Automatic modulation classification (AMC) is essential for spectrum awareness, cognitive radio, and electronic intelligence. While classical likelihood- and feature-based methods degrade under channel impairments, deep learning models, though powerful, often ignore domain knowledge and treat frames independently. We propose a Domain Aware Depthwise Separable Temporal Convolutional Network with Multi-Head Attention (DSTCN–MHA) that integrates statistical signal features with learned temporal embeddings and aggregates multiple frames to emphasize informative segments. Two variants are introduced: a light model optimized for efficiency and a heavy model targeting accuracy. Experiments on the RML22.01A dataset show that even at K=1 frames, both models outperform CNN and ResNet baselines, while at K=6 they achieve over 20% gains at low signal-to-noise ratio (SNR) and near-perfect accuracy above 10 dB, with significantly fewer parameters than prior models.

The Open Radio Access Network (O-RAN) architecture offers an innovative approach to wireless network design by enabling multi-vendor compatibility and dynamic resource allocation. However, extensive network configurations and high data traffic volumes present significant challenges to its sustainability in terms of energy consumption. Therefore, in this study, we develop a 5G O-RAN traffic steering and sleep mode control system based on Deep Q-Learning (DQN), targeting low-traffic scenarios where significant energy savings can be achieved through selective activation and deactivation of devices. The proposed scheme leverages a deep reinforcement learning algorithm to optimize the mappings from User Equipments (UEs) to Radio Units (RUs), from RUs to Distributed Units (DUs), and from DUs to Centralized Units (CUs), in order to maximize energy savings in O-RAN. Using UE Reference Signal Received Power (RSRP) and RU load levels as input states, the system generates energy-efficient mapping actions for dynamic traffic steering and sleep mode control. The simulation results demonstrate that the proposed mapping methods achieve 5∼12% energy savings compared to the benchmark scenario.

Blockchain is an eminent technique to enhance the safety and robustness of the Internet of Things (IoT) network, due to its traits of decentralisation and transparency. Practical Byzantine Fault Tolerance (PBFT) blockchain consensus mechanism is well suited for wireless networks because of its low-computing requirement, low latency and high throughput. In this paper, we investigate the implementation of the base station (BS)-enabled wireless PBFT network, where the inter-node communications go through the BS in the normal case operation. The performance under such a scheme is analysed and evaluated through three metrics: consensus success probability, communication complexity, and average node transmit power. Results show that the proposed framework achieves higher scalability, lower communication complexity, and lower average node transmit power.

Due to the explosion of mobile users and the ever-increasing heterogeneity and scale of wireless networks, traditional communication protocols and optimizing methods can not satisfy future wireless network (FWN) requirements. As promising technologies, artificial intelligence (AI) and blockchain are deemed as the solution for the FWN. AI, famous for its big data processing ability, will enable the FWN to self-update itself to better adapt to the dynamic network condition. Blockchain, as a distributed ledger, can guarantee data integrity, security, and privacy. In this survey, we overview the concept of AI and blockchain and present their state-of-the-art applications in wireless networks. The potential of AI and blockchain is still huge and waiting to be fully explored in wireless networks. Therefore, we introduce how AI and blockchain can assist each other in FWNs. Furthermore, we explore the current constraints of applying both technologies in the FWNs. In the final part, we discuss the future direction of the deployment of AI and blockchain in FWNs.

Most existing studies consider the deep reinforcement learning (DRL) based Q-learning approach due to its ability to quickly converge to a near-optimal solution, resulting in effective allocation of resources and power. DRL-based Q-network discretizes the continuous power values which results in poor performance. It is challenging to allocate resources effectively in fast varying channel conditions in dynamic vehicular environments. In this work, we propose two approaches to overcome these challenges. First, we present a DRL-based energy-efficient resource allocation approach where we use a twin delayed deep deterministic policy gradient (TD3) scheme based on Thompson sampling to solve the power and resource allocation problem. Second, we present a dynamic meta-transfer learning framework to enhance the policy’s ability to adjust to new channel conditions. Simulation results shows that the proposed TD3 approach based on Thompson sampling enhances the system performance. Moreover, the proposed DRL-based dynamic meta-transfer learning framework takes 80% less samples to adapt to a new environment.

The Open Radio Access Network (O-RAN) architecture has been identified as a promising technology for enhanced network deployment, innovation, improved competition, and reduction of capital and operating expenses (CAPEX/OPEX) of 5G and beyond networks because of its open interfaces, disaggregated network entities and functions, virtualization of network hardware and software, and intelligent control. However, the effect of this improved technology on the energy consumption of the RAN needs to be carefully investigated, so that the many advantages that can be obtained from the O-RAN are not overwhelmed by increased energy consumption. Hence, in this paper, we investigate the O-RAN from an Energy efficiency (EE) perspective by reviewing the state-of-the-art power consumption models, and EE techniques that have been proposed to minimize the energy consumption of O-RAN. In addition, the challenges associated with the optimization of the EE of O-RAN and opportunities for further research are highlighted.

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

Heterogeneity is a key feature of 5G and beyond networks for Internet of things applications in various fields. Regarded as the leading low power wide area network, long range wide area network (LoRaWAN) is expected to accomplish 5G's massive machine-type communications target by integrating it into 5G network. In this paper, we design and implement a LoRaWAN-5G integrated network with a collaborative Radio Access Network and a converged core network. We built a 5G-based LoRaWAN gateway that communicates with 5G new radio. To the best of our knowledge, this is the first LoRaWAN gateway that uses 5G network as its backhaul. Moreover, the LoRaWAN servers are deployed within the core network of the 5G testbed, enhancing the security and privacy of LoRaWAN data. This hybrid network has been deployed to monitor the heating system of rooms in James Watt South Building at the University of Glasgow, demonstrating the stability, high flexibility and low deployment cost of the network.

Internet of vehicles (IoV) has been developed as a promising technology to improve road safety. However, resource management can be challenging in a congested traffic environment, which can affect the energy efficiency (EE) and spectrum efficiency (SE) in IoV networks. In this paper, we present a novel intelligent resource allocation approach based on deep reinforcement learning to maximize the weighted composite efficiency that incorporates the EE and SE metric subject to latency and reliability constraints of vehicle-to-vehicle (V2V) users. We employ Thompson sampling with double deep Q network to transform the objective function. Moreover, we present a probability-based learning approach to meet the quality of service requirements and to increase the learning ability of the proposed model. The simulation results indicate that the proposed approach maximizes the composite efficiency while satisfying the latency and reliability constraints of V2V users.

Mobile-edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinder IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this article investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its nonconvexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), the bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic has exposed an urgent need for effective contact tracing solutions through mobile phone applications to prevent the infection from spreading further. However, due to the nature of contact tracing, public concern on privacy issues has been a bottleneck to the existing solutions, which is significantly affecting the uptake of contact tracing applications across the globe. In this article, we present a blockchain-enabled privacy-preserving contact tracing scheme: BeepTrace, where we propose to adopt blockchain bridging the user/patient and the authorized solvers to desensitize the user ID and location information. Compared with recently proposed contact tracing solutions, our approach shows higher security and privacy with the additional advantages of being battery friendly and globally accessible. Results show viability in terms of the required resource at both server and mobile phone perspectives. Through breaking the privacy concerns of the public, the proposed BeepTrace solution can provide a timely framework for authorities, companies, software developers, and researchers to fast develop and deploy effective digital contact tracing applications, to conquer the COVID-19 pandemic soon. Meanwhile, the open initiative of BeepTrace allows worldwide collaborations, integrate existing tracing and positioning solutions with the help of blockchain technology.

A clear understanding of mixed-numerology signals multiplexing and isolation in the physical layer is of importance to enable spectrum efficient radio access network (RAN) slicing, where the available access resource is divided into slices to cater to services/users with optimal individual design. In this paper, a RAN slicing framework is proposed and systematically analyzed from the physical layer perspective. According to the baseband and radio frequency (RF) configurations imparities among slices, we categorize four scenarios and elaborate on the numerology relationships of slices configurations. By considering the most generic scenario, system models are established for both uplink and downlink transmissions. Besides, a low out of band emission (OoBE) waveform is implemented in the system for the sake of signal isolation and inter-service/slice-band-interference (ISBI) mitigation. We propose two theorems as the basis of algorithms design in the established system, which generalize the original circular convolution property of discrete Fourier transform (DFT). Moreover, ISBI cancellation algorithms are proposed based on a collaboration detection scheme, where joint slices signal models are implemented. The framework proposed in the paper establishes a foundation to underpin extremely diverse use cases in 5G that implement on a common infrastructure.

Distributed systems are crucial to the full realization of the Internet of Thing (IoT) ecosystem as it mitigates the challenges of trust, security, and scalability associated with the traditional centralized approach. In this paper, we present an analytical modeling framework for Practical Byzantine Fault Tolerance (PBFT)-a consensus method for blockchain in IoT networks. We define the viable area for the wireless PBFT networks which guarantees the minimum number of replica nodes required for achieving the protocol's safety and liveliness. We also present an analytical framework for obtaining the viable area which we later utilize for power optimization. Results show that significant energy saving can be achieved with the utilization of the viable area concept in wireless PBFT networks. The proposed framework can serve as a theoretical guidance for practical PBFT based wireless blockchain network deployment.

The cell range extension (CRE) has been successfully implemented to bias the user to base station (BS) association policy in a way that achieves load balancing and increases the capacity of heterogeneous networks. The user-centric backhaul (UCB) scheme is a CRE evolution that is both backhaul-aware and user-context-aware -two constraints that are shaping the 5G network development. In this work, we formulate and solve the multi-objective optimisation problem of the UCB user-BS association. We derive analytical expressions of the ergodic throughput resulting from the UCB and, accordingly, identify the optimum association policy. The study demonstrates the gain margins that can be realised with pertinent user-cell association which is aware of the end-to-end network limitations and users requirements.

In this paper, we quantify the effect of residual hardware impairments (RHI) on the error rate performance of a relay-based non-orthogonal multiple access (NOMA) system, where the communication between a source node and multiple users is completed via an amplify-and-forward (AF) relay node. In particular, we focus on the pairwise error probability (PEP) analysis and derive an accurate PEP approximation to characterize the performance of NOMA users under Rayleigh fading channels. The derived PEP expression is then exploited to investigate the diversity gain and the union bound on the bit error rate (BER) of the underlying system. Our results demonstrate that the presence of RHI causes an error floor at high signal-to-noise ratio (SNR) values. This error floor yields a detrimental effect on the achievable diversity order of NOMA users, where it is shown that the diversity order of all users converges to zero.

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical model is proposed for holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/Data Separation Architecture (CDSA). Second, data-driven HO prediction is proposed and evaluated as part of the holistic cost, for the first time, through novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Finally, simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be effectively utilized, based on HO management requirements. Both analytical and machine learning models are evaluated with a benchmark, real-world dataset measuring human behaviors and interactions. Numerical and comparative simulation results demonstrate the potential of our proposed deep learning-driven HO management framework, as a future benchmark for the mobile networking and machine learning communities.

We consider an idealistic scenario where the vacation (no-load) period of a typical base station (BS) is known in advance such that its vacation time can be matched with a sleep depth. The latter is the sum of the deactivation latency, actual sleep period and reactivation latency. Noting that the power consumed during the actual sleep period is a function of the deactivation latency, we derive an accurate closed-form expression for the optimal deactivation latency for deterministic BS vacation time. Further, using this expression, we derive the optimal average power consumption for the case where the vacation time follows a known distribution. Numerical results show that significant power consumption savings can be achieved in the sleep mode by selecting the optimal deactivation latency for each vacation period. Furthermore, our results also show that deactivating the BS hardware is sub-optimal for BS vacation less than a particular threshold value.

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.

In this paper, we present an analytical framework to model the sleep mode power consumption of a base station (BS) as a function of its sleep depth. The sleep depth is made up of the BS deactivation latency, actual sleep period and activation latency. Numerical results demonstrate a close match between our proposed approach and the actual sleep mode power consumption for selected BS types. As an application of our proposed approach, we analyze the optimal sleep depth of a BS, taking into consideration the increased power consumption during BS activation, which exceeds its no-load power consumption. We also consider the power consumed during BS deactivation, which also exceeds the power consumed when the actual sleep level is attained. From the results, we can observe that the average total power consumption of a BS monotonically decreases with the sleep depth as long as the ratio between the actual sleep period and the transition latency (deactivation plus reactivation latency) exceeds a certain threshold.

Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic increase in signaling overhead. This suggests a paradigm shift toward a signaling conscious cellular architecture with intelligent mobility management. In this direction, a futuristic radio access network with a logical separation between control and data planes has been proposed in research community. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This approach enables signaling efficient HO procedures since the control plane remains unchanged when the users move within the footprint of the same umbrella. Considering this configuration, we propose a core-network efficient radio resource control signaling scheme for active state HO and develop an analytical framework to evaluate its signaling load as a function of network density, user mobility, and session characteristics. In addition, we propose an intelligent HO prediction scheme with advance resource preparation in order to minimize the HO signaling latency. Numerical and simulation results show promising gains in terms of reduction in HO latency and signaling load as compared with conventional approaches.

Database-aided user association, where users are associated with data base stations (BSs) based on a database which stores their geographical location with signal-to-noise-ratio tagging, will play a vital role in the futuristic cellular architecture with separated control and data planes. However, such approach can lead to inaccurate user-data BS association, as a result of the inaccuracies in the positioning technique, thus leading to sub-optimal performance. In this paper, we investigate the impact of database-aided user association approach on the average spectral efficiency (ASE). We model the data plane base stations using its fluid model equivalent and derive the ASE for the channel model with pathloss only and when shadowing is incorporated. Our results show that the ASE in database-aided networks degrades as the accuracy of the user positioning technique decreases. Hence, system specifications for database-aided networks must take account of inaccuracies in positioning techniques.

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.

Most of the wireless systems such as the long term evolution (LTE) adopt a pilot symbol-aided channel estimation approach for data detection purposes. In this technique, some of the transmission resources are allocated to common pilot signals which constitute a significant overhead in current standards. This can be traced to the worst-case design approach adopted in these systems where the pilot spacing is chosen based on extreme condition assumptions. This suggests extending the set of the parameters that can be adaptively adjusted to include the pilot density. In this paper, we propose an adaptive pilot pattern scheme that depends on estimating the channel correlation. A new system architecture with a logical separation between control and data planes is considered and orthogonal frequency division multiplexing (OFDM) is chosen as the access technique. Simulation results show that the proposed scheme can provide a significant saving of the LTE pilot overhead with a marginal performance penalty.

In this paper, we investigate the optimal inter-frequency small cell discovery (ISCD) periodicity for small cells deployed on carrier frequency other than that of the serving macro cell. We consider that the small cells and user terminals (UTs) positions are modelled according to a homogeneous Poisson Point Process (PPP). We utilize polynomial curve fitting to approximate the percentage of time the typical UT missed small cell offloading opportunity, for a fixed small cell density and fixed UT speed. We then derive analytically, the optimal ISCD periodicity that minimizes the average UT energy consumption (EC). Furthermore, we also derive the optimal ISCD periodicity that maximizes the average energy efficiency (EE), i.e. bit-per-joule capacity. Results show that the EC optimal ISCD periodicity always exceeds the EE optimal ISCD periodicity, with the exception of when the average ergodic rates in both tiers are equal, in which the optimal ISCD periodicity in both cases also becomes equal.

The Global energy consumption and carbon footprint related to operating mobile phones in wireless networks is increasing significantly. In order to determine the overall energy consumption of a wireless network, both the operation of the network infrastructure and the devices connected to that network must be considered. Although the larger part of the global energy consumption of wireless networks is consumed at base station sites and access points, a significant part is consumed from the operation of mobile phones. In this paper, system level power consumption models for mobile phones in terms of 2G, 3G and Wi-Fi are presented. The developed model increases the accuracy of the current power profiles by considering different stages of a single transmission, including the variables affecting each stage. The power states of wireless interfaces, maintenance, network attachment/detachment and network resource allocation policies of different network operators are also considered to obtain a complete model that can accurately predict that power consumption at every stage of connectivity. A comparison between power consumption of different radio access technologies is also included to promote energy efficient use of spectrum. Using the system level power models presented in this paper, the contribution of the operation of mobile phones to overall energy consumption of wireless communication networks can be determined.

The next generation networks seem to be too dense compared to the existing one, so a self-control mechanism, which determines the optimal cell size will be essential. In this paper, we present self organized cell size control algorithms, which maintain optimum system throughput and power consumption. Particularly, we investigate three different algorithms that control the cells size, while maintaining the optimum power consumption and block allocation in the network. These algorithms differ in terms of their decision area. The first one is based on a centralized control; the second one is a distributed approach; and the final one is based on a group distributed control. In order to evaluate their performance, these algorithms are tested upon two different simulation environment, which approach real scenarios. Our results indicate that the group distributed algorithm is the best approach for future network, since it has a good performance and about 10 times lower computational complexity when compared with the centralized approach.

In this paper, the trade-off between energy efficiency (EE) and spectral efficiency (SE) is analyzed for both the uplink and downlink of the distributed multiple-input multiple-output (DMIMO) system over the Rayleigh fading channel while considering different types of power consumption models (PCMs). A novel tight closed-form approximation of the DMIMO EE-SE trade-off is presented and a detailed analysis is provided for the scenario with practical antenna configurations. Furthermore, generic and accurate low and high-SE approximations of this trade-off are derived for any number of radio access units (RAUs) in both the uplink and downlink channels. Our expressions have been utilized for assessing both the EE gain of DMIMO over co-located MIMO (CMIMO) and the incremental EE gain of DMIMO in the downlink channel. Our results reveal that DMIMO is more energy efficient than CMIMO for cell edge users in both the idealistic and realistic PCMs; whereas in terms of the incremental EE gain, connecting the user terminal to only one RAU is the most energy efficient approach when a realistic PCM is considered.

In this paper, we propose a novel closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink/downlink of distributed multipleinput multiple-output (DMIMO) system with two cooperating base stations. Our closed-form expression can be utilized for evaluating the idealistic and realistic EE-SE performances of various antenna configurations as well as assessing how DMIMO compares against MIMO system in terms of EE. Results show a tight match between our closed-form approximation and the Monte-Carlo simulation for both idealistic and realistic EESE trade-off. Our results also show that given a target SE requirement, there exists an optimal antenna setting that maximizes the EE. In addition, DMIMO scheme can offer significant improvement in terms of EE over the MIMO scheme.

In this paper, we derive a generic closed-form approximation (CFA) of the energy efficiency-spectral efficiency (EE-SE) trade-off for the uplink of coordinated multi-point (CoMP) system and demonstrate its accuracy for both idealistic and realistic power consumption models (PCMs). We utilize our CFA to compare CoMP against conventional non-cooperative system with orthogonal multiple access. In the idealistic PCM, CoMP is more energy efficient than non-cooperative system due to a reduction in power consumption; whereas in the realistic PCM, CoMP can also be more energy efficient but due to an improvement in SE and mainly for cell-edge communication and small cell deployment.

In this paper, we propose a tight closed-form approximation of the Energy Efficiency vs. Spectral Efficiency (EE-SE) trade-off for the uplink of a linear cellular communication system with base station cooperation and uniformly distributed user terminals. We utilize the doubly-regular property of the channel to obtain a closed form approximation using the Marčenko Pasture law. We demonstrate the accuracy of our expression by comparing it with Monte-Carlo simulation and the EE-SE trade-off expression based on low-power approximation. Results show the great tightness of our expression with Monte-Carlo simulation.We utilize our closed form expression for assessing the EE performance of cooperation for both theoretical and realistic power models. The theoretical power model includes only the transmit power, whereas the realistic power model incorporates the backhaul and signal processing power in addition to the transmit power. Results indicate that for both power models, increasing the number of antennas leads to an improvement in EE performance, whereas, increasing the number of cooperating BSs results in a loss in EE when considering the realistic power model.

Coordination between two or more multiple access channel (MAC) receivers can enlarge the achievable rate region of the whole system. This paper focuses on coordination by sharing the codebooks of the users between the receivers of MACs. We first define the achievable rate region of the time invariant multiple coordinated MAC (MCMAC) and subsequently derive its achievable rate region. We later express the achievable rate region in terms of the dominating points. We base our numerical analysis on the two-user two-receiver Gaussian coordinated MAC and make comparison with the interference channel, full cooperation and the individual MAC performance analysis. It is observed that this approach though suboptimal is less complex in comparison with full cooperation and that the MCMAC rate region is at least equal to the rate region of the uncoordinated approach. Over several channel states, the rate region of MCMAC exceeds that of the uncoordinated approach.