Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise channel models that may not always be practical. This paper proposes a model-free approach combining deep autoencoders and LSTM networks. The method first learns low-dimensional channel representations preserving temporal correlation through augmenting a channel charting-inspired loss function, then tracks these features to recover full channel information from limited pilots. Simulation results using ray-tracing datasets show that the proposed approach achieves up to 9 dB improvement in normalized mean square error compared to the least-squares methods under ill-conditioned scenarios, while maintaining scalability across MIMO configurations.

We propose multi-mode spectrally efficient frequency division multiplexing with index modulation (MM-SEFDM), an enhanced non-orthogonal waveform that embeds distinct constellations on every subcarrier and conveys additional bits through subcarrier-index patterns. By compressing the subcarrier spacing of orthogonal frequency division multiplexing (OFDM), MM-SEFDM significantly improves spectral efficiency at the expense of controlled inter-carrier and inter-symbol interference. This joint non-orthogonal and multi-mode index modulation structure introduces significant detection complexity, necessitating a low-complexity, yet effective receiver capable of handling interference and permutation-based index-symbol decoding. To this end, we propose a two-phase Transformer-based decoder: in Phase 1, the network learns joint index–symbol constellations over AWGN channels, while in Phase 2, it continues training over Rayleigh fading channels to enhance robustness under realistic propagation conditions. Furthermore, to enhance model generalization, the detector is trained under domain randomization across different non-orthogonality levels and channel estimation errors (CSE), ensuring robust and SNR-agnostic performance under diverse interference and operating conditions. Compared to brute-force maximum-likelihood detection, the proposed learning-based decoder reduces inference complexity from exponential to polynomial time, while achieving near-optimal bit-error rate performance.

The integration of pattern-reconfigurable antennas into hybrid multiple-input multiple-output (MIMO) architectures presents a promising path toward high-efficiency and low-cost transceiver solutions. Pattern-reconfigurable antennas can dynamically steer per-antenna radiation patterns, enabling more efficient power utilization and interference suppression. In this work, we study a tri-hybrid MIMO architecture for multi-user communications that integrates digital, analog, and antenna-domain precoding using pattern-reconfigurable antennas. For characterizing the reconfigurability of antenna radiation patterns, we develop two models---Model~I and Model~II. Model~I captures realistic hardware constraints through limited pattern selection, while Model~II explores the performance upper bound by assuming arbitrary pattern generation. Based on these models, we develop two corresponding tri-hybrid precoding algorithms grounded in the weighted minimum mean square error (WMMSE) framework, which alternately optimize the digital, analog, and antenna precoders under practical per-antenna power constraints. Realistic simulations conducted in ray-tracing generated environments are utilized to evaluate the proposed system and algorithms. The results demonstrate the significant potential of the considered tri-hybrid architecture in enhancing communication performance and hardware efficiency. However, they also reveal that the existing hardware is not yet capable of fully realizing these performance gains, underscoring the need for joint progress in antenna design and communication theory development.

Ultraviolet (UV) localization has attracted significant attention for its ability to operate without line-of-sight. Existing UV localization techniques usually require anchor nodes to be placed in specific geometric configurations, including linear alignment or co-location. However, this requirement is often impractical in real-world scenarios, where obstacles such as walls and equipment prevent anchor nodes from being placed in ideal positions. In this paper, we consider general scenarios without constraints on anchor deployment and propose a UV localization technique, referred to as EIUL (Evolutionary and Invex approximation-based Ultraviolet Localization), to address non-line-of-sight scenarios. We adopt the single-scattering model and the geometric relationship between the transmitting beam and the receiver’s field of view to develop a least-squares-based estimator for jointly estimating the target node’s location, along with the transmitting beam’s azimuth angle and elevation angle. We first apply differential evolution to obtain an initial estimate, which is then used to facilitate invex approximation, thereby formulating an invexified objective function to further refine the location estimate. We derive the Lipschitz constant for the gradient of the invexified objective function and employ the Nesterov’s accelerated gradient descent to achieve the global minimum. Additionally, we present a concise form of the Cramer-Rao lower bound for UV localization based on the single-scattering model, which is straightforward to compute numerically. Extensive simulations demonstrate that EIUL provides more accurate parameter estimation with lower computational complexity compared to existing techniques.

We propose a structured opportunistic spectrum access scheme, named ON-OFDM-IM, which employs non-orthogonal multiple access with orthogonal frequency division multiplexing and index modulation (OFDM-IM) to enable efficient spectrum reuse. The framework operates in two phases: an anchor transmission (AT) phase and a subsequent opportunistic access phase. During the first phase, anchor transmitters activate only a subset of subcarriers (SCs) and embed information through both the activation pattern and conventional constellation symbols to enhance energy efficiency. In the second phase, unlicensed devices opportunistically utilize the idle SCs left unused in the AT to support device-to-device communication. This article derives mathematical expressions for the spectral efficiency (SE) and bit error rate (BER) of ON-OFDM-IM. Analytical and simulation results confirm that the proposed scheme achieves a favorable trade-off between SE and BER, outperforming conventional benchmark systems.

In traditional fog-radio access networks (F-RANs), deploying many enhanced remote radio heads (eRRHs) faces challenges due to site limitations and fronthaul link costs. To tackle this issue, we introduced smart helpers (SHs) in our earlier study. SHs mainly listen to communications between eRRHs and users, smartly cache popular content, and serve users without having a fronthaul link to the macro base station (MBS). In this paper, we explore using an unmanned aerial vehicle (UAV) as the SH to assess the potential benefits of integrating UAVSHs into F-RANs. Because UAVs typically have limited battery life and cover smaller areas, we leverage a reconfigurable intelligent surface (RIS) to extend the service area of the UAVSH. To assess the performance of the considered UAVSH-aided F-RAN, we formulate the problem of average delivery delay minimization. To tackle the problem, we develop an algorithm to optimize user and RIS scheduling and caching decisions using multiagent reinforcement learning (MARL). The simulation results numerically prove that integrating a UAVSH into a RIS-assisted F-RAN leads to significant improvements in delivery delay, fronthaul load, and cache hit rate metrics.

In traditional fog-radio access networks (F-RANs), enhanced remote radio heads (eRRHs) are connected to a macro base station (MBS) through fronthaul links. Deploying a massive number of eRRHs is not always feasible due to site constraints and the cost of fronthaul links. This paper introduces an innovative concept of using smart helpers (SHs) in F-RANs. These SHs do not require fronthaul links and listen to the within-coverage eRRHs’ communications. Then, they smartly select and cache popular content. This capability enables SHs to serve users with frequent on-demand service requests potentially. As such, network operators have the flexibility to easily deploy SHs in various scenarios, such as dense urban areas and temporary public events, to expand their F-RANs and improve the quality of service (QoS). To study the performance of the proposed SH-aided F-RAN, we formulate an optimization problem of minimizing the average transmission delay that jointly optimizes cache resources and user scheduling. To tackle the formulated problem, we develop an innovative multi-stage algorithm that uses a reinforcement learning (RL) framework. Various performance measures, e.g., the average transmission delay, fronthaul load, and cache hit rate of the proposed SH-aided F-RAN are evaluated numerically and compared with those of traditional F-RANs.

Holographic multiple-input multiple-output (HMIMO) communication systems utilize spatially-constrained arrays equipped with large number of antennas (NoA) to benefit from increased spatial multiplexing and spatial resolution gains. We consider a multi-user HMIMO system under an electromagnetic wave compliant channel model, and study its energy-efficiency (EE). We first derive closed-form expressions of the ergodic achievable rates under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding in the downlink, and maximum ratio combining (MRC) and ZF combining in the uplink, implemented at the base station (BS) with reduced complexity dictated by the number of degrees-of-freedom (DoF) offered by the channel. Using these expressions, we formulate an EE maximization problem with respect to the power allocation (PA) and the NoA arranged within spatially-constrained HMIMO surfaces at the BS and users, and solve it using an alternating optimization algorithm. For fixed PA, the optimal NoA is derived as the solution of two analytical equations, while for fixed NoA we use sequential fractional programming to obtain the optimal PA. Numerical results yield useful insights into the EE performance in different operating regimes and under different side-lengths of HMIMO surfaces. The presented results show that under MRT and MRC in the downlink and uplink respectively, deploying more antennas in excess of the number of DoF increases the EE in low power budget (noise-limited) regime, whereas fixing the NoA to the number of DoF maximizes the EE in higher power budget (interference-limited) regime. On the other hand, under ZF precoders and combiners, the NoA that achieve the optimal EE is larger than the DoF under all power budgets, with the number of additional antennas decreasing with increasing power budget.

Cell free (CF) massive multiple-input multiple-output (mMIMO) is a promising technology in realizing beyond fifth generation (B5G) networks. Massive deployment of access points (APs) in a CF-mMIMO system increases the spectral efficiency, however it also increases the energy consumption and fronthaul requirements. Since recently reconfigurable intelligent surface (RIS) is shown to be a cost-effective solution to improve performance of wireless networks, RIS can be a promising technology to enhance the performance of CF-mMIMO systems. In this work, we study the downlink (DL) performance of CF-mMIMO system aided by multiple RISs, while considering correlated Rician fading channels, discrete RIS phase-shifts and low-complexity channel estimation (CE) protocol. Given the obtained imperfect channel state information (CSI), we derive lower bounds on the rates achieved using conjugate beamforming (CB) and zero-forcing (ZF) precoders. The obtained bounds depend on channel statistics, RIS phase-shifts and number of RIS elements. To optimize the performance with respect to RIS phase-shifts, we formulate a maximization problem and propose a sub-optimal genetic algorithm (GA)-based solution. Through simulations, we demonstrate that distributed RIS deployment outperforms centralized RIS deployment in terms of DL throughput. Interestingly, we demonstrate that the DL throughput improves as number of RISs increases until an optimal number of distributed RISs over which the DL performance of the system starts to drop. We discuss this effect under varying the number of APs and RISs. We extend the analysis by considering different precoders, CE and RIS optimization schemes, and verify the accuracy of our derived analytical results by Monte-Carlo simulations.

Some emerging 5G and beyond use-cases impose stringent latency constraints, which necessitates a paradigm shift towards finite blocklength performance analysis. In contrast to Shannon capacity-achieving codes, the codeword length in the finite blocklength regime (FBR) is a critical design parameter that imposes an intricate tradeoff between delay, reliability, and information coding rate. In this context, this paper presents a novel mathematical analysis to characterize the performance of large-scale downlink networks using short codewords. Theoretical achievable rates, outage probability, and reliability expressions are derived using the finite blocklength coding theory in conjunction with stochastic geometry, and compared to the performance in the asymptotic regime (AR). Achievable rates under practical modulation schemes as well as multilevel polar coded modulation (MLPCM) are investigated. Numerical results provide theoretical performance benchmarks, highlight the potential of MLPCM in achieving close to optimal performance with short codewords, and confirm the discrepancy between the performance in the FBR and that predicted by analysis in the AR. Finally, the meta distribution of the coding rate is derived, providing the percentiles of users that achieve a predefined target rate in a network.

Federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. In this paper, we consider minimizing the energy consumption of a device-to-device (D2D) network while maintaining the convergence rate of FL subject to its time constraint. In the considered D2D network, each device has limited transmission range and is connected partially to other devices in the network. A group of devices can form a cluster and one of these devices is judiciously selected as a local aggregator (LA) to aggregate the local models of other devices in the cluster. Leveraging the nature of D2D communications, we exploit the devices that are located at the conflict zones of LAs. As such, the LAs can disseminate their local aggregated models among them. Towards this goal, a joint optimization problem, considering scheduling the devices to the LAs and computation frequency allocation of the devices, is presented. In order to solve this NP-hard problem, an iterative solution is devised. Particularly, we decompose it into two sub-problems, namely, LAs selection and device scheduling sub-problem and computation frequency allocation sub-problem. By solving theses sub-problems iteratively, a FedD2D (federated learning with D2D communications) scheme is proposed. MATLAB simulations are conducted to verify the effectiveness of the proposed FedD2D scheme over FL conventional schemes.

The development of delay-constrained applications which require high data rates, ultra-low latency, and high reliability pushed future communication technologies towards using short codes. This necessitates studying the performance of large-scale networks under short codes. Works in the literature that study large-scale uplink (UL) networks rely on the classical Shannon coding theory which assumes long codes and vanishing frame error probability, which is imprecise for short codes. Thus, this paper studies the performance of a large-scale UL network in the finite blocklength regime (FBR), under two power control schemes to mitigate the effect of interference: truncated channel inversion power control and channel inversion power control with maximum power transmission. The average coding rate, outage probability, and reliability of this network are derived in the FBR. Numerical results study the effect of network parameters and also show that the classical coding theory overestimates the average coding rate and imprecisely characterizes the outage probability and the reliability in the FBR.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users’ private data. In this paper, we introduce a decentralized FL scheme that is called federated learning empowered overlapped clustering for decentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.

Resource allocation is investigated for offloading computational-intensive tasks in dual-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (APs) with the computing capability and the MEC servers. A user-device (UD), therefore, first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. To utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access (NOMA) for offloading the tasks from the UDs to the AP(s). In order to investigate the trade-off between latency and energy consumption, this work considers minimizing a weighted-sum that consists of latency and energy consumption, subject to UDs’ rate threshold, tasks’ time-delay, computational frequency scaling, and transmit power allocation constraints. With a joint consideration of all such factors, the problem is NP-hard and its global optimal solution is computationally intractable. A graph-theoretical approach is employed to solve the problem efficiently. Specifically, a novel joint MEC graph-based approach is devised, which solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the local computational frequency scaling problem(s) jointly. The joint MEC approach achieves near-optimal performance with high computational complexity. To strike a suitable balance between the performance and computational complexity of the resource allocation, a low complexity, yet efficient, pruning graph approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations.

We consider resource allocation for offloading computational-intensive tasks in a mobile-edge computing (MEC) system, where each loT device's task can be processed at the MEC server, access points (APs), or locally at loT device itself. The envisioned system has both cooperative APs with a computing capability and multiple radio resource blocks (RRBs) and a MEC server. We aim to study the trade-off between minimizing the energy consumption and maximizing the effective system capacity, which is the number of loT devices with a successful task processing. For this objective, we exploit non-orthogonal multiple access (NOMA) to schedule a set of loT devices to the set of MEC's subcarriers and RRBs of the APs. Due to the intractability of the energy consumption minimization problem, we split it into two sub-problems. The first sub-problem considers the offloading decision and local computation allocation, while the second sub-problem considers the loT device scheduling and power allocation. Leveraging graph-theory, we propose an approach for solving the two sub-problems. Numerical results are presented to depict the trade-off between minimizing the energy consumption and maximizing the effective system capacity of the proposed approach over benchmark schemes.

Distributed machine learning (ML) algorithms are imperative for the next-generation Internet of Things (IoT) networks, thanks to preserving the privacy of users’ data and efficient usage of the communication resources. Federated learning (FL) is a promising distributed ML algorithm where the models are trained at the edge devices over the local data sets, and only the model parameters are shared with the cloud server (CS) to generate global model parameters. Nevertheless, due to the limited battery life of the edge devices, improving the energy-efficiency is a prime concern for FL. In this work, we investigate a resource allocation scheme to reduce the overall energy consumption of FL in the relay-assisted IoT networks. We aim at minimizing the overall energy consumption of IoT devices subject to the FL time constraint. FL time consists of model training computation time and wireless transmission latency. Toward this goal, a joint optimization problem, considering scheduling the IoT devices with the relays, transmit power allocation, and computation frequency allocation, is formulated. Due to the NP-hardness of the joint optimization problem, a global optimal solution is intractable. Therefore, leveraging graph theory, joint near-optimal, and low-complexity suboptimal solutions are proposed. Efficiency of our proposed solutions over several benchmark schemes is verified via extensive simulations. Simulation results show that the proposed near-optimal scheme achieves 6, 4, and 2 times lower energy consumption, respectively, compared to the considered fixed, computation adaptation, and power adaptation schemes. Such an appealing energy efficiency comes at the cost of slightly increased FL time compared to the fixed and computation only adaptation schemes.

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In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization.

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novelcross-layer network coding (CLNC)approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.

The device-to-device communication-aided fog radio access network, referred to asD2D-aidedF-RAN, takes advantage of caching at enhanced remote radio heads (eRRHs) and D2D proximity for improved system performance. For D2D-aided F-RAN, we develop a framework that exploits the cached contents at eRRHs, their transmission rates/powers, and previously received contents by different users to deliver the requesting contents to users with a minimum completion time. Given the intractability of the completion time minimization problem, we formulate it at each transmission by approximating the completion time and decoupling it into two subproblems. In the first subproblem, we minimize the possible completion time in eRRH downlink transmissions, while in the second subproblem, we maximize the number of users to be scheduled on D2D links. We design two theoretical graphs, namelyinterference-awareinstantly decodable network coding (IA-IDNC) andD2D conflictgraphs to reformulate two subproblems as maximum weight clique and maximum independent set problems, respectively. Using these graphs, we heuristically develop joint and coordinated scheduling approaches. Simulation results show that the proposed two approaches achieve a considerable performance gain in terms of the completion time minimization.

Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the$\epsilon $-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes.

User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm.

Non-orthogonal multiple access (NOMA) allows users sharing a resource-block to efficiently reuse spectrum and improve cell sum rate$\mathcal {R}_{\mathrm{ tot}}$at the expense of increased interference. Orthogonal multiple access (OMA), on the other hand, guarantees higher coverage. We introduce partial-NOMA in a large two-user downlink network to provide both throughput and reliability. The associated partial overlap controls interference while still offering spectrum reuse. The nature of the partial overlap also allows us to employ receive-filtering to further suppress interference. For signal decoding in our partial-NOMA setup, we propose a new technique called flexible successive interference cancellation (FSIC) decoding. We plot the rate region abstraction and compare with OMA and NOMA. We formulate a problem to maximize$\mathcal {R}_{\mathrm{ tot}}$constrained to a minimum throughput requirement for each user and propose an algorithm to find a feasible resource allocation efficiently. Our results show that partial-NOMA allows greater flexibility in terms of performance. Partial-NOMA can also serve users that NOMA cannot. We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA.

We investigate adaptive transmission schemes over a hybrid radio frequency (RF)/free space optical (FSO) backhaul network for connecting macro-cell base station with small-cell base station through multiple parallel buffer-aided relay nodes. The proposed adaptive transmission schemes improve delay-throughput trade-off of backhaul network by exploiting the degrees-of-freedom over both RF and FSO links and capturing quality-of-service (QoS) requirements. We study two different system configurations, namely, single-carrier and multi-carrier hybrid RF/FSO systems. Our goal is to maximize the constant data arrival rate to the network such that the total queue occupancy in the network is bounded with certain acceptable queue-length bound violation probability. Towards this goal, we formulate novel optimization problems in order to maximize the end-to-end effective capacity over RF and FSO links of both system configurations. Efficient solutions are derived by employing the Lagrangian optimization technique. Some interesting insights are also revealed by considering special cases on link conditions and QoS requirements. Simulation results provide the following two observations: (i) the proposed adaptive transmission scheme outperforms the conventional switch-over hybrid RF/FSO transmission, especially in clear to moderately harsh weather conditions; and (ii) compared to several non-buffer-aided and buffer-aided relaying with non-adaptive transmission schemes, the proposed adaptive transmission scheme substantially improves the supportable data arrival for the hybrid RF/FSO backhaul network.

In the small-cell networks with multiple-input-single-output broadcasting (MISO-BC) backhauls, the joint dirty-paper coding and power control are investigated for the {MISO-BC} backhauls and access links in order to minimize the system transmit power. Considering the proportional rates of MISO-BC backhauls and flow-conservation constraints, the formulated optimization problem is {non-convex}. Moreover, the formulated problem couples the precoding vectors with the power-control variables. In order to handle the {non-convex} optimization problem and decouple the backhaul and access links, the structure of the formulated problem is investigated such that the optimal precoding vectors and optimal power-control variables are independently obtained. Moreover, the optimal precoding vectors are obtained in closed-form expressions. Simulation results are used to show the performance improvement over the benchmark scheme.

In this paper, we focus on analyzing the performance measure of cellular network for massive connectivity of IoT devices considering that the base stations (BSs) are distributed according to Mat\'{e}rn Cluster Process (MCP) while devices are distributed according to Poisson Point Process (PPP). In particular, we develop a generalized approach to calculate connection failure probability of IoT devices in the random access channel (RACH) phase of uplink (UL) transmission. The proposed approach uses a calculation of the devices' association probability rather than using an approximation of the Voronoi tessellation's cells area distribution that is used in PPP. To adopt this approach, we derive the void probability for MCP which is defined as the probability of having no children point of MCP in a given distance. The merit of the proposed approach besides its exact analysis, is that it can be applied for general scenarios of devices and BSs' distributions. Presented numerical results demonstrate the effectiveness and accuracy of the proposed approach. We validate our analysis comparing with the numerical simulations via MATLAB.

In the small-cell networks (SCNs) with multiple small-cell base stations (ScBSs), the joint design of beamforming vectors, user scheduling and ScBS sleeping is investigated with the constraints on proportional rate. A long-term grid-energy expenditure minimization problem is formulated for the considered SCNs, which are powered by the smart grid and natural renewable energy. Since the scheduled user indicators are coupled with the beamforming vectors, the formulated problem is challenging to handle. In order to decouple the beamforming vectors from the scheduled user indicators, the Lyapunov optimization technique is used. As a result, a practical two-scale algorithm is proposed to allocate the user scheduling indicators and ScBS sleeping variables at the coarse-grained granularity (frame) as well as obtain the beamforming vectors at the fine-grained granularity (slot). Numerical results are used to verify the performance of the proposed two-scale algorithm.

The joint design of beamforming vector and artificial noise covariance matrix is investigated for the multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. In the MISOME-SWIPT system, the base station delivers information signals to the legitimate user equipment and broadcasts jamming signals to the eavesdroppers. A secrecy energy efficiency (SEE) maximization problem is formulated for the considered MISOME-SWIPT system with imperfect channel state information, where the SEE is defined as the ratio of sum secrecy rate over total power consumption. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Two suboptimal solutions are also proposed based on the heuristic beamforming techniques that trade performance for computational complexity. In addition, the analysis of computational complexity is performed for the optimal and suboptimal solutions. Numerical results are used to verify the performance of proposed algorithms and to reveal practical insights.

Fog radio access networks (F-RANs) have recently been postulated as an innovative solution to improve the fronthaul capacities of cloud base stations (CBSs). This architecture extends the CBS service by involving enhanced remote radio heads (eRRHs), which can pre-store and transmit popular files at the network edge (i.e., close to the end users). This is referred to as caching, and it allows the offloading of CBS resources, e.g., time and frequency. Recent works have been proposed to use rate-aware network coding in order to exploit the previously downloaded popular files at the users’ devices. As such, the CBS offloading is maximized. However, the users’ achieved Quality of Service (QoS), and the standard F-RANs physical-layer resource optimization have not received any attention to date. This paper proposes use of an innovative cross-layer network coding (CLNC) to address the above-mentioned issues. The proposed CLNC scheme is not only aware of different users’ rates but also controls the rates by jointly optimizing coding combinations, users-eRRHs/power zones (PZs) assignments, and transmission power in the PZs. Using a graph theoretical representation, we formulate the joint cross-layer CBS offloading and QoS guarantee problem and show its NP-hardness. Joint and iterative heuristic approaches are then developed to solve this problem using greedy vertex search and coloring techniques. The proposed approaches are finally validated and tested against the existing algorithms in the literature.

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.

The latest release of the data over cable service interface specification (DOCSIS), namely DOCSIS 3.1, has introduced the use of adaptive bit loading profiles. The design of these profiles is critical for the overall performance of a hybrid fiber coaxial (HFC) cable system. Previous works on profile optimization for DOCSIS networks have mainly focused on grouping cable modems (CMs) based on their signal-to-noise ratio (SNR) similarity to maximize the system’s capacity. However, this approach can be inefficient due to effect of data demand and traffic variations that can highly affect the overall system performance. In this paper, we introduce a profile design approach to maximize the average throughput of an HFC system while considering the effect of both CMs’ traffic and channels SNRs on the system’s performance. Moreover, the profile optimization should not require knowledge of future instantaneous data arrival rates, which is not possible to obtain in practice. Thus, our proposed approach uses only average information which can be easily predicted using learning techniques. We show that the proposed approach yields significantly high performance, particularly for scenarios with high degree of heterogeneity in the CMs’ traffic.

Multi-dimensional (MD) mapping offers more flexibility in mapping design for bit-interleaved coded modulation with iterative decoding (BICM-ID) and potentially improves the bandwidth efficiency. However, for higher order signal constellations, finding suitable MD mappings is a very complicated task due to the large number of possible mappings. In this paper, a novel mapping method is introduced to construct efficient MD mappings to improve the error performance of BICM-ID over Rayleigh fading channels. We propose to break the MD mapping design problem into four distinct 2-D mapping functions. The 2-D mappings are designed such that the resulting MD mapping improves the BICM-ID error performance at low signal-to-noise ratios (SNRs). We also develop cost functions that can be optimized to improve the error performance at high SNRs. The proposed mapping method is very simple compared to well-known mapping methods, and it can achieve suitable MD mappings for different modulations including higher order modulations for BICM-ID. The simulation results show that our mappings significantly outperform the previously known mappings at a target bit error rate (BER) of 10−6. Our mappings also offer a lower error-floor compared to their well-known counterparts.

In this paper, we consider the use of cross- layer network coding and fog radio access networks (F- RANs) as a means to jointly optimize users quality-of-service and offload the cloud servers and cellular macro base- stations. Multiple edge nodes called enhanced remote radio heads (eRRHs) are connected to a central unit known as cloud base station (CBS). The transmit frame of each eRRH consists of multiple resources blocks called power zones (PZs), each fixed at a pre-assigned power level. The various ergodic capacities of different users at different PZs/eRRHs and the CBS bring a new trade-off between the number of multiplexed users and the transmission rates of each PZ and each CBS allocated channel. The proposed framework incorporates such information in the network coding decisions. As such, the multiplexed users, encoded files, and transmission rates of each PZ/eRRH and each CBS allocated channel maximizes the throughput which is defined as the number of correctly received bits and actual CBS physical-resource offloading, respectively. The problem is first formulated using graph theory techniques, and its intractability is shown. Given the difficulty of the problem, the paper proposes a heuristic approach by dividing it into two sequential subproblems and solving each subproblem efficiently. Presented simulation results reveal that the proposed solution achieves small offloading performance degradation compared to the cross-layer QoS unaware scheme but largely maximizes the received throughput compared to the state-of- art algorithms.

The joint design of beamforming vector and artificial noise covariance matrix is investigated for multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. A secrecy energy efficiency (SEE) minimization problem is formulated in the MISOME-SWIPT system with imperfect channel state information and proportional secrecy rate constraints. Since the formulated SEE minimization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Numerical results are used to verify the performance of the proposed algorithm and to reveal practical insights.

Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high- altitude flying relays to support communications between ground nodes due to their ability to provide line-of- sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the high-rate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.

Most of the drone-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of these flying units. This paper proposes an efficient spectrum and energy management solution by integrating the overlay cognitive radio technology. Therefore, we aim to use the drone as a secondary node and target to determine an optimized three-dimensional location and a resource control solution by which it can complete its data transfer and in parallel support the primary communication. To this end, a non-convex optimization problem is developed. The obtained solution minimizes the total energy consumption of the drone and, at the same time, maintains the required data rate level of the spectrum's owner. A resource allocation procedure and swarm intelligence-based positioning algorithm are jointly designed for this purpose. Numerical results show the efficiency of the proposed approach in terms of energy consumption savings and additional transmission opportunities as compared to other schemes.

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.

This paper proposes an energy-efficient solution to employ a drone to gather data from spatially distributed wireless sensors. Specifically, we minimize the energy consumption of the drone while accomplishing a tour to collect the needed data. This tour accounts for both the energies due to data transfer and to the motion of the drone. The objective is to determine the positions where the drone stops to collect data from each cluster of sensors as well as the path that the drone needs to follow to complete its data gathering tour. The problem is decomposed into two sub-problems. The first sub-problem targets to determine the sub-groups of sensors and stop positions to collect data from each sub- group using a clustering approach. In the second sub- problem, we employ the travel salesman solution to find the path among these stops Selected numerical results show that the achieved energy is considerably reduced compared to the one of traditional approaches. Moreover, we study the behavior of the drone versus various system parameters.

We propose a statistical delay quality-of-service (QoS) aware joint power allocation and relaying link selection scheme for uplink of a multichannel coherent free space optical communication-based fronthaul network. The proposed scheme assigns suitable relays and aggregation nodes to the remote radio heads and allocates transmit power among the orthogonal optical channels. Specifically, the proposed scheme maximizes total end-to-end effective capacity of a fronthaul network subject to certain transmit power budgets at both remote radio heads and relay nodes. The joint power allocation and relaying link selection are formulated as a mixed integer non-linear programing problem. We obtain near optimal solution to such an optimization problem by using Lagrangian dual decomposition and minimum weight matching techniques. Our analysis reveals that in order to maximize the aggregate end-to-end effective capacity, transmit power allocation and relaying link selection should consider both statistical delay-QoS requirements of the transmitted traffics and channel gains of the transmission links. Simulation results demonstrate that our proposed scheme improves statistical delay-QoS aware throughput in presence of atmospheric turbulence fading and pointing error.

Data over cable service interface specification (DOCSIS) introduced the possibility of a variable bit-loading over the subcarriers within a channel in its release DOCSIS 3.1. This variable bit-loading will improve the data rates. However, to limit the encoding processing overhead, the concept of profiles was introduced. Each profile defines the modulation per subcarrier for a given channel while the number of allowed profiles is limited. Thus, an efficient profile assignment scheme, which determines the best set of profiles based on the users’ channel conditions, is needed. Although various profile assignment algorithms have been proposed in the literature, realistic evaluation of these schemes has been difficult, as channel quality measurements of real DOCSIS 3.1 systems has not previously been available. In this paper, we exploit DOCSIS 3.1 measurement data to evaluate performance of the proposed algorithms. We propose to employ principal component analysis to derive low-dimensional clustering variables in order to ensure efficient profile optimization. We show how this technique can be employed with different clustering algorithms to improve the spectrum efficiency of the profiles by extracting the most important information of the channels in low-dimensional vectors. This not only reduces the complexity of the clustering, but also ensures better throughput. Moreover, we adapt the clustering algorithms to tailor them to the profile optimization problem. Finally, we present an exhaustive simulation-based performance analysis to compare the different algorithms for various scenarios using extrapolation of the measurements data.

The joint design of beamforming and artificial noise vectors is proposed for the full-duplex simultaneous wireless information and power transferring (FD-SWIPT) systems when multiple eavesdroppers can wiretap information from FD base station (FD-BST) and FD user equipment (FD-UE). To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a sum-information-transmission-rate-maximization (SITRM) problem under information-leakage and energy constraints. Besides, we consider the fairness issue between uplink and downlink information-transmission rates by formulating a fairness-aware-SITRM (FA-SITRM) problem. Since the eavesdroppers can receive the information of FD-BST and FD-UE, the information-leakage region becomes non-convex and challenging to handle. Hence, we propose efficient algorithms to solve the SITRM and FA-SITRM problems optimally via semidefinite relaxation (SDR) and a 1-D search. In each search iteration, our proposed algorithms can recover the rank-one constraints when the application of SDR cannot obtain the optimal solutions. Moreover, we analyze the computational complexities and propose two suboptimal solutions to the formulated problems. For the SITRM problem, our numerical results show that the performance achieved by one of two suboptimal algorithms is close to the performance of optimal algorithm with increasing the maximum transmission power of FD-BST.

We investigate the precoding, remote radio head (RRH) selection and signal splitting in the simultaneous wireless information and power transferring (SWIPT) cloud radio access networks (C-RANs). The objective is to minimize the power consumption of the SWIPT C-RAN. Different from the existing literature, we consider the nonlinear fronthaul power consumption and the multiple antenna RRHs. By switching off the unnecessary RRHs, the group sparsity of the precoding coefficients is introduced, which indicates that the precoding process and the RRH selection are coupled. In order to overcome these issues, a group sparse precoding and signal splitting algorithm is proposed based on the majorization-minimization framework, and the convergence behavior is established. Numerical results are used to verify our proposed studies.

Statistical delay quality-of-service (QoS) aware adaptive resource allocation scheme is proposed for a multi-carrier coherent free space optical (FSO) communications based fronthaul network. The proposed resource allocation assigns remote radio heads (RRHs) to the suitable aggregation nodes (ANs) and allocates the transmit power to the orthogonal optical carriers. Specifically, the proposed resource allocation provides delay-QoS at the link layer by maximizing the effective sum capacity of the all the RRHs subject to transmit power budgets at the RRHs and capacity constraint of the wired fronthaul links connecting the ANs with the baseband unit (BBU) pool. The considered resource allocation is formulated as a mixed-integer non-linear programing (MINLP) problem. We use two transmission link optimization techniques, namely, independent link optimization (ILO) and joint link optimization (JLO), in order to solve the proposed MINLP problem. Under both optimization techniques, the proposed MINLP problem is decomposed into two subproblems which are iteratively solved in order to obtain the optical transmit power allocation and assignments of RRHs to the ANs. Our analysis reveals that the optical transmit power allocation and RRH-AN assignments depend on both the atmospheric turbulence fading and delay-QoS requirements. Numerical results demonstrate that the JLO technique achieves significant higher effective capacity (EC) compared to the ILO technique in the strict statistical delay-QoS constraints. However, the EC performance gap between the JLO and ILO techniques is reduced in the loose statistical delay-QoS constraints.

Micro unmanned aerial vehicles (MUAVs) have attracted much interest as flexible communication means for multiple applications due to their versatility. Most of the MUAV-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of the flying units. These characteristics are the origin of two main challenges faced by MUAV-based communication: 1) efficient-energy management, and 2) opportunistic spectrum access. This paper proposes an energy-efficient solution, considering the hover and communication energy, to address these issues by integrating cognitive radio (CR) technology with MUAVs. A non-convex optimization problem exploiting the mobility of MUAVs is developed for the underlay CR technique. The objective is to determine an optimized three-dimension (3D) location, for a secondary MUAV, at which it can complete its data transfer with minimum energy consumption and without harming the data rate requirement of the primary spectrum owner. Two algorithms are proposed to solve these optimization problems: a meta-heuristic particle swarm optimization algorithm (PSO) and a deterministic algorithm based on Weber formulation. Selected numerical results show the behavior of the MUAV versus various system parameters and that the proposed solutions achieve very close results in spite of the different conceptional constructions.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for 5G wireless technology. In this paper, we first discuss the fundamentals of uplink and downlink NOMA transmissions in a cellular system and outline their key distinctions in terms of implementation complexity, detection and decoding at the SIC receiver(s), and the intra-cell and inter-cell interferences. Later, for both downlink and uplink NOMA, for each individual user in a two-user NOMA cluster, we theoretically derive the NOMA dominant condition, which refers to the condition under which the spectral efficiency gains of NOMA are guaranteed compared to conventional orthogonal multiple access (OMA). The conditions, which are distinct for uplink and downlink as well as for each individual user, provide direct insights into selecting appropriate users in two-user NOMA clusters. Numerical results show the significance of the derived conditions for user selection in uplink/downlink NOMA clusters and provide a comparison to the random user selection. Finally, a brief summary of the recent research investigations is provided which is followed by a discussion on the research challenges and future research directions.

The spread in the use of wireless services resulted in a remarkable growth of power consumption for telecommunication systems to satisfy the continuous growth of data demand. On the other hand, the emergence of smart grids and the expansion of low-cost distributed powering solutions represented an opportunity to enhance the energy efficiency and improve communication systems' costs. In this paper, we propose to enhance the energy efficiency of self-powered wireless networks by exploiting the possibility of energy exchange between different micro-grids. The objective is to exploit the flexibility and non homogeneity of communication services' demand to maximize the global utility. Cooperation between cells is exploited to exchange additional/needed power as function of their respective users' demands and energy availabilities. While taking into consideration the power losses due to this exchange, we propose an efficient approach to allocate the available resources across the different cells. Numerical simulations show the gains that can be achieved due to this approach as a function of the demand and energy availability variations.

Using the theory of Poisson cluster process (PCP), this paper provides a framework to analyze multi-cell uplink non-orthogonal multiple access (NOMA) systems. Specifically, we characterize the rate coverage probability of an NOMA user who is at rank m (in terms of the distance from its serving base station) among all users in a cell and the mean rate coverage probability of all users in a cell. Since the signal-to-interference-plus-noise ratio of the mth user relies on efficient successive interference cancellation (SIC), we consider three scenarios, i.e., perfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user are decoded successfully), imperfect SIC (in which the signals of m - 1 interferers who are stronger than the mth user may or may not be decoded successfully), and imperfect worst case SIC (in which the decoding of the signal of the mth user is always unsuccessful whenever the decoding of its relative m -1 stronger users is unsuccessful). To derive the rate coverage expressions, we first characterize the Laplace transforms of the intra-cluster interferences in closed-form considering various SIC scenarios. The Laplace transform of the inter-cluster interference is then characterized by exploiting distance distributions from geometric probability. The derived expressions are customized for an equivalent OMA system. Finally, numerical results are presented to validate the derived expressions. The worst case SIC assumption provides remarkable simplifications in the mathematical analysis and is found to be highly accurate for higher user target rate requirements. A comparison of Poisson point process-based and PCP-based modeling is also conducted.

In this paper, we consider a coordinated multipoint dynamic cell selection (DCS) transmission scheme for serving sleeping cell user equipments (UEs). According to this DCS scheme, packets of UEs in a sleeping cell are randomly forwarded to the potential active base stations (BSs) by the packet serving gateway and UEs in the sleeping cell dynamically select their serving BS from these active BSs. We model the system as a fork/join (F/J) queuing system and develop a cross-layer analytical model that considers the time varying nature of the channels, channel scheduling mechanism, partial channel quality information feedback, cell selection mechanism, bursty packet arrivals, and packet scheduling mechanism. The developed analytical model can be used to measure various packet-level performance parameters, such as packet loss probability (PLP) and queuing delay while accounting for out-of-sequence packet delivery. We validate the accuracy of the developed analytical model via simulations, and we compare the performance of the DCS scheme under consideration with the conventional fixed cell selection scheme and with the state-of-the-art DCS scheme. Presented numerical results show that the DCS scheme under consideration significantly improves the PLP performance. Queuing delay performance, on the other hand, depends on the system and operating parameters.

The application of renewable energy is a promising solution for realizinggreen communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a per-frame grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the zero-forcing beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) offers improved error performance over additive white Gaussian noise and fading channels if it uses a wisely designed signal mapping. Furthermore, error performance of BICM-ID can be significantly improved by employing multi-dimensional (MD) modulation. However, suitable MD mappings are obtained by computer search techniques, except for MD modulations that use small constellations, e.g., binary phase shift keying, quadrature phase shift keying, and 8-ary phase shift keying as basic modulation. The alphabet size of MD modulations increases exponentially as the order of the basic modulation increases and computer searching becomes intractable. In this paper, we propose a systematic mapping method for MD modulations. The innovation of our proposed method is that it generates MD mappings using 16- and 64-quadrature amplitude modulation very efficiently. The presented numerical results show that the proposed method improves the bit error rate of BICM-ID.

In order to improve the delay quality-of-service aware throughput performance of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted multichannel coherent optical wireless communication system. The proposed joint power allocation and relay selection scheme provides the delay aware quality-of-service guarantee by maximizing the end-to-end effective capacity under the average transmit power constraints at both source as well as relay nodes. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate that the proposed joint power allocation and relay selection scheme provides the strict statistical delay aware quality-of-service with an improved throughput without increasing the transmit power requirements.

In order to improve the energy efficiency of the optical wireless communications, a joint power allocation and relay selection scheme is proposed for a relay assisted non-line-of-sight optical wireless communication system employing coherent detection with a dual optical channel multiplexing technique. The proposed joint power allocation and relay selection scheme aims to improve energy efficiency of the optical wireless communication system by minimizing the total average transmit power, and provides the delay aware quality-of-service guarantee through maintaining a minimum required effective capacity over the atmospheric turbulence fading channels. The joint power allocation and relay selection problem is formulated as a non-convex and combinatorial optimization problem, and the solution of this optimization problem is obtained using a time-sharing relaxation technique. Subsequently, a fast-convergent algorithm is proposed for the joint power allocation and relay selection by considering the delay aware quality-of-service requirements of the services transmitted over the optical wireless communication channels. Numerical results demonstrate the advantages of the proposed joint power allocation and relay selection scheme in terms of the energy saving for different statistical delay constraints.

We investigate a quality-of-service-aware and energy-aware adaptive power allocation scheme for a point-to-point and line-of-sight optical wireless communication system employing the coherent detection and polarization multiplexing. The proposed power allocation scheme minimizes the average transmit power and provides the delay aware quality-of-service guarantee through maintaining a required effective capacity over the fading channels. The power allocation scheme is formulated as a convex optimization problem, and using the sub-gradient method, a fast convergent algorithm for the optimal power allocation is proposed. Numerical results demonstrate the advantages of the proposed power allocation algorithm in terms of the energy saving for the case of having strict statistical delay constraints.

In this paper, we develop a data rate fairness optimal cooperative beamforming technique for a cognitive radio system with multiple secondary receivers (SRs) and multiple primary receivers (PRs) in the presence of asynchronous interferences. In particular, the data rate fairness beamforming design is formulated as an optimization problem to maximize the minimum data rate of the SRs subject to transmission power constraints at the secondary cooperative transmitters (SCTs) and asynchronous interference power constraints at the PRs. The optimal beamforming design is a quadratically constrained quadratic program max-min optimization problem, which is non-convex and non-linear. Therefore, we reformulate the optimal beamforming design as a quasi-convex problem using the semidefinite program (SDP) relaxation, which can be solved using the SDP solvers and bisection method. The complexity of the optimal beamforming scheme stands against its practical implementation. So, we study important properties of the optimization problem. By exploiting these properties, we also propose low complexity suboptimal beamforming techniques. Furthermore, we extend the beamforming techniques to incorporate the uncertainties in channel and propagation delay estimation between SCTs and PRs.

In this paper, we consider a low-complexity uniform power loading scheme for orthogonal frequency division multiplexing (OFDM) systems with two reduced feedback mechanisms and analyze its performance over Rayleigh fading channels. In the first feedback mechanism, the receiver feeds back to the transmitter the channel gains and the indices of the best$M$subchannels; while for the second feedback mechanism, the receiver feeds back only the indices of the best$M$subchannels. The available power budget is equally distributed over the best$M$subchannels for both feedback mechanisms. We derive closed-form expressions for the achievable capacity and an upper bound on the outage capacity of the first and second mechanisms, respectively. A simple elimination algorithm is provided to find the optimal number of best subchannels$M$that maximizes the achievable capacity. Numerical results show the dependence of the optimal number of the best subchannels$M$on the system parameters. Additionally, the presented results interestingly show that the low-complexity uniform power loading scheme can achieve up to 98.72% of the channel capacity, obtained using the well-known waterfilling solution, when the optimal value of$M$is used. Moreover, the uniform power loading scheme can achieve up to 88.86% of the energy efficiency at reduced complexity.

We propose a distributed beamforming for cooperative cognitive radio (CR) networks, that reduces the required feedback overhead in terms of sharing the channel state information (CSI) between the cooperating CR relays. Assuming the reciprocity of the channels of each cooperating CR node toward the CR receiver, as well as toward the primary receiver, the proposed beamforming technique only requires sharing the location information of the CR relays among the cooperating CR relays, rather than the global CSI. Reducing the required feedback overhead provides enhanced scalability of the cooperative CR network with lower deployment cost. In this paper, we also propose two autonomous decision making strategies that can help each CR user to independently decide whether to participate in the cooperative transmission or not. This participation decision is tackled from a game theoretic approach, by quantifying the reward and cost functions of each CR relay. The first proposed strategy, named regret testing-based strategy, is proved to asymptotically achieve an approximate Nash equilibrium state of the system. However, it has higher complexity and slower convergence time. So we also propose a suboptimal decision making strategy, named learning-based strategy, that has lower complexity and faster convergence time.

In this paper, we consider a coordinated multipoint (CoMP) dynamic cell selection (DCS) scheme in order to improve performances of sleeping cell users. According to this DCS, packets for user equipments (UEs) in a sleeping cell are randomly forwarded to the potential active base stations (BSs) by the packet serving gateway (PSG) and UEs in the sleeping cell dynamically select these active BSs to be served in each time slot. We develop an analytical model using the fork/join (F/J) queuing model that considers the time varying nature of the channels, the channel scheduling mechanism, partial channel quality feedback, bursty packet arrivals and the packet scheduling mechanism. The developed analytical model can be used to measure various packet level performance parameters such as packet loss probability (PLP) and queuing delay while accounting for out-of-order packet delivery. Presented numerical results show that the considered CoMP DCS scheme provides significant performance improvement compared to the traditional fixed cell selection.

In this paper, we consider an orthogonal frequency division multiple access based full duplex distributed antenna system (FDDAS) with energy recycling capability and develop an energy efficient resource allocation scheme for such system. In particular, in order to optimize the energy efficiency of FDDAS, we formulate the problem of joint subchannel allocation and power control as a mixed integer nonlinear programming problem. Since the formulated optimization problem is a NP-hard problem whose computation complexity will rise exponentially with larger network scale, we develop a low complexity subchannel allocation and power control algorithm. The convergence property of the proposed algorithm is established. Simulation results show that the proposed algorithm for FDDAS results in a higher system energy efficiency than the existing algorithm for distributed antenna systems without energy recycling capability.

Effective capacity is defined as the maximum constant traffic arrival rate that a communication channel can support in order to guarantee a certain statistical delay constraint. We investigate the effective capacity performance of the optical wireless communications employing the coherent detection and polarization multiplexing over the Gamma-Gamma turbulence fading channels for two different power adaptation techniques, namely, independent power adaptation and joint power adaptation. In a polarization multiplexing system, independent power adaptation allocates transmit power to a particular channel considering only the signal-to-noise ratio statistics of that particular channel whereas the joint power adaptation allocates transmit power to a particular channel considering the joint signal-to-noise ratio statistics of both channels. Our analysis reveals the superiority of the joint power adaptation technique in order to support the stringent statistical delay constraint services over the strong turbulence fading channels compared to an independent power adaptation technique. However, the numerical results demonstrate that the performance gap between the joint and independent power adaptations gets significantly reduced as the statistical delay constraints become loose and/or the turbulence fading gets weaker.

In this paper, we develop a cooperative beamforming technique for a cooperative cognitive radio (CR) system with multiple secondary cooperative transmitters (SCTs), secondary receivers (SRs) and primary receivers (PRs) in presence of asynchronous interferences. The objective of the beamforming technique is to maximize the minimum data rate of the SRs subject to transmission power constraints at the SCTs as well as asynchronous interference power constraints at the PRs. The optimal beamforming design is a quadratically constrained quadratic program max-min optimization problem, which is non-convex and non-linear. Therefore, we reformulate the optimal beamforming design as a quasi-convex problem by using the so-called semidefinite program (SDP) relaxation technique that can be solved using the standard SDP solvers and bisection method. However, the computational complexity of the optimal beamforming technique is high. Therefore, we study important properties of the optimization problem and propose a low complexity suboptimal beamfoming technique by using the properties.

In a cognitive radio (CR) network, a group of CR users can act as relays for a given user to improve the quality of communications for this user. To achieve such cooperative transmission, we propose a distributed beamforming technique that reduces the required feedback overhead in terms of sharing the channel state information between the cooperating CR relays. In return for their cooperation, the assisted CR user can lease its own channel, for a certain amount of time, to the cooperating CR relays for their opportunistic access. However, a particular CR relay spends a certain amount of power to forward the data to the assisted CR user. Therefore, we also propose an autonomous decision making strategy that can help each CR user to independently decide whether to participate in the cooperative transmission or not, by quantifying the reward and cost functions of each CR relay. The presented numerical results show an enhancement in the achievable sum rate of the cooperative CR network up to 43% for a primary channel occupancy probability of 0.3, compared to the case of non cooperative transmission.

In this paper, we analyze the performance of two low-complexity uniform power loading with reduced- overhead orthogonal frequency division multiplexing (OFDM) schemes over Rayleigh fading channels. In the first feedback scheme, the receiver feeds back to the transmitter the channel gains and the indices of the best $M$ subchannels; while for the second feedback scheme, the receiver feeds back only the indices of the best $M$ subchannels. In both schemes, the available power budget is equally distributed over the best $M$ subchannels. We derive closed-form expressions for the capacity and the outage capacity of the first and second schemes, respectively. Numerical results show that there is an optimal number of the best subchannels, $M$, that maximizes the achievable capacity and it depends on the system parameters.

In this paper, we adopt an energy-efficiency (EE) metric, namedworst-EE, that is suitable for EE fairness optimization in the uplink transmission of amplify-and-forward (AF) cooperative orthogonal frequency division multiple access (OFDMA) networks. More specifically, we assign subcarriers and allocate powers for mobile and relay stations in order to maximize the worst-EE, i.e., to maximize the EE of the mobile station (MS) with the lowest EE value, subject to MSs transmit power, relay station (RS) transmit power, and MSs quality-of-service (QoS) constraints. The formulated primal max–min optimization problem is nonconvex fractional mixed integer nonlinear program, i.e., NP-hard to solve. We provide a novel optimization framework that studies the structure of the primal problem and prove that the dual min–max optimization problem attains the same optimal solution of the primal problem. Additionally, we propose a modified Dinkelbach algorithm, named dual Dinkelbach, to achieve the optimal solution of the dual problem in a polynomial time complexity. We further exploit the structure of the obtained optimal solution and develop a low complexity suboptimal heuristic. Numerical results show the effectiveness of the proposed algorithm to improve the network performance in terms of fairness between MSs, worst-EE, and average network transmission rate when compared to traditional schemes that maximize the EE of the whole network. Presented results also show that the suboptimal heuristic balances the achieved performance and the computational complexity.

Small-cell networks (SCNs) have emerged as a potential solution to the rapidly increasing demand for high-data-rate services over wireless networks. The small-cell access nodes (SANs) provide service to users through the access link and can be connected to the core/global network, preferably, via a wireless backhaul link. In this paper, we develop a queuing analytical model that considers the channel scheduling mechanisms in both links of SCNs, the time-varying nature of the channels, bursty packet arrivals, and the network topology e.g., the number of SANs and the coverage of the small cells. For the access link, we consider the so-called max rate/opportunistic channel scheduling mechanism, while for the backhaul link, we consider three different channel scheduling mechanisms, namely, fixed channel scheduling, round-robin channel scheduling, and access-link-dependent channel scheduling. Our developed analytical model is useful for gauging various data-link-layer performance measures, such as packet loss probability and average queuing delay of the packets, for the channel scheduling mechanisms under consideration. Presented numerical examples show that the choice of channel scheduling mechanism in the backhaul link is not unique and it depends on the operating scenario and required quality-of-service (QoS) parameters. The developed queuing model can also facilitate cross-layer design to meet the required QoS parameters. Presented simulation results validate the accuracy of the developed model.

This paper characterizes the spectral efficiency of an uplink radio frequency (RF)-powered macrocell network considering harvest-then-transmit protocol such that the macrocell users transmit in the uplink while replenishing the energy from their serving base station (BS) in the downlink. Using the theory of order statistics, a tractable mathematical framework is developed to derive the uplink spectral efficiency and the downlink power consumption resulting due to wireless energy transfer. The framework captures the impact of the locations of the users that are selected for uplink transmission, their channel statistics for information and energy transfer, and different user selection schemes. We first analyze the performance of state-of-the-art greedy and round-robin scheduling schemes in RF-powered cellular networks. Closed-form expressions for the minimum power outage probability (i.e., the probability that the selected user is unable to harvest sufficient power for uplink transmission) are also derived. We then develop modified versions of the conventional user selection schemes that improve the spectral efficiency on a given uplink transmission channel with zero power outage probability (i.e., probability of outage due to insufficient amount of harvested power). The developed schemes are shown to outperform the conventional user scheduling schemes in terms of the throughput and energy harvesting time with a trade-off in fairness among users. The accuracy of the expressions is validated via Monte-Carlo simulations. Numerical results highlight the trade-offs associated with the various user selection schemes as a function of network parameters.

We propose a new bit-interleaved coded modulation (BICM)-based cooperative communication system where different BICM modules can be optimized jointly considering the average signal to noise ratios of the direct and the two-hop Rayleigh fading channels. As such, the full benefit of BICM can be exploited in the context of cooperative communication. Our design considers cooperative communication systems with so called max-min relay selection scheme that has no loss in performance in terms of diversity- multiplexing trade off in orthogonal cooperation. The presented numerical results for rate 1/2 convolutional code with 8-ary pulse amplitude modulation equivalently 64-ary quadrature amplitude modulation show that the proposed design can offer gains up to 1.4 dB over the traditional BICM design for a target bit error rate of 10^(-6). Moreover the results show that the amount of gain depends on the relays' positions and increases with the number of relays available for selection.

Asymmetry in average channel conditions is a challenging factor in two-way relay communication (TWRC) system in order to obtain a balanced performance at two end nodes. In this paper, for a coded TWRC system, we consider hierarchical modulation and network coding assisted bit interleaved coded modulation (HMNC-BICM) based system. The performance of the considered HMNC-BICM based system is compared with the bidirectional network coding assisted traditional bit-interleaved coded modulation (BNC-BICM) based system that uses regular signal constellations and a single interleaver. The presented simulation results show that the HMNC-BICM system not only provides the flexibility of having same reliability at both end nodes but also it has a superior performance in terms of average BER in an asymmetric TWRC system.

In this paper, competition among multiple secondary users (SUs) for spectrum access is modeled as a simultaneous repeated auction. Upon participation in an auction, a SU is charged with an entry fee. However, participation does not ensure an access to the channel. This tradeoff leads it to decide either for or against entering the auction. We consider no cooperation among the SUs, and model this situation as a Bayesian game. A modification of the standard regret testing procedure is proposed to fit our system model. Our proposed procedure converges to Nash equilibrium (NE) of the game. Since this procedure is computationally expensive, we propose a less expensive learning based procedure for the decision taking. We present computer simulation results to compare the average profits and bidding efficiencies over time for the proposed procedure. We also compare their bidding efficiencies to another procedure in the literature, based on second highest bid prediction.

Small-cell networks (SCNs) have emerged as a potential solution to the rapidly increasing demand for high data rates. The base stations (also referred to as access nodes) of the small cells provide service to a group of users through the access link and they are connected to the core/global network via a backhaul link. In this paper, we develop a queuing analytical model that considers the channel scheduling mechanisms in both links, the time varying nature of the channels as well as bursty packet arrivals. For the access link we consider the so-called max rate/opportunistic channel scheduling mechanism, while for the backhaul link we consider three different channel scheduling mechanisms, namely, fixed channel scheduling, round-robin channel scheduling and an access-link dependent channel scheduling mechanisms. Our developed analytical model is useful to measure various data link layer performances such as packet loss probability and average queuing delay. These performance measures can be used to determine which of the scheduling mechanisms considered for the backhaul link provides the best performance.

In a cooperative cognitive radio (CR) network when a group of CR users acts as relays for a given user, a cooperative beamforming among these relays can be used to improve the quality of communications. However, this cooperative beamforming can introduce asynchronous interference at the primary receivers due to different propagation delays between different CR relays and the primary receivers. We propose an innovative cooperative beamforming method that maximizes the received signal power at the secondary destination while keeping the asynchronous interference signals at multiple primary receivers below their corresponding target thresholds. Moreover, in this paper, we propose two relay selection strategies in conjunction with cooperative beamforming. The presented numerical results show that the relay selection schemes can further improve the received signal power at the secondary destination.

Experimental measurements have shown that the noise in many communication channels is non-Gaussian. Bit interleaved coded modulation (BICM) is very popular for spectrally efficient transmission. Recent results have shown that the performance of BICM using convolutional codes in non-fading channels can be significantly improved if the coded bits are not interleaved at all. This particular BICM design is called BICM trivial (BICM-T). In this paper, we analyze the performance of a generalized BICM-T design for communication over Gaussian mixture noise (GMN) channels. The results disclose that for an optimal bit error rate (BER) performance, the use of an interleaver in BICM for GMN channels depends upon the strength of the impulsive noise components in the Gaussian mixture. The results presented for 16-QAM show that the BICM-T can result in gains up to 1.5 dB for a target BER of 10−6if the impulsive noise in the Gaussian mixture is below a certain threshold level. The simulation results verify the tightness of developed union bound (UB) on BER performance.

The performance of bit-interleaved coded modulation (BICM) using convolutional codes in nonfading channels can be significantly improved if the coded bits are not interleaved at all. This particular BICM system is referred to as BICM trivial (BICM-T). In this paper, we analyze a generalized BICM-T system that uses a nonequally spaced signal constellation in conjunction with a bit-level multiplexer in an additive white Gaussian noise (AWGN) channel. As such, one can exploit the full benefit of BICM-T by jointly optimizing different system modules to further improve its performance. We also investigate the performance of the considered BICM-T system in the Gaussian mixture noise (GMN) channel because of its practical importance. The presented numerical results show that an optimized BICM-T system can offer gains up to 1.5 dB over a non-optimized BICM-T system in the AWGN channel for a target bit error rate of$10^{-6}$. The presented results for the GMN channel interestingly reveal that if the strength of the impulsive noise component, i.e., the noise component due to some ambient phenomenon in the GMN, is below a certain threshold level, then the BICM-T system performs significantly better as compared to traditional BICM system.

To ensure customer satisfaction and greater market acceptance, voice over Wi-Fi networks must ensure voice quality under various network parameters, configurations and traffic conditions, and other practical effects, e.g., channel noise, and capturing effects. An accurate voice capacity estimation model considering these factors can greatly assist network designers. In the current work, we propose an analytical model to estimate voice over Internet Protocol (VoIP) capacity over Wi-Fi networks addressing these issues. We employ widely used ITU-T E-model to assess voice quality and VoIP call capacity is presented in the form of an optimization problem with voice quality requirement as a constraint. In particular, we analyze delay and loss in channel access and queue, and their impacts on voice quality. The proposed capacity model is first developed for a single hop wireless local area network (WLAN) and then extended for multihop scenarios. To model real network scenario closely, we also consider channel noise and capture effect, and analyze the impacts of transmission range, interference range, and WLAN radius. In absence of any existing call capacity model that considers all the above factors concomitantly, our proposed model will be extremely useful to network designers and voice capacity planners.

Base station (BS) sleeping has emerged as a viable solution to enhance the overall network energy efficiency by inactivating the underutilized BSs. However, it affects the performance of users in sleeping cells depending on their BS association criteria, their channel conditions toward the active BSs, and scheduling criteria and traffic loads at the active BSs. This paper characterizes the performance of cellular systems with BS sleeping by developing a systematic framework to derive the spectral efficiency and outage probability of downlink transmission to the sleeping cell users taking into account the aforementioned factors. In this context, a user association scheme is also developed in which sleeping cell users associate to a BS with maximum mean channel access probability (MMAP). The MMAP-based user association scheme adapts according to traffic load and scheduling criteria at the active BSs. We consider greedy and round-robin schemes at active BSs for scheduling users in a channel. We also derive the analytical results for the conventional maximum received signal power (MRSP)-based user association scheme. Finally, we derive the statistics of the received signal and interference power to evaluate the downlink spectral efficiency of a given sleeping cell user. Numerical results provide a comparison between two user association schemes as a function of system parameters and demonstrate the efficacy of the MMAP-based association scheme in non-uniform traffic load scenarios.

In this paper, we study rate allocation mechanisms that allocate available transmission rate of a particular secondary user (SU) among its different classes of services assuming that a specific channel assignment algorithm across active SUs is in place. In particular, we formulate the rate allocation mechanism of a SU between its two different classes of services namely, delay sensitive (DS) and best effort (BE) services as a Markov decision process. Then the optimal rate allocation mechanism that minimizes the average queuing delay of DS packets while guaranteeing the packet loss probabilities of both classes of packets is obtained using a linear programming technique. Since the optimal rate allocation mechanism can be complex for implementation in a SU device due to its probabilistic nature, we also study a low-complexity suboptimal rate allocation mechanism. For this suboptimal scheme, we develop a queuing analytic model to measure and compare different data-link layer performance parameters e.g., packet loss probability and queuing delay distribution. The developed analytic model for the suboptimal scheme is further useful for a call admission decision. We compare the performances of the suboptimal rate allocation mechanism with its optimal counterpart. Presented simulation results show that the suboptimal mechanism has a quite similar performance as the optimal mechanism.

In this paper, we study rate allocation mechanisms that allocate available transmission rate of a particular cognitive radio (CR) user among its different class of services. In particular, we formulate the rate allocation mechanism of a CR user between its two different class of services namely, delay sensitive (DS) and best effort (BE) services as a Markov decision process. Then the optimal rate allocation mechanism that minimizes the average queuing delay of DS service while guaranteeing the packet loss probabilities of both class of services is obtained using a linear programming technique. Since the optimal rate allocation mechanism can be complex to implement in practice, we study a low-complexity suboptimal rate allocation mechanism. For this suboptimal scheme, we develop a queuing analytic model in order to measure different quality parameters. Selected numerical results show that the performance of suboptimal rate allocation mechanism is quite similar to the optimal rate allocation mechanism for the considered system parameters. The developed queuing analytic model is also useful for call admission controller design when the suboptimal scheme is employed.

In a cooperative cognitive radio (CR) network when a group of CR users acts as relays for a given CR user, a cooperative beamforming can be used to improve the quality of communications. However, this cooperative beamforming can introduce asynchronous interference at the primary receiver due to different propagation delays between different CR relays and the primary receiver. In this paper, we propose an innovative cooperative beamforming method that maximizes the received signal power at the secondary destination while keeping the asynchronous interference at the primary receiver below a target threshold. The presented numerical results show that the proposed beamforming method can significantly reduce the interference at the primary receiver and thereby decreases the outage probability up to 75% compared to the zero forcing beamforming method, for a primary user's busy probability of 0.75. This beamforming method is further extended for the case when the channels between the primary receiver and the CR relays are not known perfectly.

An adaptive subcarrier intensity modulation employing rectangular quadrature amplitude modulation is investigated for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive scheme is implemented by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Highly accurate series approximations for the achievable spectral efficiency, average bit-error rate, and outage probability are derived using a series expansion of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are also presented. Our asymptotic analysis reveals that the diversity order of the considered system depends only on the smaller channel parameter of the Gamma-Gamma turbulence.

Recently, it has been shown that the performance of bit-interleaved coded modulation (BICM) using convolutional codes in nonfading channels can be significantly improved if the coded bits are not interleaved at all. This particular BICM design is referred to as BICM trivial (BICM-T) and is shown to be asymptotically as good as Ungerboeck's one dimensional (1D) trellis coded modulation (TCM). This BICM-T design and analysis considered a simple case of rate 1/2 channel encoder with equally spaced 16-ary quadrature amplitude modulation (QAM) constellation where the code rate matches with the modulation order as required in TCM transmission. In this paper, we consider and analyze a new BICM-T design that uses a non equally spaced signal constellation in conjunction with a bit level multiplexer. With this design and analysis, one can not only exploit the full benefit of BICM-T design by jointly optimizing different transceiver's modules but also enjoys the same design flexibility as the traditional BICM to independently choose the code rate and the modulation order. The presented numerical results for 64-ary QAM with rate 1/3 code shows that the considered design can offer gains up to 2.5 dB over the traditional optimal BICM design for a target bit error rate (BER) of 10−6.

We investigate a variable-rate, constant-power adaptive subcarrier intensity modulation employing M-ary phase shift keying and rectangular quadrature amplitude modulation for optical wireless communication over the Gamma-Gamma turbulence channels. The adaptive schemes offer efficient utilization of optical wireless communication channel capacity by adapting the modulation order according to the received signal-to-noise ratio and a pre-defined target bit-error rate requirement. Novel closed-form series solutions are presented for the achievable spectral efficiency, average bit-error rate, and outage probability using a series expansion approach of the modified Bessel function. In addition, asymptotic bit-error rate and outage probability analyses are presented. Our asymptotic bit-error rate analysis shows that the diversity order of both non-adaptive and adaptive systems depends only on the smaller channel parameter of the Gamma-Gamma turbulence. Numerical results demonstrate high accuracy of our series solutions with finite number of terms and improved spectral efficiency achieved by the adaptive systems without increasing the transmitter power or sacrificing bit-error rate requirements.

In a cognitive radio (CR) network when a group of CR users acts as relays for a given CR user, a cooperative beamforming can be used to improve the quality of communications. However, this cooperative beamforming can introduce asynchronous interference at the primary receiver due to different propagation delays between different CR relays and the primary receiver. In this paper, we propose an innovative beamforming method that maximizes the received signal power at the secondary destination while keeping the asynchronous interference at the primary receiver below a target threshold. The presented numerical results show that the proposed beamforming method can significantly reduce the interference at the primary receiver compared to the zero forcing beamforming as well as joint leakage suppression method and thereby decreases the outage probability. This beamforming method is further extended for the case when the channels between the primary receiver and the CR relays are not known perfectly. Moreover, in this paper, we propose and investigate two relay selection strategies in conjunction with cooperative beamforming. The presented numerical results show that the relay selection schemes in conjunction with the cooperative beamforming method can further improve the received signal power at the secondary destination.

In this paper, we propose a new BICM design which considers hierarchical (nonequally spaced) constellations, a bit-level multiplexer, and multiple interleavers. It is shown that this new scheme outperform previous BICM designs because it exploits the temporal structure of the coded sequence. An analytical bound on the bit error rate (BER) of the proposed BICM scheme in terms of the constellation parameters and the multiplexing is developed for Nakagami-m fading channels. This bound is used to design a BICM transceiver with improved BER performance. Numerical results show that the gains compared to conventional BICM designs depend on the fading parameter and reach 2 dB for a target BER of 10-7and m=5.

Bit-interleaved coded modulation (BICM) transceivers often use equally spaced constellations and a random interleaver. In this paper, we propose a new BICM design, which considers hierarchical (nonequally spaced) constellations, a bit-level multiplexer, and multiple interleavers. It is shown that this new scheme increases the degrees of freedom that can be exploited in order to improve its performance. Analytical bounds on the bit error rate (BER) of the system in terms of the constellation parameters and the multiplexing rules are developed for the additive white Gaussian Noise (AWGN) and Nakagami-m fading channels. These bounds are then used to design the BICM transceiver. Numerical results show that, compared to conventional BICM designs, and for a target BER of 10^{-6}, gains up to 3 dB in the AWGN channel are obtained. For fading channels, the gains depend on the fading parameter, and reach 2 dB for a target BER of 10^{-7} and m=5.

In this letter, we develop an optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feedback from the PU receivers. A suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed optimal power allocation algorithm CR user can achieve significantly higher transmission capacity for given statistical interference constraints and a given power budget compared to the classical power allocation algorithms namely, uniform and water-filling power allocation algorithms. The suboptimal algorithm outperforms both water-filling algorithm and uniform power loading algorithm. The proposed suboptimal algorithm give an option of using a low complexity power allocation algorithm where complexity is an issue with a certain amount of transmission rate degradation.

In this paper we study bit-interleaved coded modulation (BICM) transmission using non-uniform (NU) quadrature amplitude modulation (QAM) constellations. For such a NUQAM-BICM transmission, we develop closed-form approximations for the probability density function of the L-values, and we use them to predict the coded bit error rate (BER)performance of the system in the AWGN channel. We then numerically optimize NU-QAM-BICM based on convolutional codes. Compared to uniform QAM-based BICM transmission, and for a target BER of 10-7, we reach gains up to 1 dB. When the design is applied to turbo codes a decrease in the error floor can be obtained.

Our contribution, in this paper, is two-fold. First, we analyze the performance of a hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) scheme, which was proposed by the authors, in a fading environment where different users have different average link gains. Specifically, we present a new expression for the spectral efficiency (SE) of the users and using this expression, we compare the degrees of fairness (DOF) of the TBS scheme with that of classical single user opportunistic scheduling schemes, namely, absolute carrier-to-noise ratio (CNR) based single-best user scheduling (SBS) and normalized CNR based proportional fair scheduling (PFS) schemes. The second contribution is that we propose a new hybrid two-user opportunistic scheduling (HTS) scheme based on our earlier proposed TBS scheme. This HTS scheme selects the first user based on the largest absolute CNR value among all the users while the second user is selected based on the ratios of the absolute CNRs to the corresponding average CNRs of the remaining users. The total transmission rate i.e., the constellation size is selected according to the absolute CNR of the first best user. The total transmission rate is then allocated among these selected users by joint consideration of their absolute CNRs and allocated number of information bit(s) are transmitted to them using hierarchical modulations. Numerical results are presented for a fading environment where different users experience independent but non-identical (i.n.d.) channel fading. These selected numerical results show that the proposed HTS scheme can considerably increase the system?s fairness without any degradation of the link spectral efficiency (LSE) i.e., the multiuser diversity gain compared to the classical SBS scheme. These results also show that the proposed HTS scheme has a lower fairness in comparison to the PFS scheme which suffers from a considerable degradation in LSE.

In our earlier works, we proposed rate adaptive hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) and hybrid two-user scheduling (HTS) schemes. The proposed schemes are innovative in the sense that they include a second user in the transmission opportunistically using hierarchical modulations. As such the frequency of information access of the users increases without any degradation of the system spectral efficiency (SSE) compared to the classical opportunistic scheduling scheme. In this paper, we analyze channel access delay of an incoming packet at the base station (BS) buffer when our proposed TBS and HTS schemes are employed at the BS. Specifically, using a queuing analytic model we derive channel access delay as well as buffer distribution of the packets that wait at BS buffer for down-link (DL) transmission. We compare performance of the TBS and HTS schemes with that of the classical single user opportunistic schemes namely, absolute carrier-to-noise ratio (CNR)-based single user scheduling (ASS) and normalized CNR-based single user scheduling (NSS). For an independent and identically distributed (i.i.d.) fading environment, our proposed scheme can improve packet's access delay performance compared to the ASS. Selected numerical results in an independent but non-identically distributed (i.n.d.) fading environment show that our proposed HTS achieves overall good channel access delay performance.

We consider, in this paper, channel allocation and rate adaptation scheme for relayed transmission over correlated fading channels via cross-layer design. Specifically, jointly considering the data link layer buffer occupancy and channel quality at both the source and relay nodes, we develop an optimal channel allocation and rate adaptation policy for a dual-hop relayed transmission. As such the overall transmit power for the relayed system is minimized while a target packet dropping rate (PDR) due to buffer over flows is guaranteed. In order to find such an optimal policy, the channel allocation and rate adaptation transmission framework is formulated as a constraint Markov decision process (CMDP). The PDR performance of the optimal policy is compared with that of two conventional suboptimal schemes, namely the channel quality based and the buffer occupancy based channel allocation schemes. Numerical results show that for a given power budget, the optimal scheme requires significantly less power than the conventional schemes in order to maintain a target PDR.

The contribution of this letter is the computation of exact symbol error probability (SEP) of three-dimensional (3- D) signal constellations over an additive white Gaussian noise (AWGN) channel. The originality of the proposed method is that it can be applied to any arbitrary 3-D constellation whose decision regions may not meet at right angles. We express the SEP in a triple integral form which is further simplified. The simplified form requires a single integral evaluation of standard "erf" function. Using the derived exact SEP formula, we plot SEP for a number of selected 3-D constellations. The SEP obtained using the proposed formula is validated by simulation results. It is also compared with the union bound approximation, an upper bound for SEP.

Our contribution, in this paper, is two-fold. First, we analyze performance of a hierarchical modulation-assisted two-best user opportunistic scheduling (TBS) scheme, which was proposed by the authors in [1], in a fading environment where different users have different average link gains. Specifically, we present expression for the spectral efficiency (SE) of the users and using this expression, we compare degrees of fairness (DOF) of the TBS with that of classical single user opportunistic scheduling schemes. Second contribution is that we propose a new hybrid two-user opportunistic scheduling (HTS) scheme based on the TBS scheme. This HTS selects the first user based on the largest absolute carrier-to-noise (CNR) value among all the users while the second user is selected according to the ratios of the absolute CNRs to the corresponding average CNRs of the remaining users. Numerical results are presented for a fading environment where different users experience independent but non-identical (i.n.d) channel fading. These selected numerical results show that the proposed HTS scheme can considerably increase the system's fairness without any degradation of the link SE compared to the classical single best user scheduling. These results also show that the proposed HTS has a lower fairness in comparison to the proportional fair scheduling (PFS) which suffers from a considerable degradation in link SE.

In our earlier works, we proposed rate adaptive modulation assisted two-user opportunistic scheduling schemes. The proposed schemes are innovative in the sense that they include second user in a transmission opportunistically using rate adaptive hierarchical modulations. As such the frequency of information access of the users increases without any degradation of the system link spectral efficiency (SE) compared to the classical opportunistic scheduling. In this paper, we analyze delay performance of our earlier proposed schemes. Specifically, using a queuing analytic model we derive queuing delay as well as buffer distributions of the packets that wait at the base station (BS) buffer for downlink (DL) transmission. Using this model, we compare performance of the two-user opportunistic scheduling schemes with that of the classical single user opportunistic schemes. These single user opportunistic scheduling schemes are the absolute carrier-to-noise ratio (CNR) based scheduling and normalized CNR based scheduling which is equivalent to the proportional fair scheduling (PFS). For an interdependent and identically distributed (i.i.d.) fading environment, our proposed scheme can improve delay performance significantly. Selected numerical results in an independent but non-identically distributed (i.n.d.) fading environment, our proposed scheme achieves overall good delay performance.

In this paper, we investigate an optimal power loading algorithm for an OFDM-based cognitive radio (CR) system. The downlink transmission capacity of the CR user is thereby maximized, while the interference introduced to the primary user (PU) remains within a tolerable range. We also propose two suboptimal loading algorithms that are less complex. We also study the effect of a subcarrier nulling mechanism on the performance of the different algorithms under consideration. The performance of the optimal and suboptimal schemes is compared with the performance of the classical power loading algorithms, e.g., water-filling and uniform power but variable rate loading schemes that are used for conventional OFDMbased systems. Presented numerical results show that for a given interference threshold, the proposed optimal scheme allows CR base station (BS) to transmit more power in order to achieve a higher transmission rate than the classical loading algorithms. These results also show that although the proposed suboptimal schemes have certain degradation in performance compared to the optimal scheme, they outperform the classical loading algorithms. We also present numerical results for nulling mechanism. Finally, we investigate the effect of imperfect channel gain information at the transmitter.

Cognitive radio (CR) technology is an innovative radio design philosophy in order to increase the spectrum utilization by exploiting the unused spectrum in dynamically changing environments. Specifically, the CR technology will allow a group of potential users (referred to as secondary users) to access available spectrum resources unused by primary users (PUs) for whom the band has been licensed. The spectrum available for secondary users (SUs) depends on PUs' activity in the licensed spectrum. In this paper, we develop a queuing analytic framework to study important performance measures experienced by SUs in a CR network. We study queuing delay and buffer statistics of SUs' packets by modeling PUs' activity as a two state Markov chain and SUs' channel quality variation as a finite state Markov chain (FSMC). In order to allocate available channels among the SUs, an opportunistic channel allocation scheme is considered. The proposed framework facilitates to design an admission controller for SUs' network in order to maintain a given quality service (QoS) requirement which is specified in the form of statistical delay guarantee.

Cognitive radio (CR) technology is an innovative radio design philosophy which aims to increase spectrum utilization by exploiting unused spectrum in dynamically changing environments. Orthogonal frequency division multiplexing (OFDM) is a potential modulation technique for CR networks' air interface. In this paper, we study and explore optimal power loading algorithm for an OFDM-based CR system and the rate in each subcarrier is adjusted according to the power. As such the downlink capacity of the CR user is maximized while the interference introduced to the primary user remains within a tolerable range. We also propose two suboptimal loading algorithms that have less complexity. The performance of optimal and suboptimal schemes are compared with the performance of classical power loading algorithms that are used for conventional OFDM-based systems e.g., water-filling and uniform power but variable rate loading schemes. Presented numerical results show that for a given interference threshold the proposed optimal scheme allows the CR users to transmit more power in order to achieve higher transmission rate than the classical loading algorithms. These results also show that the proposed suboptimal schemes offer a performance close to the optimal scheme. Finally, we study the effect of subcarrier nulling mechanism on the performance of different algorithms under consideration.

In this letter, we study an application of hierarchical constellations (known also as embedded, multi-resolution, or asymmetrical constellations) for multi-user opportunistic scheduling. The key idea is to rely on hierarchical constellations to transmit information to two or more best users simultaneously in each transmission. The transmit power as well as the constellation parameter is changed according to the link qualities of the selected users in a way that a given target bit error rate (BER) is satisfied. The expressions for the average transmit power, and the outage probability with truncated channel inversion power control are presented. We also analyze and compare buffer distribution, average buffer occupancy, and packet loss probability of different; schemes via queuing analysis. We finally compare the hierarchical scheme for multi-user scheduling with a uniform constellation-based time-slotted scheme

We propose and study a two-best user opportunistic scheduling scheme using fixed power discrete rate adaptive hierarchical constellations (known also as embedded, multi- resolution, or asymmetrical constellations). According to the classical opportunistic scheduling, the scheduler transmits information to the selected first best user in each transmission slot in order to maximize the spectral efficiency. Our newly proposed scheme transmits information not only to the first best user but also to the second best user if the channel qualities of the selected two users support. As such the frequency of channel access of the users is increased without any degradation of overall spectral efficiency compared to the classical single best user scheduling. The key idea is to rely on hierarchical constellations to transmit information to two best users simultaneously. Specifically, according to the channel quality of the first best user, the alphabet size of the square quadrature amplitude modulation (QAM) is selected in order to maximize the spectral efficiency. However, if the first best user's channel quality is good enough to decode the information transmitted in the second hierarchy of the selected size hierarchical QAM constellation while the second best user's channel quality is capable of decoding the information transmitted in the first hierarchy, information is transmitted to both the selected first and second best users using the hierarchical QAM constellation. Otherwise, information is transmitted only to the first best user using the selected size uniform QAM constellation. We present expressions for the information transmission probability and the overall average spectral efficiency [bits/sec/Hz] defined as the overall average transmitted data rate per unit bandwidth for a specified number of users, a specified average carrier-to-noise ratio (CNR), and bit error rate (BER). Numerical results for a fading environment where the channel variation among the users is identically and independently distributed (i.i.d.) are presented. These results show that, in such i.i.d. fading environment, our newly proposed fixed power discrete rate adaptive hierarchical modulation- assisted two-best user scheduling provides more frequent access to the information (almost double) without any average spectral efficiency degradation compared to the classical single best user opportunistic scheduling.

In their earlier work, the authors proposed a multiple best user opportunistic scheduling using power controlled hierarchical constellations and showed that the proposed scheme offers more frequent information access (almost double) to the users at the expense of certain increase in the average transmit power. As follow-up of our earlier work, in this paper, we analyze higher layer performances of the proposed two best user scheduling scheme via queuing analysis. More specifically, we analyze the buffer distribution, the average buffer occupancy, the delay distribution, and the packet loss probability of the hierarchical constellation-based multiple best user scheduling which has been proposed earlier by the authors. We then compare these higher layer performances of the hierarchical two best user scheduling with the classical single best user scheduling. Selected numerical results show that the hierarchical two best user scheduling scheme outperforms the classical single best user scheduling in terms of buffer distribution, average buffer occupancy (i.e., average buffering delay), delay distribution, and packet loss probability of the packets at the transmission buffer at the expenses of an increase in the average transmit power.

We study in this paper an integrated packet scheduling and unequal error protection strategy for the transmission of packetized multi-resolution (layered) image/video data over time varying wireless channels. Different transmission priorities are given to different layers of video/image data not only by using hierarchical constellation (known also as nonuniform, asymmetric, multi-resolution constellation) at the physical layer but also by a buffer adaptive packet scheduling protocol at the medium access control (MAC) layer. Basically, according to the current buffer occupancy and channel state, the scheme dynamically selects packets from different resolution levels to transmit for the current transmission. The bits from the selected packets are assigned to different hierarchies of a hierarchical 4/16-quadrature amplitude modulation (QAM). In our proposed transmission framework, the transmit power level and the constellation priority parameter are also selected dynamically according to the quality of the wireless channel and scheduling state. In order to minimize the distortion in the received image/video with a given power budget, a joint optimization of the packet scheduling protocol, the transmit power level, and the constellation priority parameter is proposed. The power-distortion trade-offs curve is plotted for a given fading rate. We also compare the proposed scheme with a scheme employing uniform signal constellation. The comparison shows that in order to achieve a given quality of the received image/video, the scheme employing hierarchical QAM constellation always requires less power than the scheme employing uniform constellation.

We propose a new technique for multi-resolution video/image data transmission over block fading channels. The proposed scheme uses an adaptive scheduling protocol employing a retransmission strategy in conjunction with a hierarchical signal constellation (known also as nonuniform, asymmetric, multi-resolution constellation) to give different transmission priorities to different resolution levels. Transmission priorities are given in terms of average packet loss rate as well as average throughput. Basically, according to the transmission scheduling and channel state (acknowledgment signal) of the previous transmission, it dynamically selects packets from different resolution levels to transmit for the current transmission. The bits from the selected packets are assigned to different hierarchies of a hierarchical 4/16-quadrature amplitude modulation to transmit them with different error protections. The selection of packets for transmission and the assignment of these selected packets to different hierarchies of the hierarchical constellation are referred to as the scheduling protocol in our proposed scheme. We model this protocol by a finite state first order Markov chain and obtain the packet loss rate and the packet transmission rate over Nakagami-m block fading channel in closed-form. Some selected numerical results show that the proposed scheme can control the relative packet loss rate and the packet transmission rate of different resolution levels by varying the priority parameter (or equivalently, the asymmetry) of the hierarchical constellation and the maximum number of allowed retransmissions.

We propose and study a two best user opportunistic scheduling algorithm using discrete rate adaptive hierarchical constellations (known also as embedded, multi-resolution, or asymmetrical constellations). The key idea is to rely on hierarchical constellations to transmit information to the two best users opportunistically. The newly proposed scheme transmits information to single or two best users jointly considering the channel qualities of these users. We present expressions for the information transmission probability and the overall average spectral efficiency [Bits/Sec/Hz] defined as the overall average transmitted data rate per unit bandwidth for a specified number of users, average carrier-to-noise ratio (CNR), and bit error rate (BER). Numerical results for an identical and independent (i.i.d.) fading environment, where the channel variation among the users is i.i.d., are presented. The results show that in such i.i.d. fading environment, our newly proposed discrete rate adaptive hierarchical modulation assisted two best user scheduling provides more frequent access to the information (almost double) without any average spectral efficiency degradation compared to the classical single best user opportunistic scheduling.

This paper explores optimal and suboptimal power and bit loading algorithms for a multicarrier system. Specifically, we study the trade-offs between the total transmit power of an orthogonal frequency division multiplexing (OFDM) system and the buffering delay of the packets in a transmission buffer. The loading framework is formulated as a Markov decision process (MDP) and an optimal loading policy which minimizes the transmit power while meeting a target delay constraint is obtained via equivalent linear programming (LP) methodology. The complexity of finding the optimal loading policy and its' implementation issues are described. Since finding the optimal policies becomes complex and practically un-realizable for large number of carriers in the system, we offer a sub-optimal power and bit loading algorithm using the results of the single carrier system's power and rate adaptation policy and a greedy approach. Selected numerical examples show that the sub-optimal algorithm, which has reduced complexity, has performance close to the optimal one.

We propose a new technique for multi-resolution video/image data transmission over block fading channels. The proposed scheme uses an adaptive scheduling protocol employing a retransmission strategy in conjunction with a hierarchical signal constellation (known also as nonuniform, asymmetric, multi-resolution constellation) to give different transmission priorities to different resolution levels. Transmission priorities are given in terms of packet loss rate as well as average throughput i.e., transmission rate. Basically, according to the transmission scheduling and channel state (acknowledgment signal) of the previous transmission, it dynamically selects packets from different resolution levels to transmit for the current transmission. The bits from the selected packets are assigned to different hierarchies of a hierarchical 4/16- quadrature amplitude modulation to transmit them with different error protections. The selection of packets for transmission and the assignment of these selected packets to different hierarchies of the hierarchical constellation are referred to as the scheduling protocol in our proposed scheme. We model this protocol by a finite state first order Markov chain and obtain the packet loss rate and the packet transmission rate over Nakagami-m block fading channel in closed-form. Some selected numerical results show that the proposed scheme can control the relative packet loss rate and the packet transmission rate of different resolution levels by varying the priority parameter (or equivalently the asymmetry) of the hierarchical constellation and the maximum number of allowed retransmissions.