows that more antennas may be added to the system with negligible complexity increase at the receiver. Coding gain is achieved with a standard parallel turbo code. Numerical results for frame error rate, throughput and number of required retransmissions for a varying number of transmit antennas are presented. The impact of the proposed scheme in the performance of a higher layer protocol, as TCP, is also investigated through computer simulations.

An unequal error protection strategy for minimising the impact of error bursts in the storage or transmission of compressed data has been proposed. The strategy is designed for the LZSS algorithm, which is a dynamic dictionary compression method. Through extensive computer simulations we investigate which information classes within LZSS are more sensitive to burst errors. Based on these results, an unequal error protection strategy that greatly reduces the error propagation during the decompression process has been proposed. Numerical results using text files from a common database are presented.

In this paper, we investigate the behavior of the distance spectrum of convolutional codes when the decoding complexity is measured by the total number of edge symbols per information bit in the minimal trellis module representing the code. We conduct a code search restricted to the recently introduced class of generalized punctured convolutional codes, which is broad enough to contain good codes and yet has structural properties that facilitate the code search. New good convolutional codes are tabulated. For the same decoding complexity and the same code rate, the new codes have slightly better distance spectrum than the best known punctured convolutional codes. When compared to the best known unit-memory convolutional codes of the same rate, the new codes typically have improved free distance for a given decoding complexity, or about the same distance spectrum is achieved with much lower decoding complexity. Under this decoding complexity measure, the behaviour that higher decoding complexity implies better distance spectrum remains valid.

The effect of the shaping filter excess bandwidth in the performance of symbol-sampled receivers over unknown continuous-time channels is investigated. The performance loss is determined in terms of information rate, and relative to the case of perfectly synchronized matched filtering, where the continuous-time channel has to be known a priori. Numerical results are presented for the case of a raised cosine shaping filter and 2-ray and 3-ray continuous-time channel models.

Non-systematic channel codes can have a significant advantage over systematic codes when utilizing redundancy left in the data for channel decoding. However, results previously attained show that even with such codes there is a performance gap to the theoretical limits. We propose a method that combines unequal energy allocation with source controlled decoding of non-systematic turbo codes. The method benefits from both (non-systematic codes and unequal energy allocation) to improve channel decoding performance in the presence of redundancy. Simulations demonstrate superior performance to the performances attained by each of the methods separately. A significant advantage is demonstrated over using non-systematic codes with equal energy allocation. At moderate (more practical) non-uniformities, there is also a clear advantage to the new method over unequal energy allocation with systematic turbo codes

The performance of symbol-sampled receivers is usually evaluated via Forney's finite impulse response (FIR) model for the equivalent channel [discrete-time transversal filter (DTTF)]. This model contains a matched filter, and, thus, requires prior knowledge of the continuous-time channel-impulse response. Therefore, if the channel is continuous and unknown, it is unrealistic to use the DTTF model, which leads to an upper bound on the system performance. Using an alternative model for the equivalent discrete-time channel, where the matched filter is replaced by a receive filter matched to the symbol waveform, we propose a framework to quantitatively investigate the performance loss from a theoretical perspective. The theoretical results are corroborated using a practical system.

Symbol-sampled receivers that estimate the channel blindly or semi-blindly have had their performance evaluated via Forney's FIR model for the equivalent channel, the discrete time transversal filter (DTTF). This model, which allows maximum likelihood sequence estimation, contains a matched filter, and thus requires a priori knowledge of the continuous-time channel impulse response. As a consequence, if the channel is continuous and unknown, and if we keep sampling at the symbol-rate, it is unrealistic to use the DTTF model, which becomes an upper bound on the system performance. Using an alternative model for the equivalent discrete-time channel, where the continuous-time channel is unknown and the matched filter is replaced by a receive filter matched to the symbol waveform, we investigate the performance loss considering both a theoretical and a practical point of view. First, we estimate the theoretical performance loss, in terms of the information rate, relative to the case when the continuous-time channel is known a priori (DTTF). Later, considering a practical system for semi-blind symbol-sampled equalization and turbo decoding, we perform computer simulations which agree with the theoretical results, and show that the loss in performance is negligible when the optimum sampling instant is available.

A source-controlled turbo coding scheme for nonuniform binary memoryless sources sent over AWGN channels is designed. The use of nonbinary signaling allows performance improvements over previous schemes. The source and channel parameters can be estimated in the encoding and decoding processes.

We present a semiblind iterative receiver for space-time coded data transmitted through unknown MIMO frequency selective quasistatic Rayleigh fading channels. The expectation maximization (EM) algorithm is used in the channel estimation process, while a BCJR algorithm for multiple antenna systems is used for both data detection, over the channel trellis, and decoding, over the space-time code trellis. Noise variance estimation is also performed by the receiver. Simulation results showing the diversity gain over MIMO flat fading channels are presented.

This paper presents an analysis that justifies the improved performance of the multi-split LMS algorithm. It is shown that instead of reducing the eigenvalue ratio, the multi-split operation increases the diagonalization factor of the transformed input signal autocorrelation matrix, which assists the power normalized and time-varying step-size LMS algorithm used for updating the single parameters independently. Case studies and simulation results enable us to evaluate the improved performance of the multi-split LMS algorithm.

We consider a design procedure for achieving full 2-level diversity and coding advantage for space-time convolutional codes over GF(p), where p can be any prime number. Using a p-ary constellation (not necessarily PSK), the spectral efficiency is log/sub 2/(p) b/s/Hz. Simple conditions on the generator matrices of a rate R = 1/2 convolutional code are given so that maximum diversity is guaranteed, while reducing the computer search effort. New 2-space-time codes are presented for the 5-PSK and 7-PSK modulations. Simulation results validate the presented code design procedure.

This paper presents the implementation of blind equalizers with a new filtering structure, the split transversal filter. The aim is to improve the robustness of known blind algorithms. Comparisons between the new scheme and the traditional structure for three of the Bussgang techniques are presented. While the traditional equalizers are attracted by a local minimum, the split equalizer can avoid them and reach the global solution.