Carrier phase positioning (CPP) is widely used in satellite system applications, enabling centimeter-level localization accuracy. Recently, CPP is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes. The offered results highlight that only two carriers need to be aggregated to substantially facilitate resolving the integer ambiguity problem.

Satellite networks have become central to the evolution of modern communications systems due to their potential for extensive global coverage. However, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for performance metrics that characterize ultra-reliable low-latency communications. To address these challenges, data-oriented approach, which is already widely studied in terrestrial networks, provides a fresh perspective by evaluating the transmission performance where it prioritizes both reliability and latency. This work introduces the data-oriented approach to uplink hybrid satellite-terrestrial networks (HSTNs), focusing on non-orthogonal multiple access (NOMA)-assisted semi-grant-free (SGF) transmission where terrestrial relays support the transmission between the satellite and users. Specifically, a novel grant-free user (GFU) admission protocol based on distributed contention control, the corresponding power allocation scheme for GFUs, and the relay selection procedure presented following the data-oriented approach. Then, the maximum number of GFUs that can be admitted under given data-oriented design requirements is determined. The analytical results are verified by extensive Monte-Carlo simulations, while a wide body of numerical results are also offered to understand and demonstrate the impacts of different system parameters. The proposed analytical framework offers useful insights toward the design of practical HSTNs, particularly in the context of delay-sensitive applications, by revealing the relationship between the amount of information data, the power consumption, and the satellite distance. The results demonstrate that the proposed scheme improves the DOR performance compared to conventional grant-free access and frequency division multiple access methods by dynamically selecting GFUs and allocating transmit power based on channel conditions. It is also shown that more GFUs can be admitted, especially with larger bandwidths and/or relaxed threshold settings.

In addition to satellite systems, carrier phase positioning (CPP) is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as the carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks, while noting also practical imperfections such as clock offsets between the user equipment (UE) and the network as well as mutual clock imperfections between the network nodes. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes or base stations. The offered results highlight that only two carriers suffice to substantially facilitate resolving the integer ambiguity problem while also largely enhancing the robustness of positioning against imperfections imposed by the network-side clocks and multi-path propagation. In addition, we also propose a two-stage practical estimator framework that achieves the derived bounds under all realistic bandwidth and transmit power conditions. Furthermore, we show that with an additional search-based refinement step, the proposed estimator becomes particularly suitable for narrowband Internet of Things (IoT) applications operating efficiently even under narrow carrier bandwidths. Finally, both the derived bounds and the proposed estimators are extended to scenarios where the bands assigned to each base station are nonuniform or fully disjoint, enhancing the practical deployment flexibility.

Satellite networks have become central to advancing modern communication standards due to their potential for extensive global coverage. Despite this, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for reliable metrics that reflect ultra-reliable, low-latency communication. To address these challenges, data-oriented approaches, widely adopted in terrestrial networks, offer a fresh perspective by assessing transmission performance through delay outage rates. This work introduces the data-oriented approach to hybrid satellite-terrestrial networks (HSTNs), focusing on an uplink non-orthogonal multiple access (NOMA) scheme where grant-based and grant-free users coexist. The proposed analytical framework reveals the relationship between the amount of information data, power consumption, and the satellite distance, providing valuable pathway for optimizing the performance of HSTNs in the context of delay-sensitive applications.

High-Precision localization turns into a crucial added value and asset for next-generation wireless systems. Carrier phase positioning (CPP) enables sub-meter to centimeter-level accuracy and is gaining interest in 5G-Advanced standardization. While CPP typically complements time-of-arrival (ToA) measurements, recent literature has introduced a phase-only positioning approach in a distributed antenna/MIMO system context with minimal bandwidth requirements, using deep learning (DL) when operating under ideal hardware assumptions. In more practical scenarios, however, antenna failures can largely degrade the performance. In this paper, we address the challenging phase-only positioning task, and propose a new DL-based localization approach harnessing the so-called hyperbola intersection principle, clearly outperforming the previous methods. Additionally, we consider and propose a processing and learning mechanism that is robust to antenna element failures. Our results show that the proposed DL model achieves robust and accurate positioning despite antenna impairments, demonstrating the viability of data-driven, impairment-tolerant phase-only positioning mechanisms. Comprehensive set of numerical results demonstrates large improvements in localization accuracy against the prior art methods.

This paper investigates the uplink carrier phase positioning (CPP) in cell-free (CF) or distributed-antenna-system context, assuming a challenging case where only the phase measurements are utilized as observations. In general, CPP can achieve sub-meter to centimeter-level accuracy but it is challenged by the integer ambiguity problem. In this work, we propose two deep learning approaches for phase-only positioning, overcoming the integer ambiguity challenge. The first one directly uses the phase measurements, while the second one first estimates the integer ambiguities and then it integrates them with the phase measurements for improved accuracy. Our numerical results demonstrate that an inference complexity reduction of two to three orders of magnitude is achieved, compared to the maximum likelihood baseline solution, depending on the approach and on the parameter configuration. This emphasizes the potential of the developed deep learning solutions for efficient and precise positioning in future CF 6G systems.

As wireless systems move toward 6G, ensuring ultra-reliable low-latency communication (URLLC) securely is a key design challenge. Addressing the strict latency and reliability demands requires a shift in performance evaluation. The delay-outage rate (DOR) has recently emerged as a data-oriented metric that captures the probability of transmission time exceeding a threshold under ideal conditions. This work extends the data-oriented framework to include physical layer security, focusing on jamming and eavesdropping threats. We analyze how constant and random jamming affect DOR in the multi-antenna systems, highlighting the role of spatial and spectral resources in mitigation. Our analysis, supported by empirical simulations, offers new insights for designing secure, low-latency systems resilient to jamming—supporting robust ultra- or hyper-reliable low-latency communications in future 6G networks.

Ultra-reliable low-latency IoT communications (URLLC-IoT) has recently gained a growing interest. Here the challenge in reliable low-latency uplink transmission results from the transmission power limitations and lack of multiple antennas. However, in many IoT services the data volumes are small and sensor deployments may include massive number of devices. In this work we consider a coordinated uplink transmission of clustered IoT devices. The focus is on scenarios where location-based channel knowledge map (CKM) can be applied to enable cooperation. We model and analyse the impact of hardware impairments and erroneous CKM information. In the performance evaluation we focus on the recently introduced dataoriented approach that has gathered significant attention in the context of short-packet transmissions. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. Yet, the performance of the coordinated transmission system is heavily depending on the strength of the static channel component in the CKM based cooperation, the level of hardware impairments and the quality of CKM information. Analytic results are verified against simulations, showing only minor differences between analytical and experimental results.

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology for sixth-generation (6 G) communication networks, offering the ability to dynamically shape wireless propagation environments and thus efficiently enhance received signal quality. However, practical implementation of RIS faces challenges, including potential failures of individual elements (pixels), which can degrade the performance significantly. This paper leverages autoencoders and end-to-end (E2E) learning in RIS-aided systems to jointly optimize the RIS phase profiles and receiver angle-of-departure (AoD) estimation in the presence of pixel failures. The proposed E2E approach demonstrates resilience against practical pixel errors while is shown to achieve performance close to the fundamental bounds, thereby advancing the state-of-the-art in RIS-aided systems towards the 6 G era.

The data-oriented communications approach was initially introduced to develop novel transmission strategies for individual data sessions by accounting for instantaneous channel conditions under latency constraints. However, existing studies are limited to scenarios where channel state information (CSI) is either fully or partially available at the receiver, which may be impractical for short-packet transmissions. In this context, the present work focuses on analyzing the delay-outage rate (DOR), a data-oriented performance metric, under the noncoherent communications paradigm, where accurate CSI is not required. To this end, we first derive and calculate the DOR metric, which is then used to define transmission as well as receiver design requirements based on data and bandwidth parameters. Beyond emphasizing the significance of the new data-oriented DOR metric, the results provide valuable insights for designing and facilitating delay-sensitive and highly reliable noncoherent data transmission in future networks.

In this article, we study the timely notion of short-packet communications, with specific focus onuplink rate-splitting multiple access (RSMA) systems under the finite blocklength regime. Specifically, we consider rate-adaptive and power-adaptive uplink RSMA mechanisms, incorporating multiple user groups, and derive analytical expressions for the fundamentaldelay-outage rate (DOR)metric. The analytical derivations are validated through the corresponding Monte Carlo numerical simulations, reflecting high accuracy. Then, the derived DOR expressions are exploited for providing optimal DOR performance under diverse individual transmission parameters, such as different information delivery time thresholds, varying blocklengths and channel bandwidths, while also considering the non-orthogonal multiple access (NOMA) as a particular special case. Importantly, DOR optimization in terms of the message splitting ratio is also pursued, and a feasible optimization algorithm is proposed. A vast collection of numerical results is then provided, comparing between the rate-adaptive and the power-adaptive schemes, assessing the impact of message splitting ratio optimization while also comparing between RSMA and NOMA. Additionally, the overall power efficiency and impacts of imperfect channel state information (CSI) are assessed and shown. Overall, the offered analysis methods and numerical results provide valuable tools and insight for deploying, designing and optimizing data-oriented uplink RSMA mechanisms in future wireless systems such as the emerging 6G networks.

The so-called data-oriented approach has gathered significant attention in the context of short-packet communications in fifth-generation (5G) and beyond networks. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Such data-oriented approach has recently been introduced in downlink rate-splitting multiple access (RSMA) system context, which represents a flexible and promising non-orthogonal multiple access paradigm. Building upon such prior-art, this paper introduces the data-oriented approach to uplink RSMA systems under the finite blocklength regime. Specifically, we consider rate-adaptive and power-adaptive uplink RSMA mechanisms, under the data-oriented approach, and derive analytical expressions for the delay-outage ratio (DOR) metric. The analytical derivations are validated through corresponding Monte Carlo numerical simulations. The numerical results show that the DOR performance is directly linked with the transmit antenna diversity, while being also dependent on the accuracy of the channel state information as well as on the power budget of the uplink users. The obtained results highlight the importance of the new data-oriented DOR metric for efficient design of the uplink RSMA mechanism in future networks.

In this paper, we propose a novel three-dimensional (3D) near-field beamforming (BF) design for Large Intelligent Surface (LIS). We firstly investigate the definitions of near-field and far-field of LIS, and derive the Fresnel near-field region where amplitudes variations are negligible but only phase variations worsen the harvested array-gains. We show that the Fresnel region which covers the majority part of near-field, can be enlarged by a factor of four when considering possible imperfectness from a conventional two-dimensional (2D) far-field BF. Therefore, it is of interest to design an analog 3D-BF that can recover array-gain losses in this region. Secondly, with a decomposition theorem we show that the optimal 3D-BF can be decomposed into a 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations from mismatches in the azimuth and elevation angles, while the 1D near-field BF compensates remaining phases variations caused by distance differences from a user-equipment (UE) to different antenna-elements on LIS. Such a proposed “2D+1D” BF design reduces codebook-size significantly and is compatible with the existing far-field BF in the fifth-generation new-radio (5G-NR) system. Thirdly, we analyze an optimal codebook design for the 1D near-field BF, and show that with a small codebook it can perform close to optimal. Numerical results verify that the proposal is effective to recover array-gains in the near-field of LIS.

As wireless information transmission (WIT) progresses into its sixth generation (6G), a challenge arises in sustaining terminal operations with limited batteries for Internet-of-Things (IoT) platforms. To address this, wireless power transfer (WPT) emerges as a solution, empowering battery-less infrastructures and enabling nodes to harvest energy for sustainable operations. Thus, the eclectic integration of WPT with WIT mechanisms becomes crucial to mitigate the need for battery replacements while providing secure and reliable communication. A novel protocol that amalgamates WIT and WPT calledInformation Harvesting (IH)has recently been proposed to effectively handle challenges in wireless information and power transfer (WIPT) by employing index modulation (IM) techniques for data communication atop the existing far-field WPT mechanism. This paper presents a unified framework for IM-based IH mechanisms and evaluates their energy harvesting capability, bit error rate (BER), and ergodic secrecy rate (ESR) performance for diverse IM schemes. The findings indicate the significant potential of the IM-based IH mechanism in facilitating reliable data communication within existing far-field WPT systems while underscoring promising refinements in green and secure communication paradigms for next-generation IoT wireless networks.

This paper presents a promising design concept for reconfigurable intelligent surfaces (RISs), named plug-in RIS, wherein the RIS is plugged into an appropriate position in the environment, adjusted once according to the location of both base station and blocked region, and operates with fixed beams to enhance the system performance. The plug-in RIS is a novel system design, streamlining RIS-assisted millimeter-wave (mmWave) communication without requiring decoupling two parts of the end-to-end channel, traditional control signal transmission, and online RIS configuration. In plug-in RIS-aided transmission, the transmitter efficiently activates specific regions of the divided large RIS by employing hybrid beamforming techniques, each with predetermined phase adjustments tailored to reflect signals to desired user locations. This user-centric approach enhances connectivity and overall user experience by dynamically illuminating the targeted user based on location. By introducing plug-in RIS’s theoretical framework, design principles, and performance evaluation, we demonstrate its potential to revolutionize mmWave communications for the limited channel state information (CSI) scenarios. Simulation results illustrate that plug-in RIS provides power/cost-efficient solutions to overcome blockage in the mmWave communication system and a striking convergence in average bit error rate and achievable rate performance with traditional full CSI-enabled RIS solutions.

Considering the current capability in hardware design, the wireless power transmission (WPT) enables the next stage in the current consumer electronics revolution by prolonging the lifetime of devices powered by batteries. Recently, Information Harvesting (IH) introduced a novel mechanism for wireless power and information transfer which differs from existing far-field wireless power transfer and simultaneous transmission of power and data protocols. In this paper, a new IH mechanism which relies on quadrature spatial modulation (QSM) is proposed and the simulation results demonstrate that the proposed mechanism improves the secrecy at information receiver and provide more harvested energy to the harvester.

In this paper, we propose a novel near-field beamforming (BF) design with a Large Intelligent Surface (LIS) that is implemented as a discretized 2D-array. We first investigate the definitions of the near-field and far-field regions, and determine the Fraunhofer distance of the LIS, which scales up linearly in the surface-area of the LIS. Hence, a user-equipment (UE) can enter the near-field of a LIS in practice. In addition to Fraunhofer distance, we further derive the Fresnel near-field region where both amplitude and angle variations are negligible, as long as the distance from the UE to the LIS is larger than a threshold, which only scales up linearly in the diameter of LIS. Therefore, in the majority region of near-field, only phase variations worsen the the quality of received signal and result in significant array-gain losses. Motivated by this observation, we further propose a two-step near-field BF design that can effectively recover the array-gain losses in Fresnel near-field, and is fully compatible with a conventional far-field BF.

Vertical sectorization introduces considerable gain to particular scenarios where user equipment (UE) are distributed in 3D domain, i.e. high-rise buildings, in terms of coverage and network capacity thanks to availability of active antenna systems. However, due to the huge varieties in distribution of UEs and different physical environments, presenting a comprehensive analytical framework is quite challenging. From this aspect, most available studies on vertical sectorization are limited to present only empirical results. In this paper, we introduce a novel methodology to forecast the performance of NB-IoT systems over urban scenarios. In particular, a logistic distribution-based analytical framework is exploited in order to calculate the group probabilities for each available UE. Based on these probabilities, we propose a scheduling framework with beamforming which improves physical resource block (PRB) utilization by over 50% compared to the case with no scheduling.

Visible Light Communication (VLC) systems provide not only illumination and data communications, but also indoor monitoring services if the effect that different events create on the received optical signal is properly tracked. For this purpose, the Channel State Information (CSI) that a VLC receiver computes to equalize the OFDM subcarriers can be reused to train an unsupervised learning classifier. This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events, such as the position of an object in the sensing area. When compared to supervised learning algorithms, the proposed approach does not need tags in the training data, simplifying notably the implementation. The validation of this monitoring approach was done using a software-defined VLC transmission based on OFDM, in which a reflected copy of the intensity modulated signal coming from a white LED was captured by a pair of photodetectors. The experimental evaluation of the VLC-based monitoring demo achieved a positioning accuracy in the few-centimeter-range, without the necessity of deploying a large number of sensors and/or adding a sensor on the object to-be-tracked.

We propose the use of adaptive optimized irregular constellation to enhance the performance of full duplex (FD) wireless radio systems which suffer from residual self-interference (RSI). Specifically, an error performance expression which takes into account the RSI level and signal-to-noise ratio is first derived for a single FD relay link. During the search for optimal constellation for a given parameter set, there are no predefined assumptions on symbol point locations. The optimization framework yields adaptive irregular constellations depending on RSI levels and power budget values. It is shown that the proposed adaptive transmission along with optimized irregular constellations achieves better error performance than the conventional$M$-QAM constellations and other$M$-ary improper Gaussian signaling (IGS) constellations.

This paper studies a novel way to estimate the position of an object in an indoor environment, using the Channel State Information (CSI) that a Visible Light Communication (VLC) system collects to maintain the link-level connectivity. First, supervised learning is applied to characterize, the effect that an object in variable but known positions has on the received optical wireless signal. Second, the trained classifier is used to estimate the new unknown positions that the object may take, making use of the instantaneous CSI that is used to equalize the data-carrying signal samples in reception. The practical validation of the proposed positioning approach was done with the aid of a software-defined VLC link based on OFDM, in which a copy of the intensity modulated signal coming from a Phosphor-converted LED is captured by Photodetectors (PDs) in different room locations. Then, the CSI of the VLC receiver is used to train a Random Forest classifier, which will predict the position of the object during the assessment phase. The performance evaluation of our experimental setting shows that the proposed VLC-based positioning approach can reach a few centimeter accuracy, provided that a proper training is executed, without the necessity of deploying a large number of PDs in the room, or adding a VLC receiver on the object to be tracked.

Visible light communication (VLC) systems should be designed to provide illumination and wireless data services simultaneously. To achieve this goal at a reasonable cost, the use of Phosphor-Converted (PC) LEDs for indirect illumination should be favored to provide a homogeneous and reliable coverage in the whole service area. Unfortunately, PC-LEDs found in the market so far have not been designed for data transmission; moreover, the response of the other (electro-) optical components of the VLC link are far from ideal. In this paper, we estimate the data rate that is feasible with VLC when indirect illumination is used. For this purpose, the end-to-end response of the VLC link is first modeled using actual measurements of the spectral power distribution of a PC-LED lamp and the ceiling reflectance. Then, different power allocation schemes are studied assuming an optical OFDM waveform. As commercial LEDs have a relatively slow time response, the equivalent VLC channel that results has strong frequency selectivity; therefore, notable data rate gains are achievable when waterfilling power allocation is applied.

Visible Light Communication (VLC) systems are designed to provide illumination and data services simultaneously. To achieve this goal, LED lamps are usually deployed on the ceiling of the rooms, in order to maximize the chances of having Line-of-Sight (LoS) connectivity between the VLC transmitter and the random locations that the VLC receiver can take. In an early stage of adoption, where the cost of LED lighting fixtures incorporating VLC technology will be high to enable ultra-dense deployments, it is expected that only one VLC transmitter is placed per room. In this situation, the use of direct illumination may have serious problems to satisfy the illumination constraints and provide a homogeneous data rate coverage. Moreover, the use of a single powerful LED lamp per room may create discomfort glare effect to users and over-exposure problems in areas of the room at which the light beam is directed. In order to address these problems, this paper studies the data rate that VLC technology can achieve with indirect illumination. That is, when the LED lamp is pointing upwards, and the VLC user receives the optical signal that is reflected back from the ceiling. Obtained simulation results show that indirect illumination provides a more homogeneous data rate coverage when compared to the direct case, while simultaneously verifying the illumination constraints in most of the places that the VLC receiver may take in the room.

This paper demonstrates the feasibility of using supervised learning algorithms to identify the presence of different objects, taking advantage of the effect that they create on the VLC channel gains. For this purpose, a software-defined VLC link is implemented using a Phosphor-converted LED, whose light intensity is modulated by an Optical OFDM frame that includes synchronization words and pilot sequences for channel estimation. Actual estimated channel gains are collected in the receiver, which are used to train and assess the performance of the Random Forest classifier. The accuracy of the monitoring system is evaluated using three different objects, showing an accuracy in the order of 90% in detecting the objects, even when they take different positions when obstructing the VLC link.

There has been a rejuvenated interest in the use of non-equally spaced (irregular) constellations after promising performance results were demonstrated compared with the conventional lattice constellations. This paper investigates the use of the irregular constellations in convolutionally coded systems. To enable the use of such irregular constellations in the presence of convolutional encoders, we derive a bit-error-rate (BER) upper bound expression for the downlink of the coded transmit maximum ratio combining systems operating in Nakagami-m fading environments. In addition, the analysis is extended to turbo trellis-coded modulation scenarios in which the convolutional encoders are used as the constituent codes. We demonstrate, via simulation, that commonly used performance analysis techniques in the literature fail to provide a valid BER bound in coded cases where quasi-regularity is not satisfied. In contrast, the technique proposed herein does not require the chosen pair of constellation and encoder to be quasi-regular. Furthermore, the system model includes a provision for multiple orthogonal transmission stages with different numbers of transmit antennas. Antenna correlation as well as distributed transmission is also supported by the model. Simulation results demonstrate the accuracy of the derived analytical results for a wide range of system scenarios.

There has been a rejuvenated interest in the use of arbitrary constellations (non-equally spaced) after promising performance results were demonstrated compared to conventional grid and circular constellations. To enable the development of such constellations, in this contribution we derive an upper bound bit-error-rate (BER) expressions for coded transmit maximum ratio combining (TMRC) systems operating in Nakagami-\textit{m} fading environments with arbitrary constellations and encoder types. Unlike most existing works on error performance analysis for coded systems, a chosen pair of constellation and encoder need not be quasi-regular. Furthermore, the system model includes a provision for multiple orthogonal transmission phases with different number of transmit antennas. Simulation results demonstrate the accuracy of the derived analytical results for a range of system scenarios.

This paper considers a two-way relay system with time division broadcast (TDBC) protocol. In such protocol two end-sources exchange information with each other through help of a half-duplex decode- and-forward relay. To enhance the spectral efficiency in this system, we propose a novel scheme which incorporates signal space diversity with TDBC protocol. In the proposed scheme, original data symbols are rotated by a certain angle before being transmitted, and then the end- sources and the relay cooperate for transmitting in-phase and quadrature components of two consecutive rotated symbols. Thereby, the number of transmitted symbols are doubled over three time slots, which results in an increase in spectral efficiency. In particular, for this system we first derive a closed-form expression for the end- to-end (E2E) error probability with an arbitrary constellation to acquire all the resulting non- uniform constellation cases. Then, using the derived expression, we optimize the rotation angle at the different values of the signal-to-noise ratio to further improve the E2E error probability performance. Numerical results corroborate the theoretical analysis and show that the scheme proposed herein provides not only higher spectral efficiency, but also higher reliability transmission.

Constellation design is a well studied topic. For instance, it is known that, when 2-dimensional (2D) signalling schemes are used, the performance gains that the optimal constellations yield in comparison to the commonly employed square constellations are rather small. However, most of the earlier literature in this area considers rather simple communication protocols (for instance, without retransmissions), even in the absence of channel coding. With the advent of advanced communication protocols as well as signal processing techniques, there has been a rejuvenated interest in constellation design in recent years. In this paper, we consider a generic two-transmission scheme which may correspond to a relay, HARQ (hybrid automatic repeat request), or CoMP (coordinated multipoint) based transmission scenario, with a maximum likelihood receiver. The system has the flexibility of using a different 2D constellation in each transmission (however, the constellation size, i.e., the number of bits per symbol, remains the same). A generic channel coding scheme for which an encoder transfer function can be written (such as, convolutional and turbo codes) is considered for the versatile Nakagami-m fading channel. The main contribution of this paper is the derivation of an upper bound on the bit error rate (BER) which is expressed as a function of the distances between the constellation points. Using proper optimization techniques, this bound (tight for the high SNR values) which captures the impact of the distances between the constellation points can enable the design of good constellations for a given coding scheme in the above explained two-transmission setting.