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.

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.

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.

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 recent years, vehicular communication has attracted significant research attention for its potential as a fifth generation (5G) application. The IEEE 802.11p standard enables the wireless technology that defines vehicular communication and, due to the time-varying characteristic, one of its critical challenges is to ensure communication reliability. Moreover, this standard is based on orthogonal frequency division multiplexing (OFDM) transmission scheme, which may suffer from nonlinear distortions induced by high power amplifiers (HPA) at the transmitter, degrading the channel estimation and detection performance of the receivers. In this work, the application of deep learning (DL) based channel estimation schemes to the IEEE 802.11p standard in presence of HPA distortions is presented. Simulation results show that the deep neural network (DNN) based estimation schemes outperform conventional data-pilot aided (DPA) and spectral temporal averaging (STA) estimators in terms of bit error rate and normalized mean squared error, evidencing their superiority in providing reliable estimation in mobility scenarios in presence of HPA nonlinear distortions. Furthermore, the hybrid solution, employing a joint DNN-based post-processing and conventional estimation, achieves less computational complexity than the DL-based proposal on top of the initial DPA estimation.

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.

Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications. However, the channel state information (CSI) acquisition is one of the major challenges for the practical deployment of the RIS. The passive RIS without any baseband processing capabilities brings difficulty on the channel estimation (CE), since the individual channels or the cascaded one can be estimated only at base station (BS) via uplink training or mobile station (MS) via downlink training. In order to facilitate the CSI acquisition, we focus on the hybrid RIS architecture, where a small number of elements are active and able to receive and process the pilot signals at the RIS. The CE is performed in two stages by following the atomic norm minimization to recover the channel parameters, i.e., angles of departure (AoDs), angles of arrival (AoAs), and propagation path gains. Simulation results show that the proposed scheme can outperform the passive RIS CE under the same training overhead. Furthermore, we also study the theoretical performance limits in terms of mean square error (MSE) via Cramér-Rao lower bound (CRLB) analyses.

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.

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 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.

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference,i.e., interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the$l_{2}$and the Schatten-$p$norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.