Search

In this paper, a robust transmit power control scheme is proposed for multi-channel underlay device-to-device (D2D) communications with imperfect channel state information (CSI). The transmit power of the D2D user equipment (DUE) on each channel is optimized to maximize the average spectral efficiency (SE) whilst maintaining the quality-of-service (QoS) of the cellular user equipment (CUE) in the presence of errors in the CSI. To this end, we propose a novel deep neural network (DNN) structure and training methodology, in which artificially distorted CSI is used to compensate for the effect of imperfect CSI, such that a robust transmit power control strategy against channel error can be derived. Our simulation results show that even when the CSI is inaccurate, in our proposed scheme the degradation of the average SE can be kept low whilst maintaining negligible QoS violation, thereby confirming its effectiveness and robustness.

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN.

In this paper, we consider a wireless-powered two-way communication, called transmit-harvest-respond , with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii) receivers transmit responses back to transmitters using this harvested energy. We aim to find the transmit power and energy harvesting ratios that maximize the sum rate of the forward links while ensuring a minimum rate requirement for each backward link. Due to the non-convexity and NP hardness of the optimization problem considered here, we first derive suboptimal solutions using an iterative algorithm (IA) on the basis of asymptotic strong duality. In view of the high computation time of the IA, we then design an efficient deep neural network (DNN) framework and novel training strategy as a means of combining supervised and unsupervised training. Specifically, DNNs are pre-trained using the suboptimal solutions obtained by the IA in a supervised manner, as a means of initialization; further training is then applied to DNNs using a well-designed loss function in an unsupervised manner to enhance performance. Simulation results reveal that the pre-training technique using IA solutions is beneficial for improving the performance of the DNN. The proposed hybrid scheme thus achieves near-optimal performances with a lower computation time, compared with the use of IA or DNN alone.

Given the ever increasing use of near field communication (NFC), the security of this system is becoming increasingly important. Recently, radio frequency (RF) fingerprinting, where the physical RF characteristics of a communication device are used as a means to provide guarantees of authenticity and security, has received serious consideration due to the uniqueness of these characteristics, making cloning difficult. In this article, we discuss the feasibility of RF fingerprinting assisted by deep learning for use in identifying NFC tags. To this end, we implement a hardware testbed with an off-the-shelf NFC reader and software defined radio. An RF signal corresponding to one-bit transmission from the NFC tag is used to extract the RF characteristics, which enables rapid identification. Three different types of deep neural network are used, namely fully connected layer-based neural network” convolutional neural network, and recurrent neural network. By exper-iment, we confirm that deep-learning-based algorithms can uniquely distinguish 50 NFC tags with up to 96.16 percent accuracy. We also discuss some of the key technical challenges involved in the use of deep-learning-based RF fingerprinting for NFC.

In this paper, we investigate deep learning-aided distributed transmit power control in the context of an underlay cognitive radio network (CRN). In the proposed scheme, the fully distributed transmit power control strategy of secondary users (SUs) is learned by means of a distributed deep neural network (DNN) structure in an unsupervised manner, such that the average spectral efficiency (SE) of the SUs is maximized whilst allowing the interference on primary users (PUs) to be regulated properly. Unlike previous centralized DNN-based strategies that require complete channel state information (CSI) to optimally determine the transmit power of SU transceiver pairs (TPs), in our proposed scheme, each SU TP determines its own transmit power based solely on its local CSI. Our simulation results verify that the proposed scheme can achieve a near-optimal SE comparable with a centralized DNN-based scheme, with a reduced computation time and no signaling overhead.

In this letter, we propose a hybrid resource allocation scheme for multi-channel underlay device-to-device (D2D) communications. In our proposed scheme, the transmit power of D2D user equipment (DUE) allocated to each channel is controlled in order to maximize the sum rate of the DUEs for a given Quality of Service (QoS) constraints. We consider two QoS constraints such that the interference caused on cellular user equipment (CUE) is kept to be less than a predefined level and the rate of individual DUE is managed to be larger than a predefined threshold. In order to solve the drawbacks associated with previous deep neural network (DNN)-based approaches in which QoS constraints could be violated with high probability, a heuristic equally reduced power (ERP) scheme, is utilized together with a DNN-based scheme. By means of simulations under various environments, we verify that the proposed scheme provides a near-optimal sum rate while guaranteeing the QoS constraints with a low computation time.

In this article, we consider the joint optimization of transmit power and power splitting ratio to maximize the energy efficiency in a simultaneous wireless information and power transfer based interference channel, in which receivers use a power splitting policy to harvest energy from a wireless signal. We propose an optimization-based iterative algorithm (O-IA) from well-known optimization techniques as a comparative scheme, and also devise a neural network based learning algorithm (NN-LA) to deal with nonconvexity caused by cochannel interference among multiple nodes. Through simulations, we provide a comparative study of the two approaches in terms of energy efficiency and time complexity. In particular, we find that NN-LA achieves a near-optimal energy efficiency, whereas its time complexity is significantly reduced, in comparison with O-IA.

We propose a deep spread multiplexing (DSM) scheme using a DNN-based encoder and decoder and we investigate training procedures for a DNN-based encoder and decoder system. Multiplexing for multiple orthogonal resources is designed with an autoencoder structure, which originates from the deep learning technique. Furthermore, we investigate training methods that can leverage the performance in terms of various aspects such as channel models, training signal-to-noise (SNR) level and noise types. The performance of these factors is evaluated by training the DNN-based encoder and decoder and verified with simulation results.

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.

Deep learning continues to offer groundbreaking technologies in communications systems just as it has in a variety of other fields. Despite the notable advancements of technologies based on DNN-based technologies in recent years, their high computational complexity has been a major obstacle to the application of DNN in practical communications systems where real-time operation is required. In this sense, challenges regarding its practical implementation must be addressed before the proliferation of DNN-based intelligent communications becomes a reality. To the best of the authors' knowledge, the present article is the first to present an efficient learning architecture and related design strategies including link-level verification through the implementation of digital circuits using hardware description language (HDL) to mitigate this challenge and to deduce the feasibility and potential of DNNs in communications systems. In particular, a DNN is applied to an encoder and a decoder to enable flexible adaptation with respect to their system environments, without the need for any domain-specific information. Extensive investigations and interdisciplinary design considerations, including a DNNbased autoencoder structure, learning framework, and implementation of a low-complexity digital circuit for real-time operation, are taken into account, all of which support the use of DNNbased communications in practice.

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS.

A transmit power control strategy using a deep neural network (DNN) is proposed for underlay device-to-device (D2D) communication where D2D user equipment (DUE) shares radio resources with cellular user equipment (CUE). In this scheme, a transmit power control strategy for DUE is found with the aid of a newly proposed DNN structure. Both the spectral efficiency (SE) of the DUE and the amount of interference at the CUE are taken into account, such that the SE of the DUE can be improved while alleviating any deterioration in the cellular transmission. Using simulations, we show that the proposed scheme can achieve a high SE of the DUE while properly regulating the interference caused to the CUE, with a low computation time.

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized to maximize the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communications systems with different design objectives.

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

As multimedia traffic has been increasing and is expected to grow more sharply, various technologies using caches have been attracting lots of attention. As one breakthrough technology to deal with the explosively growing traffic, exclusive OR (XOR)-based index coding has been widely investigated because it can greatly enhance the efficiency of network resource by reducing the number of transmissions. In this paper, we investigate how to apply XOR-based index coding to large-scaled practical streaming systems for video traffic that accounts for more than 70% of total Internet traffic. Contrary to most previous studies that have focused on theoretical analysis of optimal performance or development of optimal index coding schemes, our study proposes a new XOR coding-based video streaming (XC). We also propose a new grouping algorithm for creating XC groups while guaranteeing the complete backward compatibility of XC with existing streaming schemes such as unicast (UC), multicast (MC), and broadcast (BC). The performance of the proposed scheme is analyzed in two steps. First, the behavior of video contents in caches at clients is modeled as a Markov chain, and the steady-state probabilities and caching probabilities for each piece of video content are derived. Based on the probabilities, the performance of the proposed system is then analyzed in terms of the average number of connections that each client requires in order to receive one video content. Our numerical results show that the proposed video streaming scheme using XC can reduce the average number of transmissions by up to 18%, compared to the conventional scheme.

In this letter, a resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU). In the proposed scheme, the allocation of transmit power in each channel for SUs is found by utilizing the newly proposed DNN model, which separately determines the overall transmit power of individual SUs and the proportion of transmit power allocated to each channel. Both the spectral efficiency (SE) of the SU and the amount of interference caused to the PU are considered in the training of the DNN model, such that the interference caused to the PUs can be properly regulated while the SE of the SU is improved. Through simulations, we show that our scheme enables a high SE of the SU to be achieved while the interference caused to the PU can be maintained at less than the threshold.

In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing. We also propose a form of DPC that can be performed in a distributed manner with local channel state information, allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.

In this paper, we propose an enhanced signal-to-generating interference ratio (SGIR)-based resource scheduling scheme to reduce the feedback overhead in backhaul links for frequency division duplex (FDD) uplink networks. In the proposed scheme, each base station (BS) transfers only the information of dominant interference channels greater than a given threshold instead of the whole interference channels. We analyze the performance of the proposed scheme in terms of the amount of feedback reduction in the backhaul links and average uplink sum-rate. In addition, we derive a closed-form solution to determine an adequate threshold level. The numerical results show that the proposed scheme can significantly reduce the amount of feedback traffic in the backhaul links, while yielding almost the same average uplink sum-rate.

Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.

High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.

A pricing-based distributed spectrum access technique for cognitive radio (CR) networks which adopt the geolocation database (GD) is proposed. The GD contains which frequency bands are occupied by the primary system in a particular location. Given that multiple CR systems may attempt to transmit data over the same frequency band when the GD is used, the achievable rate of the CR systems becomes deteriorated due to interference. In the proposed technique, each (secondary) CR system determines whether it utilises vacant frequency bands by considering the cost of using them, which is calculated by taking into account the interference. The authors analyse the behaviour of the CR systems based on game theory. In particular, it is shown that the sum capacity of CR systems is maximised when the number of utilised bands is proportional to the relative channel gain with respect to the average channel gain at each CR system. In addition, the authors obtain the optimal cost for the vacant bands, which achieves the maximum sum capacity of CR systems. Finally, it is shown that the sum capacity of the (secondary) CR systems is significantly improved via proper pricing policy on the vacant frequency bands through extensive computer simulations.

Due to the increasing popularity of device-to-device (D2D) services, it is becoming increasingly important to find efficient means of identifying nearby users, i.e., by peer discovery. Herein, we propose a low-power peer discovery scheme, in which we exploit the tradeoff between the power consumption and the accuracy/scope of peer discovery. In our proposed scheme, the transmission of a proximity beacon is scheduled based on channel values, such that users in close proximity are likely to transmit beacons at similar time instants due to the spatially correlated wireless channel. As a result, users can find nearby peers accurately with a lower power consumption by shortening the reception period for the beacons. The performance of the proposed scheme, in terms of accuracy and power consumption, is derived. By means of simulation results, we show that nearby users can be found with lower power consumption than in conventional schemes, while achieving a high accuracy of peer discovery.

To mitigate inter-cell interference, which causes throughput degradation for cell-edge users, a shared relay with sector antennas that are connected to neighboring base stations (BSs) with optical cable is deployed in the cell boundary. Each sector of the shared relays can be considered a remote antenna directed to the connected BS. By controlling the transmit power, inter-cell interference can be mitigated and high-throughput performance can be achieved. Additionally, better handover performance can be obtained by reducing unnecessary handovers caused by ambiguity in the cell boundary. Through computer simulations with the hexagonal cell structure and the proposed shared relay with three sectors, performance gain of the proposed system deployment was verified.

In this paper, we consider underlay cognitive radio (CR) networks where an amount of interference caused by secondary stations (STAs) has to be kept below a predefined level, which is called interference temperature. We propose opportunistic p-persistent carrier sense multiple access schemes for the CR networks, which opportunistically exploit wireless channel conditions in transmitting data to the secondary access point. We also devise an adaptive interference-level control technique to further improve quality-of-service of a primary network by limiting the excessive interference due to collisions among STAs. The performances of the proposed schemes are mathematically analyzed, and they are validated with extensive computer simulations. The simulation results show that the proposed schemes achieve near optimal throughput of the secondary network while they are backward-compatible to the conventional p-persistent CSMA scheme.

Multihop cellular networks offer a cost-effective solution to the problem of achieving higher capacity and extending coverage by using relay stations (RSs) deployed within the coverage area of a base station (BS). We herein consider directional transmission based on adaptive beamforming in which multiple users are served at the same time using multiple directional beams; this represents one promising technology that could improve the capacity of multihop cellular network. In directional transmission, severe intra-BS/RS, inter-BS---RS, and inter-RS interferences can occur due to the concurrent transmission and accordingly, system performance can be degraded severely. In this paper, we propose resource allocation to maximize the system capacity of multihop cellular networks by taking into account these severe interferences caused in multihop cellular network using directional transmission. To alleviate the problem of high computational complexity, we propose a sub-optimal scheme. A heuristic scheme is also proposed which can be performed in distributed manner with lower computational complexity. Through simulation results, we show that the use of our proposed schemes increases system capacity.

We investigate mobile device-to-device (D2D) caching networks where mobiles called receiving devices (RDs) can receive data directly from caching server devices (CSDs). In mobile D2D caching networks, the quality of communications can be greatly enhanced because end-to-end communication ranges are shortened compared to cellular communications. In this paper, we propose a completely distributed transmission algorithm for mobile caching networks (DMC) based on signal-to-generating interference ratio (SGIR) that can eliminate overhead for signaling and feedback. In the DMC, each CSD autonomously chooses an RD to transmit data to and the CSD multicasts the data to RDs that requested the same data. One CSD is only activated to transmit data to each RD in a distributed manner based on timers. Our numerical results show that the DMC can yield comparable average sum-rates to a centralized scheme in various environments with no extra overhead.

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.

We compare the capacity of two most popular methods for acquiring the state of the spectrum in cognitive radio technology: geolocation-database-based schemes and spectrum-sensing-based schemes. For the comparison, we use a new Hidden Markov Chain based channel model, because recent measurements show that a conventional two-state model, which has been widely used, is not appropriate for modeling the behavior of channels in the cognitive environment. We also consider more generic cognitive environments in which each wireless system has its own licensed bands and uses unlicensed bands in addition to its licensed bands to increase its capacity. This type of wireless systems can comprise conventional cognitive radio systems by letting the number of licensed bands be zero. Moreover, we have derived the optimal number of unlicensed bands to be used for maximizing the capacity of wireless system by taking into account interference from neighboring wireless systems. Through simulations, we compare the capacity of wireless systems with two channel state acquisition schemes and show the counterbalancing relation between two schemes, which has never been investigated in previous works. To the best of our knowledge, this is the first work to compare the capacity of these two schemes for acquiring the state of the spectrum.

Recently, mobile communication systems are suffering from exponentially increasing data traffic. As a promising solution to the increase in data traffic, a coordinated multi-point transmission and reception (CoMP) scheme has been proposed. Although a great deal of research has been done on this new technology, the performance of mobile communication systems with CoMP has not been evaluated properly in a typical indoor environment. To address this, we have developed a system-level simulator and evaluated the performance of mobile communication systems with CoMP. Unlike previous works, we have used an actual antenna pattern in our simulator and link-level results are properly taken into account through link-level abstraction. By using a system-level simulator, we have evaluated the performance of mobile communication systems with CoMP in an indoor environment and found that unlike an outdoor cellular environment, CoMP may not improve the performance of overall mobile communication systems in an indoor environment.

We consider a channel state acquisition scheme in the cognitive radio (CR) ad hoc system. Unlike cellular CR systems, the channel state acquisition scheme, which uses a geolocation database, is hard to implement in an ad hoc CR system. Hence, we consider hybrid channel state acquisition schemes that use cooperative multiband sensing to build a virtual spectrum database for ad hoc CR systems, which we refer to as the spectrum pool. Then, we compare three schemes, i.e., a sensing scheme and two hybrid schemes, in which the spectrum information or the band can be cooperatively shared among CR users without using a dedicated common control channel. By using the spectrum pool, the CR users can have more information on the spectrum compared with the case when there is no channel state sharing. Therefore, the number of spectrum sensing attempts to find a vacant band that can be reduced, and the performance of CR systems can be improved. Through simulation, we show that our analysis results are well matched to simulation results. We also find that the throughput can be improved by utilizing the spectrum pool.

We propose a new cooperative spectrum sensing for cognitive radio (CR) systems that is designed to detect low-power primary users (PUs). In previous works, it has been assumed that all cognitive terminals (CTs) are within the no-talk zone of PU, which is the region that a CT should not use the band of PU when it lies within the region. However, when the PU transmits at low power, only some CTs will lie within the no-talk zone. It is therefore clear that low-power PUs cannot be protected adequately when conventional sensing schemes are used. To solve this problem, we propose a new scheme in which CTs perform sensing in a number of different stages. Through performance analysis and simulations, we show that our proposed scheme can detect low-power PUs with high accuracy, with the result that the PUs can be protected properly. We also show that the throughput of CR systems can be improved.

We propose a distributed scheduling algorithm for wireless ad hoc systems that use cooperative transmission in which multiple cooperative relays can transmit at the same time by using an amplify-and-forward (AF) scheme or a decode-and-forward (DF) scheme. Our proposed scheme is a carrier-sense-multiple-access-like (CSMA-like) scheduling algorithm, which uses simultaneous request-to-send (RTS) and sequential clear-to-send (CTS) (SRSC) transmission in which multiple transmitters broadcast RTS at the same time, and the power of CTS transmission changes dynamically according to the relaying scheme used and the number of cooperative relays. Therefore, each cooperative transmission set can recognize efficiently whether its transmission will interfere with neighboring ongoing transmissions. Through performance evaluation by simulation, we show that the proposed scheme can enhance the performance of wireless ad hoc systems that use cooperative transmission.

Cognitive radio (CR) is a promising technology for overcoming the lack of available communication bands. In CR technology, spectrum sensing is an important issue, which has recently been extensively studied. We provide a solution to the spectrum-sensing problem for multiple cognitive terminals (CTs) that takes into account the difference among CTs with respect to the probabilities of a false detection and a missed detection. We optimize the spectrum-sensing performance by differentiating the number of spectrum-sensing operations that each CT performs. This has not previously been proposed in the literature. Moreover, we use a simultaneous spectrum-sensing and data transmission scheme that utilizes multiple-input-multiple-output (MIMO) antenna technology. As a result, the degradation of quality of service (QoS) that is caused by spectrum sensing can be reduced, and the throughput of CR systems can be increased, while maintaining the accuracy of spectrum sensing. Through performance analysis, we show that our proposed scheme can achieve the desired levels of performance with respect to the probabilities of a false detection and a missed detection and improve the performance of the CR systems with respect to throughput and delay.

We propose a group handover scheme for multiaccess networks that uses adjusted delay to reduce the number of handover blockings that occur during group handover. In a group handover, many users simultaneously start the handover process, which causes network congestion and increases the number of handover blockings. In the proposed scheme, each user in the group handover selects a base station (BS) on the basis of the remaining bandwidth of the BS at that time and initiates the handover process after adjusted delay. To find a strategy for selecting BSs, an optimization problem whose objective is to minimize the probability that handover blocking will occur is formulated, and a solution is derived by using the Karush-Kuhn-Tucker condition. Through performance analysis and simulation results, we show that the probability that handover blocking will occur during group handover is less in our proposed scheme than in a conventional scheme.

We propose a velocity estimation scheme for mobile stations (MSs) in cellular communication systems that uses the azimuth spread (AS), delay spread (DS), and shadow fading of MSs. Due to the fact that the AS, DS, and shadow fading are correlated in the spatial domain, the variation of the AS, DS, and shadow fading of an MS is related to its velocity. And, we also consider the variation of velocity which was not considered in previous velocity estimation studies. Through analysis and numerical results, we show that we can estimate the velocity of an MS with lower estimation error compared to previous estimation schemes, despite of measurement error.