We consider an Internet of Things (IoT) network, where a large amount of sensor data is transmitted from wireless IoT devices to a central server for the classification of system behaviors. When the number of IoT devices and their sensors is very large, the redundancy in the collected data at the server can also be very high due to the high correlation between the data. By compressing the sensor data at IoT devices for reducing the redundancy, the transmit power can be largely reduced but the accuracy of the classification can also be decreased. Our objective is to maximize the classification accuracy by determining the compression ratio at each device, under the total transmit power constraint. In traditional resource allocation for multiuser wireless networks, the classification accuracy cannot be directly considered since it is usually data-agnostic. On the other hand, in deep learning, the classification accuracy can be maximized by training a neural network architecture using datasets. The deep learning, however, does not usually consider the wireless channel information, that is essential for the resource allocation. In this article, we propose a resource allocation scheme based on a binarized neural network in order to maximize the classification accuracy at the server while satisfying the total transmit power constraint by exploiting both the wireless channel state information and the data-driven approach. In experimental results, we show that the classification accuracy can largely be increased by the proposed scheme using MNIST and CIFAR-10 datasets.

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multidevice terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporateboth rate and transmission distanceinto the objective function of our problem formulation with key features of THz bands, including the spreading and molecular absorption losses. More specifically, as a performance metric of Tera-IoT networks, we adopt the transport capacity (TC), which is defined as the sum of therate–distance productsover all users. This metric has been widely adopted in large-scale ad hoc networks and would also be appropriate for evaluating the performance of various Tera-IoT applications. We then formulate an optimization problem that aims at maximizing the TC. Moreover, motivated by the importance of the transmission distance that is very limited due to the high path loss in THz bands, our optimization problem is extended to the case of allocating the subwindow, transmit power, and transmission distance. We show how to solve our problems via an effective two-stage resource allocation strategy. We demonstrate the superiority of our adaptive solution over benchmark methods via intensive numerical evaluations for various environmental setups of large-scale Tera-IoT networks.

This article studies simultaneous wireless information and power transfer (SWIPT) for unmanned aerial vehicle (UAV)-enabled Internet-of-Things (IoT) networks. Specifically, it is assumed that a single UAV wishes to simultaneously send common and private data streams as well as energy to multiple IoT nodes, in which the common stream should be recovered by all nodes while private streams are recovered only by the dedicated nodes. In addition, it is assumed that each node uses a power-splitting method that divides the received signal into two parts for energy harvesting and information decoding. Under this setting, by applying a concave-convex procedure (CCCP) method, we propose a novel algorithm that maximizes the minimum rate of private streams of IoT nodes by properly allocating the transmit power of each stream, adjusting the power-splitting ratios at IoT nodes, and designing the trajectory of the UAV, while the common rate and energy harvesting constraints at each node should also be satisfied. Moreover, to enable multiuser communication, a superposition coding in conjunction with successive interference cancellation (SIC) decoding is considered for SWIPT. The performance of the proposed scheme is evaluated and compared with benchmark schemes in various aspects by numerical simulations.

In Internet of Things (IoT) systems, a number of sensor devices monitor the physical system states and exchange information with each other. The main limitation is that the IoT devices are generally energy constrained since those are powered with batteries. To address this energy problem, we consider a cooperative wireless-powered communication network (WPCN), which consists of three phases: 1) downlink (DL) energy transfer from a multi-antenna access point (AP); 2) data sharing among IoT devices; and 3) uplink (UL) information transfer from single-antenna IoT devices. Based on the shared data and the harvested energy, the single-antenna IoT devices in the neighborhood cooperate to form a virtual antenna array in order to transmit their information simultaneously to the multi-antenna AP using a multiple-input multiple-output (MIMO) technique in the UL information transfer phase. In this study, the transmit covariance matrices (i.e., beamforming vectors and the corresponding transmit power allocation) used for both DL energy transfer and UL information transfer are jointly designed to maximize the UL capacity based on the Lagrangian method. Furthermore, the time allocation for each phase is optimized based on a stochastic gradient method. In the numerical results, it is shown that our proposed beamforming scheme and the stochastic time allocation can achieve near-optimal performance.

The conventional studies on virtual multiple-input multiple-output based cooperative time division multiple access (TDMA) have been focused on the cooperative retransmission of failed data. In this paper, we propose an opportunistic cooperative TDMA (OC-TDMA) scheme where a source user transmits its own data and the cooperative users opportunistically transmit low-data-rate users' data. To do this, time slots are divided into non-cooperative and cooperative time slots. The non-cooperative time slots are located at earlier time slots, in which weak users, who cannot satisfy the required signal-to-interference-plus-noise ratio (SINR), are scheduled. The cooperative time slots are located at later time slots, in which strong users, who can satisfy the required SINR, are scheduled. In the cooperative time slots, the source user transmits its own data and the other users (i.e., cooperative users) assist to transmit the weak users' data which was transmitted at the non-cooperative time slots. The optimal and sub-optimal OC-TDMA algorithms are analyzed and evaluated in terms of the required excess capacity, which indicates the capacity additionally required to satisfy the SINR constraints of all the users after all the transmission is finished. Throughout the simulation and numerical results, it is demonstrated that OC-TDMA schemes outperform conventional TDMA schemes.

An Internet-of-Things (IoT) cellular network is considered in which IoT devices communicate with an IoT base station using IoT sub-bands placed between long-term evolution (LTE) bands. Due to spectral leakage, inter-band interference exists among IoT sub-bands and also between LTE and IoT bands. It is assumed that the IoT cellular network is responsible for reducing its interference to the LTE network to a certain threshold level. Under such interference regulation to LTE bands, we establish a joint sub-band assignment and power allocation optimization in order to maximize the sum rate of the IoT cellular network. A novel two-stage suboptimal algorithm that sequentially performs sub-band assignment and power control is proposed, reflecting the impact of spectral leakage in its optimization procedure. Simulation results demonstrate that the proposed algorithm considering the impact of spectral leakage outperforms the conventional optimization algorithms without considering spectral leakage. It is further shown that it provides almost the same sum rate achievable for a stand-alone network as if there were no LTE networks.

An internet of things (IoT) cellular network is considered in which IoT devices communicate with an IoT base station using IoT sub-bands placed between long-term evolution (LTE) bands. Due to spectral leakage, inter-band interference exists among IoT subbands and also between LTE and IoT bands. It is assumed that the IoT cellular network is responsible for reducing its interference to the LTE network to a certain threshold level. Under such interference regulation to LTE bands, we establish a joint sub-band assignment and power allocation optimization in order to maximize the sum rate of the IoT cellular network. A novel two-stage suboptimal algorithm that sequentially performs sub-band assignment and power control is proposed, reflecting the impact of spectral leakage in its optimization procedure. Simulation results demonstrate that the proposed algorithm considering the impact of spectral leakage outperforms the conventional algorithms without considering spectral leakage.

We study a simultaneous wireless information and power transfer (SWIPT) setup for the K-user multiple-input single-output (MISO) interference channel. Each transmitter can either send a private or a common message, and each receiver uses a power splitting method that divides the received signal into two parts for information decoding and energy harvesting. The private message is recovered by the destination receiver only while the common message is recovered by all the receivers. While satisfying individual rate and energy harvesting constraints, our goal is to minimize the total transmit power by properly selecting the message types, designing the beamforming vectors at transmitters, and adjusting the power splitting parameters at receivers. To optimize the parameters of our proposed scheme, we first formulate the problem in terms of a semidefinite programming (SDP) and further relax the formulation to propose low complexity algorithms for numerical optimization. In numerical results, it is shown that the required transmit power of the proposed scheme is much lower than that of the conventional scheme based on transmitting private messages only, especially when the required harvesting energy is high or the channel gains of the cross-links are strong.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) for Internet of Things (IoT) sensor networks. The transmitters (e.g., access point) employ hybrid beamforming and each IoT receiver adopts a power splitting (PS) method that divides the received signal into two parts for information recovery and energy harvesting. We propose a novel strategy for SWIPT in which the transmitters have the option to either send a private or a common message. The private message is recovered only by a designated IoT receiver while common messages are recovered by all the receivers. While requiring the receivers to recover a common message results in additional rate constraints, the overall system performance benefits by mitigating interference. We propose SWIPT schemes that minimize the total transmit power by properly selecting message configurations, designing hybrid beamforming vectors, and adjusting the PS ratio at the receivers to satisfy the individual rate and energy harvesting constraints. In particular, we develop tractable and efficient twostage algorithms that, in the first stage determine the message configurations and in the second stage find the beamforming vectors (for both analog and digital components). Numerical simulations demonstrate that the proposed schemes significantly outperform conventional schemes that transmit private messages only using digital beamforming.

A cooperative cognitive radio for satellite networks is considered, in which the primary network is a satellite network and the secondary network is a cellular network. Due to the lack of multipath in a satellite environment, the channel matrices of the satellite network are assumed to be rank-deficient, which implies that the capacity cannot be increased in proportion to the number of antennas. To overcome the rank deficiency, we propose a novel cooperative transmission strategy where the base station or mobile users in the cellular network both help the communication of the satellite network and transmit and receive their own streams. Not only does the secondary network carefully adjust the number of transmitted streams to avoid causing interference to the primary network beyond a certain threshold; it also provides alternative signal paths for the primary network, thereby effectively increasing the channel ranks of the primary network. We obtain both the achievable sum degrees of freedom (DoFs) and the sum rate under the proposed scheme, and we also derive upper bounds on the sum DoF. Using the analytical and numerical analysis, we show that our scheme significantly improves the overall system throughput compared with the satellite network alone, without cognitive access.

In this paper, we introduce a network-decomposed hierarchical cooperation (HC) protocol and completely characterize the corresponding throughput-delay tradeoff for a large wireless ad hoc network formed in the context of social relationships. Instead of randomly picking source-destination pairings, we first consider a distance-based social formation model characterized by the social group density γ and the number of social contacts per node q where the probability that any two nodes in distance d away from each other are socially connected is assumed to be proportional to d-γ, which is a feasible scenario. Then, using muiltihop and network-decomposed HC protocols under our social formation model, we analyze a generalized throughput-delay tradeoff according to the operating regimes with respect to parameters γ and q in both a dense network of unit area and an extended network of unit node density via a non-straightforward network transformation strategy. Our main results reveal that as γ increases, performance on the throughput-delay tradeoff can remarkably be improved, compared to the network case with no social relationships. It is also shown that in the dense network, the network-decomposed HC protocol always outperforms the multihop protocol, while the superiority of the network-decomposed HC depends on γ and the path-loss exponent in the extended network.

An integer-forcing (IF) linear multiple-input multiple-output (MIMO) receiver has recently been proposed, which is theoretically shown to achieve near capacity with almost the same complexity as that of conventional linear receivers. The key idea is that the receiver attempts to directly decode integer-linear combinations of codewords. To ensure that this sum-decoding operation is feasible, in previous works, lattice codes over$\mathbb {Z}_{q}$were employed. Although those codes can attain good theoretical performance, however, its implementation complexity can be considerably high in practice, especially when$q$is large to support high-order modulations. In this paper, we propose a practical multilevel coding scheme for IF MIMO, in which multilevel encoding composed of binary linear codes$(q = 2)$in conjunction with the natural mapping is employed on the transmitter side and multistage decoding adapted to the IF operation is employed on the receiver side. The performance of the proposed scheme is extensively evaluated both analytically and numerically, showing that the gain of IF over conventional receivers is indeed achievable in practical settings with almost the same complexity. Our results imply that the proposed IF MIMO can be an attractive solution for the 5G communications due to its ability of supporting high spectral efficiency with low complexity.

The cost-effective impact and fundamental limits of infrastructure support with rate-limited wired backhaul links (i.e., GreenInfra support), directly connecting base stations (BSs), are analyzed in a large-scale hybrid network of unit node density, where multiantenna BSs are deployed. We consider a general scenario such that the rate of each BS-to-BS link scales at an arbitrary rate relative to the number of randomly located wireless nodes n. For the operating regimes with respect to the number of BSs and the number of antennas at each BS, we first analyze the minimum rate of each backhaul link CBS, required to guarantee the same throughput scaling as in the infinite-capacity backhaul link case. We then identify the operating regimes in which the required rate CBS scales slower than nϵfor an arbitrarily small ϵ>0 (i.e., the regimes where CBS does not need to be infinitely large). We also show the case where our network with GreenInfra is fundamentally in the infrastructure-limited regime, in which the performance is limited by the rate of backhaul links. In addition, we derive a generalized throughput scaling law including the case where the rate of each backhaul link scales slower than CBS. To validate the throughput scaling law for finite values of system parameters, numerical evaluation is also shown via computer simulations.

In this paper, we introduce a large-scale 3D erasure network, where n wireless nodes are randomly distributed in a cuboid of nλ× nμ× nνwith λ + μ + ν = 1 for λ, μ, ν > 0, and completely characterize its capacity scaling laws. Two fundamental path-loss attenuation models (i.e., exponential and polynomial power-law models) are used to suitably model an erasure probability for packet transmission. Then, under the two erasure models, we introduce a routing protocol using percolation highway in 3D space, and then analyze its achievable throughput scaling laws. It is shown that, under the two erasure models, the aggregate throughput scaling nmin{1-λ,1-μ,1-ν}can be achieved in the 3D erasure network. This implies that the aggregate throughput scaling n2/3can be achieved in 3D cubic erasure networks, while √n can be achieved in 2D square erasure networks. The gain comes from the fact that, compared with 2D space, more geographic diversity can be exploited via 3D space, which means that generating more simultaneous percolation highways is possible. In addition, cut-set upper bounds on the capacity scaling are derived to verify that the achievable scheme based on the 3D percolation highway is order-optimal within a polylogarithmic factor under certain practical operating regimes on the decay parameters.

We study the sum degrees of freedom (DoF) of interference channels with hybrid beamforming in which each transmitter i uses M'iantennas and MiRF chains and each receiver i uses N'iantennas and NiRF chains, where Mi≤ M'iand Ni≤ N'i, ∀i = 1, 2,..., K, and hybrid beamforming composed of analog and digital precoders is employed at each node. For the two-user case, we completely characterize the sum DoF for an arbitrary number of antennas and RF chains by developing an achievable scheme optimized for the hybrid beamforming structure and deriving its matching upper bound. For a general K-user case, we focus on a symmetric case where Mi= M, Ni= N, M'i= M , and N'i= N', ∀i = 1, 2, . . ., K, and obtain lower and upper bounds on the sum DoF, which are tight when max{M',N'}/min{M',N'} is an integer. The results show that with a fixed number of RF chains, employing more antennas can increase the sum DoF of interference channel under certain conditions while this cannot improve the sum DoFs of point-to-point channel, multiple access channel, and broadcast channel.

In this paper, we consider the power splitting relaying (PSR) and time switching relaying (TSR) protocols for decode-and-forward (DF)-based relay networks consisting of a source, a relay, and a destination. For the networks, the relay is assumed to have a rechargeable battery with a certain amount of remaining energy for energy harvesting through the received signal transmitted from the source. Specifically, for the PSR protocol with and without the direct-path, we present the outage probability expression with a given power splitting coefficient, and we obtain the optimum power splitting coefficient to maximize the transmission rate and derive its transmission rate and outage probability, where the optimum coefficient depends on channel conditions and remaining energy. For the TSR protocol with and without the direct-path, we present the outage probability expression with a given time switching coefficient, and we obtain the optimum time switching coefficient to maximize the transmission rate and derive its transmission rate, where the optimum coefficient also depends on channel conditions and remaining energy.

We consider heterogeneous network (HetNet) systems comprising of two different types of base-stations (BSs): macro and small BSs. In the previous studies for HetNet, it is observed that the distribution of signal to interference plus noise ratio (SINR) is independent of the BS density. This implies that network throughput increases linearly with the number of BSs. In this paper, however, we have identified key practical factors which degrade the SINR distribution for dense HetNet. On the contrary to the previous studies, limited network throughput is verified by evaluating the areal capacity with respect to the density of BS under the practical channel model. The observation is made regardless of the deployment scenario whether the two types of BSs share the same frequency band or not. Based on the analysis and the simulation results provided in this paper, we present principal guidelines for cell deployment and system operation to improve the areal capacity of dense HetNet systems.

Capacity scaling of a large hybrid network with unit node density, consisting of wireless ad hoc nodes, base stations (BSs) equipped with multiple antennas, and one remote central processor (RCP), is analyzed when wired backhaul links between the BSs and the RCP are rate-limited. We first derive the minimum backhaul link rate required to achieve the same capacity scaling law as in the infinite-capacity backhaul link case. Assuming an arbitrary rate scaling of each backhaul link, a generalized achievable throughput scaling law is then analyzed in the network based on using one of pure multihop, hierarchical cooperation, and two infrastructure-supported routing protocols, and moreover, information-theoretic operating regimes are identified. In addition, to verify the order optimality of our achievability result, a generalized cut-set upper bound under the network model is derived by cutting not only the wireless connections but also the wired connections.

The impact and information-theoretic limits of infrastructure support with rate-limited wired links are analyzed in hybrid ad hoc networks, where multi-antenna base stations (BSs) are deployed and the rate of each BS-to-BS link scales at an arbitrary rate relative to the number of randomly located wireless nodes. For the operating regimes with respect to the number of BSs and the number of antennas per BS, we first analyze the minimum rate of each BS-to-BS link, Cbs, required to guarantee the capacity scaling for the network using infinite-capacity backhaul links. We then identify the operating regimes in which the required rate CBs scales much slower than 1. We also show the achievable throughput scaling for the case where the rate of each BS-to-BS link scales lower than CBs.

In this paper, a relay precoding problem is considered in a non-regenerative multiple-input multiple output (MIMO) relay system, when multiple interferers exist near the destination. The relay has the perfect channel state information (CSI) of the source-relay link and only the covariance information of the relay-destination link. Also, we assume that the training signals of the interferers are known at the destination, and thus, the covariance information of the channels from the interferers to the destination can be estimated at the destination and the information is fed back to the relay. For this scenario, the structure of the optimal relay precoder is derived to maximize the average capacity seen by the relay under a relay transmit power constraint. For the derivation of the optimal relay precoder, a new partial ordering result for the outage probability and the ergodic capacity of spatially correlated MIMO channels is derived. Numerical results demonstrate that the proposed scheme considerably improves the performance. Overall, the contributions of this paper are twofold: i) a new partial ordering result for MIMO channels is derived and ii) the structure of the optimal relay precoder is derived using the partial ordering result.

We consider a relay precoding problem in a non-regenerative multiple-input multiple-output (MIMO) relay system, where the relay has the perfect channel state information of the source-relay link and only the covariance information of the relay-destination link. The average capacity seen by the relay is maximized under a relay transmit power constraint. The relay precoding problem is transformed to its equivalent power allocation problem at the relay. The optimal power allocation solution is then derived by formulating the optimization problem via difference of convex (DC) functions programming. To reduce the computational complexity, we also propose a suboptimal scheme by modifying the naive Jensen method. Furthermore, we propose the maximum eigenmode relaying (MER) scheme, which allocates the relay transmit power only to the maximum eigenmode. A necessary and sufficient condition for the MER to be asymptotically optimal is derived in the high signal-to-noise ratio (SNR) regime of the source-relay link. The performance of the proposed schemes is investigated through extensive numerical simulations.

In this paper, the optimal power allocation problems are solved for hybrid distributed beamforming in wireless relay networks. The capacity of the source-destination link is maximized and the total transmit power of the source and the relays is minimized. The hybrid relaying which adapts relaying strategies such as amplify-and-forward (AF) and decode-and-forward (DF) to the channel condition can enhance the signal reliability. Three policies are used to determine the ordering of the relays for switching the relaying mode from AF to DF. Simulation results show that the performance of the proposed scheme is nearly optimal for both capacity maximization and power minimization with a few iterations.

Multihop cellular networks (MCNs) is an extension of single hop cellular networks (SCNs) having benefits of a fixed base station and flexible ad hoc networks. The radio resource management becomes more complex to deal with the routing and resource allocation jointly. In this paper, we have proposed the algorithm which solves the joint subchannel allocation, routing and power allocation problem in OFDMA MCNs. Optimization problem has been decomposed into two subproblems: (1) subchannel allocation problem; (2) routing and power allocation problem, and solved by iterative two-step approach. The simulation results showed that the average total utility increases and the increment of the utility decreases as the number of hops increases.