In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0.4 ms.

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by proactively encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which removes the commonly assumed Slater’s condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. Furthermore, we consider a variant of ODOTS with multi-step local gradient descent updates, termed ODOTS-MLU, and show that it provides improved performance bounds. As an example application, we apply both ODOTS and ODOTS-MLU to enable communication-efficient federated learning. Our experimental results based on canonical image classification demonstrate that ODOTS and ODOTS-MLU obtain higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions.

We consider online distributed optimization where a server and multiple devices collaborate to minimize a sequence of time-varying global loss functions. To accommodate slow devices that may require multiple time slots to compute their local decisions, the server uses semi-asynchronous aggregation of the local decisions, which complicates device scheduling and performance optimization. In this work, we first analyze the convergence of semi-asynchronous aggregation in the presence of time-varying local update delays and loss-function weights. Our analysis leads to an online scheduling problem to minimize the accumulated age of information on the local decision updates, subject to individual long-term constraints on the total weights of the scheduled devices. We then design an efficient scheduling policy, termed Age-of-Information Minimization with Weight Limits (AIMWeL), through a modified Lyapunov optimization approach that uses the weighted sum of linear age-of-information values and quadratic virtual queues as a new Lyapunov function. We show that AIMWeL has bounded optimality ratio, via a novel double relaxation approach to handle the unique scheduling-dependent communication indicator with time-varying probabilities of completing local decision update caused by semi-asynchronous aggregation. When AIMWeL is applied to semi-asynchronous federated learning, our simulation results based on standard image classification datasets demonstrate that AIMWeL uses significantly less time to reach the same classification accuracy achieved by the current best alternatives for both convex logistic regression and non-convex convolutional neural networks.

We consider wireless network virtualization (WNV) in an uplink multiple-input multiple-output system, where multiple service providers (SPs) operate in virtually isolated networks managed by an infrastructure provider (InP) that owns the communication equipment. Service isolation is achieved at the physical layer by exploiting a large number of antennas at the base stations. We formulate this WNV as a non-convex optimization problem for the InP, jointly considering the uplink receive beamforming at the BS and the transmit power of the SPs' subscribing user devices. We decompose the problem into two subproblems and derive closed-form solutions to both. We then adopt an alternating optimization approach to combine the closed-form solutions to solve the original problem. Our simulation results show that the proposed method provides strong service isolation among the SPs while retaining efficiency similar to or better than centralized beamforming without virtualization, and it substantially outperforms traditional WNV with strict resource separation.

In modern applications, the underlying computation and communication networks are often hierarchical, which is typified by the three-layer client-edge-cloud system that has become prominent in recent times. We study minimax fairness in distributed optimization over such systems, to provide robust performance guarantee for the worst-case mixture of loss functions. We propose HierMinimax, a communication efficient distributed algorithm to solve the minimax optimization problem. We provide convergence analysis for both convex and non-convex loss functions, leading to performance bounds that enable tuning the tradeoff between the communication complexity and the optimization convergence rate. Our experiments on classification problems with canonical datasets show that HierMinimax substantially improves the fairness in learning accuracy and reduces the communication overhead compared with the current best alternatives.

In this paper, we explore the integration of a reconfigurable intelligent surface (RIS) with a multi-antenna base station (BS) for downlink multi-user multiple-input-single-output (MU-MISO) systems. We aim to enhance energy efficiency (EE) by jointly optimizing beamforming and phase shifts at the BS and RIS, respectively, while ensuring each mobile user meets their link budget requirements. The resulting optimization problem is inherently non-convex. To address this challenge, we employ proximal policy optimization (PPO), known for efficiently managing non-convex problems and reducing training overhead in continuous action spaces through a clip factor. Furthermore, by leveraging deep neural networks (DNN), the proposed PPO-based solution provides the optimum values for the beamforming at the BS and the phase shift at the RIS, respectively. Finally, we demonstrate the effectiveness and accuracy of the proposed PPO-based algorithm through an extensive simulation campaign, comparing its performance against baseline methods (i.e., fractional programming (FP) and deep deterministic policy gradient (DDPG)). The results show that our proposed PPO-based algorithm outperforms the considered baseline approaches (i.e., FP and DDPG) in terms of EE by 34.2% and 15.8%, respectively.

Internet-of-Things (IoT) networks provide massive connectivity for many application scenarios. Recently, much work has been dedicated to develop spectrum access strategies for IoT networks with a massive number of nodes and sporadic data traffic behavior. The case becomes more challenging in critical applications when Ultra-Reliable Low-Latency (URLL) transmissions are required. Such networks entail spectrum-efficient transmission schemes in which Non-Orthogonal Multiple-Access (NOMA) is considered a key enabler. We proposed a Distributed Queuing (DQ) approach in NOMA for critical massive IoT (mIoT) applications. More specifically, we introduce a frame structure to support DQ-based NOMA so that dynamic NOMA clustering (at the nodes) and dynamic Successive Interference Cancellation (SIC) ordering at the Base Station (BS) are supported. We also use adaptive power back-off strategy to reduce power collisions by utilizing both nodes’ and clusters’ activation index. We investigate network performance metrics, such as reliability, delay violation probability, and effective sum rate. These metrics are derived analytically, and the effect of different network parameters such as blocklength, active node arrival rate, and the number of contention subslots on the network metrics are investigated and compared with the S-ALOHA-TD benchmark.

We consider federated learning in a wireless edge network, where multiple power-limited mobile devices collaboratively train a global model, using their local data with the assistance of an edge server. Exploiting over-the-air computation, the edge server updates the global model via analog aggregation of the local models over noisy wireless fading channels. Unlike existing works that separately optimize computation and communication at each step of the learning algorithm, in this work, we jointly optimize the training of the global model and the analog aggregation of the local models over time. Our objective is to minimize the accumulated training loss at the edge server, subject to individual long-term transmit power constraints at the mobile devices. We propose an efficient algorithm, termed Online Model Updating with Analog Aggregation (OMUAA), to adaptively update the local and global models based on the time-varying communication environment. The trained model of OMUAA is channel-and power-aware, and it is in closed form incurring low computational complexity. We study the mutual impact between model training and analog aggregation over time, to derive performance bounds on the computation and communication performance metrics. Furthermore, we consider a variant of OMUAA with double regularization on both the local and global models, termed OMUAA-DR, and show that it can significantly reduce the convergence time to reach long-term transmit power constraints. In addition, we extend both OMUAA and OMUAA-DR to enable analog gradient aggregation, while preserving their performance bounds. Simulation results based on real-world image classification datasets and typical wireless network settings demonstrate substantial performance gain of OMUAA and OMUAA-DR over the known best alternatives.

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading and the action space representing the association decisions of the user with the access points (APs). We develop a novel algorithm that employs this model to derive a HO policy for a mobile user based on current and future rewards. To alleviate the high complexity of our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved separately. Then, the policy and the candidate pool of APs for the sub-problem that produced the best total expected reward are used to perform HOs within a specific time horizon. We then introduce modifications to our algorithm to decrease the number of HOs. The results show that half of the number of HOs in the UC-mMIMO networks can be eliminated. Namely, our novel solution can control the number of HOs while maintaining a rate guarantee, where a 47%-70% reduction of the cumulative number of HOs is observed in networks with a density of 125 APs per km2. Most importantly, our results show that a POMDP-based HO scheme is promising to control HOs.

We consider a bidirectional communication between two single-antenna transceivers using multiple multi-antenna access points (APs) in a cell-free network architecture. In such a network, because of different propagation delays associated with different APs, the end-to-end link is a multi-path channel that results in inter-symbol-interference (ISI) in the signals received at the transceivers. To tackle ISI, we resort to cyclic prefix (CP) assisted block transmission of the information symbols and employ joint pre- and post-channel equalizers at both the transceivers to mitigate the impact of intra-block interference. Considering the amplify-and-forward technique at the APs, we cast the joint design of equalizers, beamforming matrices, and transceivers’ transmit powers as a power minimization problem while guaranteeing predefined data rates at the transceivers. Assuming symmetric beamforming matrices at the APs, we devise a semi-closed-form solution for this problem. We prove rigorously that at the optimum only a synchronous subset of the APs should participate in the information exchange between the two transceivers. This is achieved by proving that at the optimum, the pre-equalizer matrices should be unitary and the post-equalizer matrices should be invertible.

We consider cooperative multiple-input multiple-output (MIMO) precoding design with multiple access points (APs) assisted by a central controller (CC) in a fading environment. Even though each AP may have its own local channel state information (CSI), due to the communication delay in the backhaul, neither the APs nor the CC has timely global CSI. Under this hierarchical semi-online setting, our goal is to minimize the accumulated precoding deviation, between the actual local precoders executed by the APs and an ideal cooperative precoder based on timely and perfect global CSI, subject to per-AP transmit power limits. We propose an efficient algorithm, termed Semi-Online Precoding with Information Parsing (SOPIP), which accounts for the network heterogeneity in information timeliness and computational capacity. SOPIP does not require the CC to send the full global CSI to each AP. Instead, it takes advantage of the precoder structure to substantially lower the communication overhead, while allowing each AP to effectively combine its own timely local CSI with the delayed global CSI to enable adaptive precoder updates. We analyze the performance of SOPIP in the presence of multi-slot communication delay, CSI inaccuracy, and gradient estimation error, showing that it has a bounded performance gap from an offline optimal solution. Simulation results under typical cellular system settings further demonstrate the substantial performance gain of SOPIP over other centralized and distributed schemes.

We consider online distributed optimization in a networked system, where multiple devices assisted by a server collaboratively minimize the accumulation of a sequence of global loss functions that can vary over time. To reduce the amount of communication, the devices send quantized and compressed local decisions to the server, resulting in noisy global decisions. Therefore, there exists a tradeoff between the optimization performance and the communication overhead. Existing works separately optimize computation and communication. In contrast, we jointly consider computation and communication over time, by encouraging temporal similarity in the decision sequence to control the communication overhead. We propose an efficient algorithm, termed Online Distributed Optimization with Temporal Similarity (ODOTS), where the local decisions are both computation- and communication-aware. Furthermore, ODOTS uses a novel tunable virtual queue, which completely removes the commonly assumed Slater’s condition through a modified Lyapunov drift analysis. ODOTS delivers provable performance bounds on both the optimization objective and constraint violation. As an example application, we apply ODOTS to enable communication-efficient federated learning. Our experimental results based on real-world image classification demonstrate that ODOTS obtains higher classification accuracy and lower communication overhead compared with the current best alternatives for both convex and non-convex loss functions.

Despite the many known benefits of Federated Learning (FL), in the wireless environment, its performance is significantly impacted by the statistical and system heterogeneities among the local data sets and local clients. Therefore, judicious sampling of clients and resource allocation among them are of vital importance in FL. In this work, we consider the online joint optimization of probabilistic client sampling and power allocation to improve the training performance of wireless FL. Our optimization is based on a new convergence bound for non-convex loss functions under probabilistic client sampling, which considers the different data ratios and gradient norms among clients. We propose a new algorithm based on the Lyapunov optimization framework, termed PCSPA, that accounts for how the statistical and system heterogeneities affect both the convergence rate and training time of FL, as well as the long-term power constraints and the expected number of sampled clients. Experiments on image classification with wireless FL show that the proposed algorithm can substantially outperform conventional separate optimization strategies and a state-of-the-art joint optimization method.

We investigate the use of reconfigurable intelligent surfaces (RISs) in wireless networks to maximize the sum secrecy rate (i.e., the sum maximum rate that can be communicated under perfect secrecy). Specifically, we focus on a network that utilizes RIS-assisted unmanned aerial vehicles (UAVs) under imperfect channel state information (CSI). Our objective is to maximize the sum secrecy rate while dealing with the presence of multiple eavesdroppers. To achieve this, we jointly optimize the active (UAV) and passive (RIS) beamforming together with the UAV's trajectories. The formulated problem is non-convex due to the coupling of CSI with the maneuverability of the UAV. To overcome this challenge, we propose a policy-based deep reinforcement learning (DRL) approach that solves the non-convex optimization problem in a centralized fashion. Finally, simulation results show that our proposed approach significantly improves average sum secrecy rates over conventional approaches.

In many dynamic systems, decisions on system operation are updated over time, and the decision maker requires an online learning approach to optimize its strategy in response to the changing environment. When the loss and constraint functions are convex, this belongs to the general family of online convex optimization (OCO). In existing OCO works, the environment is assumed to vary in a time-slotted fashion, while the decisions are updated at each time slot. However, many wireless communication systems permit only periodic decision updates,i.e.each decision is fixed over multiple time slots, while the environment changes between the decision epochs. The standard OCO model is inadequate for these systems. Therefore, in this work, we consider periodic decision updates for OCO. We aim to minimize the accumulation of time-varying convex loss functions, subject to both short-term and long-term constraints. Feedback information about the loss functions within the current update period may be delayed and incomplete. We propose an efficient algorithm, termed Periodic Queueing and Gradient Aggregation (PQGA), which employs novel periodic queues together with possibly multi-step aggregated gradient descent to update the decisions over time. We derive upper bounds on the dynamic regret, static regret, and constraint violation of PQGA. As an example application, we study the performance of PQGA for network virtualization in a large-scale multi-antenna system shared by multiple wireless service providers. Simulation results show that PQGA converges fast and substantially outperforms the current best alternative.

We consider online convex optimization (OCO) with multi-slot feedback delay. An agent selects a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that may be time-varying. Both the convex loss function and the long-term constraint function may experience multiple time slots of feedback delay to be received by the agent. Existing works on OCO under this general setting has focused on the static regret, which measures the gap of losses between an online decision sequence and a time-invariant static offline benchmark. In this work, besides the static regret, we also consider a more practically meaningful metric, the dynamic regret, where the benchmark is a time-varying online optimal decision sequence. We propose an efficient algorithm, termed Delay-Tolerant Constrained-OCO (DTC-OCO), which uses a novel double regularization together with a new penalty mechanism on the long-term constraint violation, to tackle the asynchrony between information feedback and decision updates. We obtain upper bounds for its static regret, dynamic regret, and constraint violation, proving that they are sublinear under mild conditions. Furthermore, we consider a variation of DTC-OCO with multi-step gradient descent, and show it provides improved dynamic regret and constraint violation bounds for strongly convex loss functions. For numerical demonstration, we apply DTC-OCO to a general network resource allocation problem. Our simulation results suggest substantial performance gain by DTC-OCO over the current best alternative.

We propose to control handoffs (HOs) in user- centric cell-free massive MIM 0 networks through a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading (LSF) and the action space representing the association decisions of the user with the access points. Our proposed formulation accounts for the temporal evolution and the partial observability of the channel states. This allows us to consider future rewards when performing HO decisions, and hence obtain a robust HO policy. To alleviate the high complexity of solving our POMDP, we follow a divide-and-conquer approach by breaking down the POMDP formulation into sub-problems, each solved individually. Then, the policy and the candidate cluster of access points for the best solved sub-problem is used to perform HOs within a specific time horizon. We control the number of HOs by determining when to use the HO policy. Our simulation results show that our proposed solution reduces HOs by 47% compared to time- triggered LSF-based HOs and by 70% compared to data rate threshold-triggered LSF-based HOs. This amount can be further reduced through increasing the time horizon of the POMDP.

We consider federated learning in a wireless edge network, where multiple power-limited mobile devices collaboratively train a global model, using their local data with the assistance of an edge server. Exploiting over-the-air computation, the edge server updates the global model via analog aggregation of the local models over noisy wireless fading channels. Unlike existing works that separately optimize computation and communication at each step of the learning algorithm, in this work, we jointly optimize the training of the global model and the analog aggregation of local models over time. Our objective is to minimize the accumulated training loss at the edge server, subject to individual long-term transmit power constraints at the mobile devices. We propose an efficient algorithm, termed Online Model Updating with Analog Aggregation (OMUAA), to adaptively update the local and global models based on the time-varying communication environment. The trained model of OMUAA is channel- and power-aware, and it is in closed form with low computational complexity. We study the mutual impact between model training and analog aggregation over time, to derive performance bounds on the computation and communication performance metrics. Simulation results based on real-world image classification datasets and typical Long-Term Evolution network settings demonstrate substantial performance gain of OMUAA over the known best alternatives.

We consider cooperative multiple-input multiple-output (MIMO) precoding design with multiple access points (APs) assisted by a central controller (CC) in a fading environment. Even though each AP may have its own local channel state information (CSI), due to the communication delay in the backhaul, neither the APs nor the CC has timely global CSI. Under this semi-online setting, our goal is to minimize the accumulated precoding deviation between the actual local precoders executed by the APs and an ideal cooperative precoder based on the global CSI, subject to per-AP transmit power limits. We propose an efficient algorithm, termed Semi-Online Precoding with Information Parsing (SOPIP), which accounts for the network heterogeneity in information timeliness and computational capacity. SOPIP does not require the CC to send the full global CSI to each AP. Instead, it takes advantage of the precoder structure to substantially lower the communication overhead, while allowing each AP to effectively combine its own timely local CSI with the delayed global CSI to enable adaptive precoder updates. We analyze the performance of SOPIP in the presence of both multi-slot communication delay and gradient estimation error, showing that it has a bounded performance gap from an offline optimal solution. Simulation results under typical Long-Term Evolution network settings further demonstrate the substantial performance gain of SOPIP over other centralized and distributed schemes.

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

We consider the analysis and design of distributed wireless networks wherein remote radio heads (RRHs) coordinate transmissions to serve multiple users on the same resource block (RB). Specifically, we analyze two possible multiple-input multiple-output wireless fronthaul solutions: multicast and zero forcing (ZF) beamforming. We develop a statistical model for the fronthaul rate and, coupled with an analysis of the user access rate, we optimize the placement of the RRHs. This model allows us to formulate the location optimization problem with a statistical constraint on fronthaul outage. Our results are cautionary, showing that the fronthaul requires considerable bandwidth to enable joint service to users. This requirement can be relaxed by serving a low number of users on the same RB. Additionally, we show that, with a fixed number of antennas, for the multicast fronthaul, it is prudent to concentrate these antennas on a few RRHs. However, for the ZF beamforming fronthaul, it is better to distribute the antennas on more RRHs. For the parameters chosen, using a ZF beamforming fronthaul improves the typical access rate by approximately 8% compared to multicast. Crucially, our work quantifies the effect of these fronthaul solutions and provides an effective tool for the design of distributed networks.

As one of the key technologies in 5G networks, Carrier Aggregation (CA) is studied in this paper. In CA, Component Carriers (CCs) can be activated and deactivated depending on multiple factors, e.g., energy consumption and Quality of Service (QoS) demand of users. We propose CC management strategies where each User Equipment (UE) minimizes its average delay and at the same time minimizes its power consumption while considering that CCs can be activated and deactivated only at certain times, as in real-world CA implementations. We first model the problem as a centralized multi-objective optimum CC management problem. Since centralized approaches would impose a large overhead on the system, we then develop a semi-distributed solution by modeling the problem as a stochastic game and propose a multi-agent Double Deep Q-Network (DDQN) based CC management algorithm to solve the stochastic game. We finally compare the proposed approaches with single CC activation and all-CC activation baseline schemes. Simulation results show that our proposed algorithms outperform the all-CC algorithm in terms of UE power consumption and have the capability of transmitting a number of bits with delay close to the all-CC scheme. Meanwhile, our DDQN-based algorithm decreases the UE power consumption by about 20% with respect to the all-CC scheme.

This paper considers the integration of non-orthogonal multiple access (NOMA) into massive multi-input multi-output (MIMO) systems for downlink transmission. We consider the joint design of user clustering, transmit beamforming, and power allocation to minimize the total transmit power while meeting the signal-to-interference-and-noise ratio targets. We decompose this challenging mixed-integer programming problem into three separate subproblems to solve. We propose a low-complexity learning-based user clustering algorithm, which is a modified version of mean shift clustering with a new channel correlation based clustering metric. The proposed clustering algorithm determines the clusters to trade-off between spatial dimension and power dimension offered by respective MIMO and NOMA for user multiplexing. We then design zero-forcing transmit beamformers to eliminate inter-cluster interference and optimize power allocation to minimize the total transmit power. We provide two case studies for both co-located and distributed massive MIMO systems in spatially highly correlated prorogation environments. Simulation results show that our proposed algorithm forms NOMA clusters based on the available degrees of freedom in the system to effectively use both spatial and power dimensions, which results in a substantial performance improvement over MIMO-only methods or other existing clustering methods in such environments.

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate anarea-basedpilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

This paper presents a new virtualization method for the downlink of a multi-cell multiple-input multiple-output (MIMO) network, to achieve service isolation among multiple Service Providers (SPs) that share the base station resources of an Infrastructure Provider (InP). Each SP designs a virtual precoder for its users in each cell, as its service demand to the InP, without the need to be aware of the existence of the other SPs or to know the channel state information (CSI) outside the cell. The InP performs network virtualization to meet the SPs’ service demands while managing both the inter-SP and inter-cell interference. We consider coordinated multi-cell precoding at the InP and formulate an optimization problem to minimize a weighted sum of signal leakage and precoding deviation, with per-cell transmit power constraints. We propose a fully distributed semi-closed-form solution at each cell, without any CSI exchange across cells. We further propose a low-complexity scheme to allocate the virtual transmit power, for the InP to regulate between interference elimination and virtual demand maximization. Simulation results demonstrate that our precoding solution for network virtualization substantially outperforms the traditional spectrum isolation alternative. It can approach the performance of fully cooperative precoding when the number of antennas is large.

We consider online coordinated precoding design for downlink wireless network virtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO) network with imperfect channel state information (CSI). In our WNV framework, an infrastructure provider (InP) owns each base station that is shared by several service providers (SPs) oblivious of each other. The SPs design their precoders as virtualization demands for user services, while the InP designs the actual precoding solution to meet the service demands from the SPs. Our aim is to minimize the long-term time-averaged expected precoding deviation over MIMO fading channels, subject to both per-cell long-term and short-term transmit power limits. We propose an online coordinated precoding algorithm for virtualization, which provides a fully distributed semi-closed-form precoding solution at each cell, based only on the current imperfect CSI without any CSI exchange across cells. Taking into account the two-fold impact of imperfect CSI on both the InP and the SPs, we show that our proposed algorithm is within an$O(\delta)$gap from the optimum over any time horizon, where$\delta $is a CSI inaccuracy indicator. Simulation results validate the performance of our proposed algorithm under two commonly used precoding techniques in a typical urban micro-cell network environment.

Carrier aggregation (CA) is considered a key enabler technology for delivering higher rates to users of LTE and 5G networks. However, the increased transmission rate comes with the price of higher energy consumption which stems from users continuously monitoring the control channel of the active component carriers (CCs) whether data transmission is ongoing or not. In order to reduce energy consumption, we exploit the activation-deactivation procedure at the medium access control (MAC) layer of LTE/5G network. In this paper, we propose a reinforcement learning-based algorithm to improve energy-efficiency by dynamically activating-deactivating secondary component carriers (SCCs) with awareness of the user traffic profiles. The proposed algorithm aims to predict the arrival of data and identify SCCs to activate for each user. In addition, a traffic splitting approach and an intelligent exploration strategy are proposed to balance users' load among CCs and improve the convergence of the algorithm, respectively. Results of the proposed algorithm are compared with three baseline algorithms. The first baseline always activates all CCs for each user, the second baseline activates one carrier only (i.e., the primary carrier) and the third baseline algorithm relies on a reactive method, where the activation-deactivation decision is performed after observing the arrival of data. Results show that Q-learning outperforms the baseline algorithms by achieving the highest sum throughput (and lowest average delay) with the lowest number of activated SCCs, which is obtained by learning to dynamically activate SCCs according to the traffic pattern. Hence, Q-learning is considered the most energy-efficient compared to the baseline algorithms.

In this paper, we study the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul. We implement a scheme where the RRHs use zero-forcing beamforming (ZF-BF) for the access channel to jointly serve multiple users, while on the backhaul the RRHs are connected to their central units (CUs) through point-to-point wireless links. We investigate the effect of this scheme on the deployment of the RRHs and on the resulting achievable spectral efficiency over the access channel (under a backhaul outage constraint). Our results show that even for noise-limited backhaul links, a large bandwidth must be allocated to the backhaul to allow freely distributing the RRHs in the network. Additionally, our results show that distributing the available antennas on more RRHs is favored as compared to a more co-located antenna system. This motivates further works to study the efficiency of wireless backhaul schemes and their effect on the performance of coordinated distributed networks with joint transmission.

We study the problem of user-scheduling and resource allocation in distributed multi-user, multiple-input multiple-output (MIMO) networks implementing user-centric clustering and non-coherent transmission. We formulate a weighted sum-rate maximization problem which can provide user proportional fairness. As in this setup, users can be served by many transmitters, user scheduling is particularly difficult. To solve this issue, we use block coordinate descent, fractional programming, and compressive sensing to construct an algorithm that performs user-scheduling and beamforming. Our results show that the proposed framework provides an 8- to 10-fold gain in the long-term user spectral efficiency compared to benchmark schemes such as round-robin scheduling. Furthermore, we quantify the performance loss due to imperfect channel state information and pilot training overhead using a defined area-based pilot-reuse factor.

Unmanned Aerial Vehicles (UAV) are expected to play a crucial role in the future of 5G and beyond. However, designing efficient routing protocols for UAV is challenging due to the mobility and energy constraints. This problem becomes harder in UAV-assisted 5G networks because of its impact on the time slot assignment for the uplink and downlink in Time Division Duplex (TDD) frame structure. Thus, in this paper, we propose a new optimum routing technique for UAV-assisted TTD 5G networks, the Optimized Load-Balancing Routing (OLBR). The optimum routing problem is formulated in such a way that the decision variables are used to compute the time slot assignment in the 5G connection between UAV nodes. The objective of the optimization model is to minimize the network-wide delay. By distributing traffic across different alternative routes, OLBR minimizes network congestion, resulting in shorter queuing delays. Such a load-balancing also decreases the possibility of node failure due to energy depletion. The proposed OLBR is compared to the shortest path routing using Monte Carlo simulation on two different network topologies at different network traffic loads. The simulation results show that the OLBR produces significant savings in network-wide packet delay compared to the shortest path.

Carrier Aggregation (CA) has been a breakthrough in LTE that led to increased throughput for users, and is still one of the key technologies in 5G that helps to enhance spectrum utilization. In CA, Component Carriers (CCs) are dynamically activated and deactivated depending on several performance factors. Optimal selection of CCs has been studied in the literature. However, the latency associated with activation and deactivation of CCs, control channel overhead for switching CCs, as well as the energy consumed for monitoring the active CCs have not been a part of the optimal CC selection problem. Nevertheless, those become stringent design constraints in practice. In this paper, we address optimal CC selection and resource allocation in 5G networks, where the above constraints are considered and the 5G network supports several service types with different 5G QoS Identifiers (5QI). The proposed optimum joint CC selection and Radio Resource Block (RB) allocation schemes maximize average throughput of users and satisfy QoS of users in terms of delay. In addition, the proposed schemes take CC activation and deactivation burden into consideration and aim to minimize the number of activations and deactivations. The simulation results demonstrate that our proposed solution outperforms the state of the art solution while satisfying the QoS requirements and creating close to 95.5% reduction on the number of CCs activations and deactivations.

The ability to accurately forecast network resource utilization is vital in next-generation wireless networks. Based on the predicted load, telecom operators can proactively allocate network resources in an efficient way. In this paper, we perform a thorough analysis of a cellular network downlink load dataset collected at millisecond resolution. We first evaluate various statistical metrics of the physical resource block (PRB) utilization data to investigate its predictability. Then, we develop deep learning-based models to forecast PRB utilization in radio access networks (RANs). In particular, we propose univariate and multivariate long short-term memory (LSTM) network-based architectures for the forecasting task and investigate the impact of various prediction horizons and history lengths. When predicting PRB utilization, our approach showed up to 49% improvement in the Coefficient of Determination (r2score) and 19.5% decrease in the Root Mean Square Error (RMSE) compared with the baseline methods used.

We consider online convex optimization (OCO) with multi-slot feedback delay, where an agent makes a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that are possibly time-varying. The current convex loss function and the long-term constraint function are revealed to the agent only after the decision is made, and they may be delayed for multiple time slots. Existing work on OCO under this general setting has focused on the static regret, which measures the gap of losses between the online decision sequence and an offline benchmark that is fixed over time. In this work, we consider both the static regret and the more practically meaningful dynamic regret, where the benchmark is a time-varying sequence of per-slot optimizers. We propose an efficient algorithm, termed Delay-Tolerant Constrained-OCO (DTC-OCO), which uses a novel constraint penalty with double regularization to tackle the asynchrony between information feedback and decision updates. We derive upper bounds on its dynamic regret, static regret, and constraint violation, proving them to be sublinear under mild conditions. We further apply DTC-OCO to a general network resource allocation problem, which arises in many systems such as data networks and cloud computing. Simulation results demonstrate substantial performance gain of DTC-OCO over the known best alternative.

To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional communication resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, a deep learning-based framework for multi-dimensional intelligent multiple access (MD-IMA) scheme is developed for beyond 5G and 6G wireless networks to meet the real-time and diverse quality of service (QoS) requirements by fully utilizing the available radio resources in heterogeneous domains. To achieve intelligent operation of MD-IMA, the proposed deep learning scheme is achieved based on the convergence of long short term memory (LSTM) and deep reinforcement learning (DRL). Specifically, an LSTM neural network is used to predict the long-term network dynamics and inference changes in QoS requirements of the MD-IMA. Meanwhile, a deterministic policy gradient (DDPG) algorithm, a model-free DRL technique, is adopted to optimize the multi-dimensional radio resource allocation in real-time by dynamically following the fluctuations of the network situation. With the aid of the DDPG algorithm, radio resource management for MD-IMA can be achieved efficiently with reduced processing latency as compared to the conventional model-based approaches. Furthermore, the effectiveness of our proposed deep learning framework for MD-IMA is validated through real-world cellular traffic data-sets. The experimental results demonstrate that the proposed scheme can outperform state-of-the-art algorithms.

Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately, effective slicing of Radio Access Networks (RAN) is still challenging due to time-varying network situations. This paper proposes a new intelligent RAN slicing strategy with two-layered control granularity, which aims at maximizing both the long-term QoS of services and spectrum efficiency (SE) of slices. The proposed method consists of an upper-level controller to ensure the QoS performance, which enforces loose control by performing adaptive slice configuration according to the long-term dynamics of service traffic. The lower-level controller is to improve SE of slices, by tightly scheduling radio resources to users at the small time-scale. To realize the proposed RAN slicing strategy, we propose a model-free deep reinforcement learning (DRL) framework, which is a hierarchical structure that collaboratively integrating the modified deep deterministic policy gradient (DDPG) and double deep-Q-network algorithm. Specifically, the lower-level control problem is a mixed-integer stochastic optimization problem with multiple constraints. This kind of problem is hard to be directly solved by the exiting DRL algorithms, since it involves searching for the solution in a vast set of mixed-integer action space, which will induce unbearable computational complexity. Thus, we propose a novel action space reducing approach, embedding the convex optimization tools into the DDPG algorithm, to speed up the lower-level control. Furthermore, simulation results confirm the effectiveness of our proposed intelligent RAN slicing scheme.

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge of tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment in cellular networks could experience very different constraints in the multi-dimensional resource domains. To achieve stringent yet diverse QoS with limited resources, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is proposed in this paper to exploit disparate resource constraints among heterogeneous equipment for 5G beyond and 6G networks. With the assist of real-time data analysis, real-time QoS requirements, and resource availability of the related equipment are first determined in the proposed MD-IMA. Based on this, multiple access (MA) scheme is then intelligently adapted accordingly for each equipment in multi-dimensional resource domain to maximize the overall system requirement with operational constraints. The resource allocation in the MD-IMA system is further formulated as an optimization problem. To solve this non-convexity optimization of high computational complexity, the overall optimization is divided into several sub-problems and a joint optimization algorithm is adopted. Simulation results demonstrate the system energy efficiency performance gain of proposed MD-IMA over traditional MA is around 15% - 18%.

With the advancements in wireless network technologies over the past few decades and the deployment of 4G LTE networks, the capabilities and services provided to end-users have become seemingly endless. Users of smartphones utilize high-speed network services while commuting on public transit and hope to have a consistent, high-quality connection for the duration of their trip. Due to the massive load demand on cellular networks and frequent changes in the underlying radio channel, users often experience sudden unexpected variations in the connection quality. To overcome such variations and maintain a consistent connection, these variations need to be predicted before they occur. This can be accomplished by the spatio-temporal analysis of the different network quality parameters and the investigation of the main factors that affect the network's performance and QoS. To this end, we conducted a network survey via Kingston Transit in Kingston, Ontario, Canada. We used the Android network monitoring application G-NetTrack Pro to build a dataset of various client-side wireless network quality parameters. The dataset consists of 30 repeated public transit bus trips at three different times of the day, each lasting around one hour. In this paper, we describe the data collection process, present an analysis of the collected data, and investigate the effects of time and location on the network's measured throughput and signal strength. We made the collected data, including more than 190 thousand unique records, publicly available to researchers in a domain where open data is rare.

A multicarrier bidirectional filter-and-forward (FF) relay-assisted network in the context of device-to-device communication is investigated. The network consists of two multicarrier-based user devices that can communicate through multiple relay nodes equipped with finite impulse response (FIR) filters to equalize the frequency-selective channels. We jointly design the distributed equalization weight vector and the power allocations at the users to minimize the total transmit power under two quality of service constraints measured by the received sum-rates at the users. We propose a novel semi-closed form sub-optimal solution to this non-convex joint optimization problem. Simulation results show that the proposed design, in conjunction with the FF-based relay nodes, attains substantially better performance than the existing designs associated with amplify-and-forward and multicarrier relay nodes.

We consider online downlink precoding design for multiple-input multiple-output (MIMO) wireless network virtualization (WNV) in a fading environment with imperfect channel state information (CSI). In our WNV framework, a base station owned by an infrastructure provider (InP) is shared by several service providers (SPs) that are oblivious to each other. The SPs design their virtual MIMO transmission demands to serve their own users, while the InP designs the actual downlink precoding to meet the service demands from the SPs. Therefore, the impact of imperfect CSI is two-fold, on both the InP and the SPs. We aim to minimize the long-term time-averaged expected precoding deviation, considering both long-term and short-term transmit power limits. We propose a new online MIMO WNV algorithm to provide a semi-closed-form precoding solution based only on the current imperfect CSI. We derive a performance bound for our proposed algorithm and show that it is within an O(δ) gap from the optimum over any given time horizon, where δ is a normalized measure of CSI inaccuracy. Simulation results with two popular precoding techniques validate the performance of our proposed algorithm under typical urban micro-cell Long-Term Evolution network settings.

We consider downlink multiple-input multiple-output (MIMO) wireless network virtualization (WNV) in a fading environment, via a base station (BS) precoding design. The BS is owned by an infrastructure provider (InP) and is shared by several service providers (SPs) who are oblivious to each other. The SPs realize their virtual-cell transmissions via MIMO precoding provided by the InP. We aim to minimize the time-averaged expected deviation of the precoding provided by the InP from the SPs' virtualization demands, considering both long-term and short-term transmit power limits at the BS. We propose an online MIMO WNV algorithm to provide a precoding solution through Lyapunov optimization. Our online precoding solution only requires the current channel state information, and it has a semi-closed form with low computational complexity. We provide an upper bound on the performance of the proposed algorithm, showing that it can be arbitrarily close to the optimum over any given time horizon. Simulation results validate the performance of our proposed algorithm under typical urban micro-cell settings.

With the deployment of large scale antenna array in millimeter wave (mmWave) band, the resolution of beamforming has been dramatically improved. To reduce the long beam-training process using reference signal (RS) in codebook-based high resolution beamforming, hierarchical codebook is often used to reduce the number of beam-training symbols. However, the large beam-training overhead is still the bottleneck for overall system performance improvement in term of the true achievable data rate. In this paper, with the angle reciprocity in frequency duplex division (FDD) system, a neural network based line of sight path angle of arrival (LAoA) estimation algorithm is proposed for beam selection, in order to achieve the non-RS-aided codebook-based beamforming. To further achieve high accuracy LAoA estimation, two-step neural network models are designed to capture the relationship between the receiving signal and the corresponding LAoA. The numerical results show that the proposed algorithm outperforms the benchmark algorithm in terms of sum weighted data rate (SWR) and sum data rate (SR). In the low signal to noise ratio (SNR) environments with a couple of uplink signal snapshots, our algorithm also performs better than MUSIC based beam selection algorithm.

We utilize the orthogonality and channel hardening properties of massive multiple-input multiple- output (MIMO) systems to propose an efficient uplink transmission scheme for a heterogeneous network (HetNet). Such a network consists of multiple user-equipments (UEs) communicating with a macro-cell base station (MCBS) through a small-cell BS (SCBS) where both BSs have a large number of antennas and deploy zero-forcing (ZF) detection. The SCBS helps relay UEs' information using quantize-forward (QF) relaying with Wyner-Ziv (WZ) binning and multiple-timeslot transmission for the binning indices to the MCBS. The MCBS then deploys separate and sequential decoding for each UE's message. To maximize the rate region, we optimize the quantization levels through geometric programming and further obtain the optimal transmission timeslot durations in terms of the optimal quantization. We show that the proposed scheme has linear codebook size and decoding complexity in the number of UEs, while it achieves the same rate region of other QF schemes that employ joint transmission at the SCBS and/or joint decoding at the MCBS, all of which have exponential complexity. Furthermore, simulation results show that the SCBS should employ finer quantization for UE signals that have strong UE-SCBS links compared with the UE-MCBS links, and the proposed scheme can substantially outperform several existing alternatives under a wide range of parameter settings.

In underlay device-to-device (D2D) communications, a D2D pair reuses the cellular spectrum causing interference to regular cellular users. Maximizing the performance of underlay D2D communications requires joint consideration for the achieved D2D rate and the interference to cellular users. In this paper, we consider the D2D power allocation optimization over multiple resource blocks (RBs), aimed at maximizing either the ergodic D2D rate or the ergodic sum rate of D2D and cellular users, under the long-term sum-power constraint of the D2D users and per-RB probabilistic signal-to-interference-and-noise (SINR) requirements for all cellular users. We formulate stochastic optimization problems for D2D power allocation over time. The proposed optimization framework is applicable to both uplink and downlink cellular spectrum sharing. To solve the proposed stochastic optimization problems, we first convexify the problems by introducing a family of convex constraints as a replacement for the non-convex probabilistic SINR constraints. We then present two dynamic power allocation algorithms: a Lagrange dual-based algorithm that is optimal but with a high computational complexity and a low-complexity heuristic algorithm based on dynamic time averaging. Through simulation, we show that the performance gap between the optimal and heuristic algorithms is small, and the effective long-term stochastic D2D power optimization over the shared RBs can lead to substantial gains in the ergodic D2D rate and the ergodic sum rate.

In order to exploit the potential capacity of 5G, the deployment of ultra-dense small cells is an approach that can dramatically increase the radio resource reuse factor and network capacity. However, network densification with a large number of small cells brings challenges due to increased network complexity, deployment cost and inter-cell interference. In this paper, a new 5G architecture with virtual small cells (VSCs), which are dynamically formed by grouping a number of user devices in close proximity and adapted according to traffic condition, is proposed to improve the cost and energy efficiency compared with the traditional fixed deployment of small cells. In each virtual small cell, one mobile device is selected as a cell head (CH) to aggregate intra- cell traffic using unlicensed band transmissions and then communicates with its macro-cell base station in a licensed band through beamformed transmission, which reduces the inter-cell interference and improves spectrum efficiency. In this paper, a highly directional beamforming technique is employed to enable a dedicated inband fronthaul link for VSC. Our work focuses on how to design adaptive beamforming to minimize the transmit power under throughput requirements and power constraints. Both the mathematical analysis and simulation results demonstrate that VSCs can increase power efficiency dramatically while providing flexibility and reduced cellular load, when compared with macrocell only deployment and traditional fixed small cells scenario.

For device-to-device (D2D) underlaid cellular networks, the perfect channel state information (CSI) may not be available at the base station (BS). In this work, under an assumption of partial CSI, we study the problem of maximizing the expected sum rate for a cellular user (CU) and a D2D pair, with receive beamforming at the BS, subject to minimum SINR requirements for both the CU and D2D pair, per-node maximum power, and inter-cell interference constraints in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. We first consider the D2D admissibility problem to determine whether the D2D pair can reuse the channel resource of the CU. We then propose a robust power control algorithm using a ratio-of-expectation approximation to maximize the expected sum rate. For benchmarking, we further provide an upper bound on the maximum expected sum rate. Simulation results show that our proposed solution gives performance close to the upper bound.

In order to support dramatically increased traffic from diverse network services, deployment of ultra dense networks to improve the overall capacity of the fifth generation (5G) wireless networks becomes inevitable. However, network densification with increased number of small cells brings significant challenges in terms of quality of service provisioning and deployment cost due to increased network complexity, signalling overhead and inter-cell interference. In this paper, virtual small cell (VSC), which is formed adaptively according to traffic condition and service requirements, is investigated as a solution for cost-effective and reliable service provisioning in 5G ultra dense networks. A K-means clustering based VSC formation scheme is proposed in this paper, and the corresponding protocol conversion for data transmission across unlicensed and licensed networks at cell head (CH) is developed. Based on the VSC architecture design, a new resource allocation algorithm is also proposed for VSC scenario in order to improve the system throughput with comparable fairness.

Massive multiple‐input multiple‐output (MIMO) systems bring manifold improvements in the system spectral efficiency but result in high hardware and processing complexity at the base station. Employing hybrid precoding at the base station can reduce such complexity. In this study, unlike most existing work on hybrid precoding design, the authors consider a sub‐connected analogue combining structure to reduce the complexity. Starting from an important observation on the effect of sequentially designed analogue phased arrays on users' sum rate, the authors develop three low‐complexity hybrid precoding schemes for a multi‐user massive MIMO system. The proposed schemes apply equal gain transmission (EGT) based analogue beamforming to reap the diversity benefit of an analogue phased array and employ zero‐forcing (ZF) beamforming for nullifying inter‐user interference. The authors carry out an extensive computational complexity analysis and simulation study on the proposed schemes. The proposed singular‐value decomposition based EGT scheme outperforms all others but incurs the highest computational burden. On the other hand, the sequential‐EGT scheme is the least computationally intensive scheme but shows the worse performance amongst them.

For device-to-device (D2D) communication under laid in a cellular network with uplink resource sharing, both cellular and D2D pairs may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this paper, under optimal BS receive beamforming, we jointly optimize the power of a cellular user (CU) and a D2D pair for their sum rate maximization, while satisfying minimum SINR requirements and worst-case ICI limit in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. First, the necessary and sufficient condition for the D2D admissibility under given constraints is obtained. Finally, we consider joint power control of the CU and D2D transmitters. We propose a power control algorithm to maximize the sum rate. Depending on the severity of ICI that D2D and CU may cause, we categorize the feasible solution region into five cases, each of which may further include several scenarios based on minimum SINR requirements. The proposed algorithm is optimal when ICI to a single neighboring cell is considered. For multiple neighboring cells, we provide an upper bound on the performance loss by the proposed algorithm and conditions for its optimality. We further extend our consideration to the scenario of multiple CUs and D2D pairs, and formulate the joint power control and CU-D2D matching problem. We show how our proposed solution for one CU and one D2D pair can be utilized to solve this general joint optimization problem. Simulation demonstrates the effectiveness of our power control algorithm and the nearly optimal performance of the proposed approach in the setting of multiple CUs and D2D pairs.

We consider a cellular network where each cell contains multiple source-destination pairs communicating through multiple amplify-and-forward relays using orthogonal channels. We propose an optimal relay beamforming design that minimizes the maximum interference at the neighboring cells subject to per-relay power limits and minimum received signal-to-noise ratio (SNR) requirements. Even though the problem is non-convex, we show that it has zero Lagrange duality gap, and we convert its dual problem to a semi-definite programming problem. Depending on the values of the optimal dual variables, we study three cases to obtain the optimal beam vectors accordingly. This results in an iterative algorithm that provides a semi-closed-form optimal solution. We extend our algorithm to the problem of maximizing the minimum SNR subject to some pre-determined maximum interference constraints at neighboring cells, by the solution to the min-max interference problem along with a bisection search. The solution to this max-min SNR problem gives insight into the worst-case signal-to-interference-and-noise ratio given some maximum interference target. The performance of the proposed algorithm is studied numerically, both for when the knowledge of interference channel is perfect and for when it is imperfect due to either limited feedback or channel estimation error.

In underlay device-to-device (D2D) communication, a D2D pair reuses the cellular spectrum and creates interference to regular cellular users. Optimal operation requires joint consideration for the achieved D2D rate and the added interference to cellular users. Most existing work on D2D rate maximization concerns only the simplified scenario where the D2D pair has access to a single channel or resource block. In this work, we present an optimization solution to allocate the D2D transmission power over multiple channels, to maximize the sum rate between D2D and cellular users, under a sum-power constraint on the D2D transmitter and minimum SINR guarantees at each RB for all cellular users. The proposed optimization is applicable to both uplink and downlink cellular spectrum sharing. Our simulation studies further shed light into how the maximum sum rate is impacted by the available D2D power and the SINR guarantees.

We investigate the optimal relay beamforming problem for multi-user peer-to-peer communication with amplify-and-forward relaying in a multi-channel system. Assuming each source-destination (S-D) pair is assigned an orthogonal channel, we formulate the problem as a min-max per-relay power minimization problem with minimum signal-to-noise (SNR) guarantees. After showing that strong Lagrange duality holds for this nonconvex problem, we transform its Lagrange dual problem to a semi-definite programming problem and obtain the optimal relay beamforming vectors. We identify that the optimal solution can be obtained in three cases, depending on the values of the optimal dual variables. These cases correspond to whether the minimum SNR requirement at each S-D pair is met with equality, and whether the power consumption at a relay is the maximum among relays at optimality. We obtain a semi-closed form solution structure of relay beam vectors, and propose an iterative approach to determine relay beam vector for each S-D pair. We further show that the reverse problem of maximizing the minimum SNR with per-relay power budgets can be solved using our proposed algorithm with an iterative bisection search. Through simulation, we analyze the effect of various system parameters on the performance of the optimal solution. Furthermore, we investigated the effect of imperfect channel side information of the second hop on the performance and quantify the performance loss due to either channel estimation error or limited feedback.

We consider a wireless cellular network with multiple amplify-and-forward (AF) relays in each cell, assisting the communication of multiple source-destination pairs with relay transmission beamforming. Our objective is to minimize the maximum interference power among all active receivers in a neighboring cell subject to per-relay power and minimum received SNR constraints. We propose an efficient algorithm to obtain the optimal relay beamforming vectors. We show that even though the optimization problem is non-convex, it has zero Lagrange duality gap and can be converted to a semi-definite programming problem. The performance of the proposed algorithm is studied numerically, both for the case where the interference channel information is exactly known and for the case of inaccurate channel information due to either limited feedback or channel estimation error. It is demonstrated that the min-max interference approach substantially outperforms the alternative where we simply minimize the maximum relay transmission power.

In this paper, the utilization of mobile devices (MDs) as decode-and-forward relays in a device-to-device assisted virtual MIMO (VMIMO) system is studied. Single antenna MDs are randomly distributed on a 2D plane according to a Poisson point process, and only a subset of them are sources leaving other idle MDs available to assist them (relays). Our goal is to develop an efficient algorithm to cluster each source with a subset of available relays to form a VMIMO system under a limited feedback assumption. We first show that the NP-hard optimization problem of precoding in our scenario can be approximately solved by semidefinite relaxation. We investigate a special case with a single source and analytically derive an upper bound on the average spectral efficiency of the VMIMO system. Then, we propose an optimal greedy algorithm that achieves this bound. We further exploit these results to obtain a polynomial time clustering algorithm for the general case with multiple sources. Finally, numerical simulations are performed to compare the performance of our algorithm with that of an exhaustive clustering algorithm, and it shown that these numerical results corroborate the efficiency of our algorithm.

We consider the downlink of a cellular system in which each base station (BS) has multiple distributed antenna ports that are geographically dispersed over the cell. The goal of the BSs is to improve cell-edge performance by selecting the subset of ports that maximizes the minimum signal-to-interference-plus-noise ratio of the user terminals in a coordinated manner. This problem is cast as a binary-constrained optimization problem, which is known to be NP-hard. To circumvent this difficulty, the semidefinite relaxation technique is used to efficiently generate Gaussian distributed vectors. Simulation results show that rounding a relatively small number of these vectors yields close-to-optimal solutions of the original problem.

In this paper, we consider coordinated downlink transmission in a cellular system wherein each base station (BS) has multiple geographically dispersed antenna ports. Each port uses a fixed transmit power and the goal of the BSs is to collectively determine the subset of ports and the corresponding beam steering coefficients that maximize the minimum signal-to-interference-plus-noise ratio observed by the user terminals. This problem is NP-hard. To circumvent this difficulty, a two-stage polynomial-complexity technique that relies on semidefinite relaxation and Gaussian randomization is developed. It is shown that, for the considered scenarios, the port state vectors and beam steering coefficients generated by the proposed technique yield a performance comparable to that yielded by exhaustive search, but with a significantly less computational complexity. It is also shown that the proposed technique results in significant power savings when compared with other transmission strategies proposed in the literature.

A cellular distributed antenna system (DAS) allows mobile user terminals (UTs) to be served at higher data rates as compared to conventional cellular systems by reducing the path loss and attaining macrodiversity gains. Moreover, such a cellular DAS can be perceived as a distributed multi-user multiple-input multiple-output system. In this paper, the block diagonalization and zero-forcing dirty-paper coding downlink transmission schemes are extended for a single-cell DAS with multi-antenna distributed antenna ports (DAPs) and multi-antenna UTs, where only a subset of all DAPs in the cell transmit to each UT. The aggregate cell spectral efficiency that is achieved by these schemes per frequency-time resource block is compared for both DAS and collocated antenna system (CAS) architectures, subject to the same total power constraint. The gains of the cellular DAS over the cellular CAS are demonstrated, and the effect of the number of antennas per DAP on the performance of the cellular DAS is investigated.

A system model is proposed that allows one to apply both trellis coding and PN spreading sequence to the data symbols to be transmitted. Rate n/n+1, trellis codes using 2/sup n+1/-point MPSK signal constellations are investigated when Gold sequences are used for PN spreading. Performance in an additive white Gaussian noise (AWGN) channel is investigated, with 5-20 users transmitting simultaneously. Using the criteria of equal complexity and throughput, the performance of the trellis codes in a SSMA (spread spectrum multiple access) environment is compared to that of medium-rate to low-rate convolutional codes through the use of a generalized transfer function bound. The average degradation due to the interuser interference is determined by the method of moments. The validity of approximating the interuser interference as a Gaussian random variable is also investigated. The numerical results illustrate that for a given complexity, chip rate and throughput, low-rate convolutional codes provide the best performance in an SSMA system. As lower-rate convolutional codes are used, there is an increase in the effective interuser interference due to the greater cross-correlation effects from using shorter PN sequences, or alternatively from the effects of partial cross-correlation. However, this increased degradation is more than overcome by the increased distance properties of the low-rate codes.>

Differential detection of duobinary CPFSK is analyzed to determine its performance as an alternative to other incoherent bandwidth efficient detection schemes. It is shown that a BPSK differential receiver can correctly detect a duobinary MSK signal when precoding (differential encoding) is employed at the transmitter. The effects of intersymbol interference due to IF filtering at the receiver are analyzed for a Gaussian and a 4th-order Butterworth IF filter. Furthermore, nonredundant error correction is shown to be feasible for differential detection of duobinary MSK and a method of analyzing the performance of the error correction receiver is presented.