dently designed. Regular JSCC setup is very effective for multimedia transmissions because of its ability to trade off between distortion and compression rate to reduce consumption of bandwidth [20]. This setup is similar to those proposed by Chaoub and Ibn-Elhaj [14], Mohr and Riskin [2], and Chande and Farvardin [18]. However, Chaoub and Ibn-Elhaj’s model operates under the interweave DSA paradigm, where the secondary network is ”opportunistic” in its transmissions. This scheme finds spectrum holes through the estimation of the primary network’s arrival as a Poisson process. The setup in this thesis operates under the **underlay** DSA paradigm and can transmit at any time and at any frequency band, providing that the interference temperature caused at the primary network is kept at a reasonable level. Mohr and Riskin uses only a single **channel** while incorporating different **channel** loss profiles such as uniform, binomial, and exponential distributions [2]. This thesis allows for multi-**channel** transmissions. Finally Chande and Farvardin utilizes rate-compatible punctured convolutional codes as **channel** codes so that the **transmission** is optimized at any **transmission** rate; whereas this work uses Reed-Solomon codes.

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Abstract: This paper investigates the performance of a 2- Dimensional (2D) **Joint** **Source** **Channel** **Coding** (JSCC) system assisted with parameter estimation for 2D **image** **transmission** **over** an Additive White Gaussian Noise (AWGN) **channel** and a Rayleigh fading **channel**. Baum-Welsh Algorithm (BWA) is employed in the proposed 2D JSCC system to estimate the **source** correlation statistics during **channel** decoding. The **source** correlation is then exploited during **channel** decoding using a Modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The performance of the 2D JSCC system with the BWA-based parameter estimation technique (2D-JSCC-PET1) is evaluated via **image** **transmission** simulations. Two images, each exhibits strong and weak **source** correlation are considered in the evaluation by measuring the Peak Signal Noise Ratio of the decoded images at the receiver. The proposed 2D-JSCC-PET1 system is compared with various benchmark systems. Simulation results reveal that the 2D-JSCC-PET1 system outperforms the other benchmark systems (performance gain of 4.23 dB **over** the 2D- JSCC-PET2 system and 6.10 dB **over** the 2D JSCC system). The proposed system also can perform very close to the ideal 2D JSCC system relying on the assumption of perfect **source** correlation knowledge at the receiver that shown only 0.88 dB difference in performance gain.

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Progressive coded images, such as those compressed by wa- velet-based compression methods, have wide application in- cluding **image** communications via band-limited wireless channels. Due to the embedded structures of the correspond- ing compressed codestreams, **transmission** of such images **over** noisy channels exhibits severe error sensitivity and al- ways experiences error propagation. Forward error correc- tion (FEC) is a typical method used to ensure reliable trans- mission. Powerful capacity-achieving **channel** codes such as turbo codes and low-density parity-check (LDPC) codes have been used to protect the JPEG2000 codestream using various methods [1–3]. The typical idea of these schemes is to assign di ﬀ erent **channel** protection levels via **joint** **source**-**channel** **coding** (JSCC) based on a rate distortion method. In addition to JSCC systems that are designed at the transmitter/encoder side, researchers also find that **joint** **source**-**channel** decoding (JSCD) can be achieved at the re- ceiver/decoder side. The concept of utilizing **source** decoded

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For the past decades, Shannon’s separation principle has stood for a justification for separate **source** and **channel** **coding**. However, this result holds true only for infinite **source** code di- mension and infinite long **channel** code. Significant progress has been made throughout last decades to optimise each indi- vidual module of communication systems. The innovative next generation of mobile network will be globally integrated archi- tecture where individual modules are jointly designed to en- able simultaneous optimisation of bandwidth as well as Quality of Service (QoS). Since ideal hypotheses of separate **source**- **channel** **coding** (SSCC) put unrealistic constrains on the sys- tem, a **joint** **source**-**channel** **coding** (JSCC) design may reduce complexity and delay to yield better end-to-end system perfor- mance.

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The performance of the proposed JSCC framework has been extensively evaluated using the wavelet-based SVC codec [18]. For the proposed JSCC UEP optimal **channel** rate, packet size and interleaver for DBTC were estimated and used as described in this paper. The proposed technique is denoted as “ODBTC.” In this paper, DVB, ARP, and DRP in- terleavers, **channel** rates (1 / 3, 2 / 5, 1 / 2, 2 / 3, 3 / 4, 4 / 5, and 6 / 7) and packet sizes (16, 55, 110, 188, 216) in bytes are consid- ered for ODBTC. Max-log-MAP algorithm produces approx- imately the same result as the MAP algorithm for DBTC, as reported in [22]. That means, the decoding complexity can be decreased without any significant loss of performance for DBTC by using Max-log-MAP algorithm. For this rea- son, the Max-log-MAP algorithm is used in ODBTC. Two other advanced JSCC techniques were integrated into the same SVC codec for comparison. The first technique used serial concatenated convolutional codes of fixed packet size of 768 bytes and pseudo random interleaver [15]. It is de- noted as “SCTC.” Since product code was regarded as one of the most advanced in JSCC, the technique using product code proposed in [12] was used for the second comparison. This product code used RS codes as outer code and turbo codes as inner code [12], so it is denoted by “RS + TC” in this paper. It is noticeable that this scheme was initially tar- geting wavelet-based **image** **transmission**. Nevertheless it is very straightforward to extend them to video **transmission** by replacing the **image** subbands using quality layers of scal- able video in RS + TC. The corresponding parameters in [12] were adopted for video in RS + TC in this paper.

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This paper examines erasure resilience of oversampled filter bank (OFB) codes, focusing on two families of codes based on cosine- modulated filter banks (CMFB). We first revisit OFBs in light of filter bank and frame theory. The analogy with **channel** codes is then shown. In particular, for paraunitary filter banks, we show that the signal reconstruction methods derived from the filter bank theory and from **coding** theory are equivalent, even in the presence of quantization noise. We further discuss frame properties of the considered OFB structures. Perfect reconstruction (PR) for the CMFB-based OFBs with erasures is proven for the case of erasure patterns for which PR depends only on the general structure of the code and not on the prototype filters. For some of these erasure patterns, the expression of the mean-square reconstruction error is also independent of the filter coeﬃcients. It can be expressed in terms of the number of erasures, and of parameters such as the number of channels and the oversampling ratio. The various structures are compared by simulation for the example of an **image** **transmission** system.

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Motivated by recent results in **Joint** **Source**/**Channel** **coding** and decoding, we consider the decoding problem of Arithmetic Codes (AC). In fact, in this article we provide different approaches which allow one to unify the arithmetic decoding and error correction tasks. A novel length-constrained arithmetic decoding algorithm based on Maximum A Posteriori sequence estimation is proposed. The latter is based on soft-input decoding using a priori knowledge of the **source**-symbol sequence and the compressed bit-stream lengths. Performance in the case of **transmission** **over** an Additive White Gaussian Noise **channel** is evaluated in terms of Packet Error Rate. Simulation results show that the proposed decoding algorithm leads to significant performance gain while exhibiting very low complexity. The proposed soft input arithmetic decoder can also generate additional information regarding the reliability of the compressed bit-stream components. We consider the serial concatenation of the AC with a Recursive Systematic Convolutional Code, and perform iterative decoding. We show that, compared to tandem and to trellis-based Soft-Input Soft-Output decoding schemes, the proposed decoder exhibits the best performance/ complexity tradeoff. Finally, the practical relevance of the presented iterative decoding system is validated under an **image** **transmission** scheme based on the JPEG 2000 standard and excellent results in terms of decoded **image** quality are obtained.

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In [6], **channel** assignment in cellular communications is addressed to maximize the frequency spectrum utilization and to minimize the frequency interference effect. However, in **cognitive** **radio** secondary users can access the spectrum bands that are not used by primary users [1]. Spectrum sensing detects the availability of spectrum bands. Spectrum bands available at the secondary users may not be the same in most of the cases [7]. Secondary users located at different locations can have different sensing results. If no common band is available between the two **cognitive** users, then the communication is established between them using relay discussed in [8]. The power allocation issues in CR systems attract a lot of attention because performance of the CR system is improved by properly allocating the power [9]. In [10], power is allocated separately for **source** node and relay node for a cooperative relay in **cognitive** **radio** **networks**, when multiple spectrum bands are available at secondary users. However, power and **channel** allocation is only on the single cast instead of the multi cast **transmission** model. In [11], **joint** relay selection and power allocation scheme is addressed to maximize the capacity in single cast system. In [12], iterative algorithm is developed to allocate the power for the **source** node and relay node jointly in physical layer network **coding**, however, the system is not considered for **Cognitive** **Radio** network and there is no primary interference limit constraints in the optimization problem.

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In our project we consider the **joint** **source**-relay beamforming design for the three-node MIMO DF relay network with **source**-destination direct link.We assume that both the **source** and relay nodes are equipped with multiple antennas while the destination node is only deployed with single antenna. Such a **transmission** scenario is readily applicable to the downlink **transmission** of a relay-enhanced cellular system. Unlike existing work with MIMO DF relay channels, which relies an complex numerical solutions , we try to derive the explicit expressions for the optimal beamforming design for our concerned model . Specifically we identify several unique properties of the optimal solutions through mathematical derivation, based on which we develop a systematic approach to arrive at the optimal beam forming vectors for the **source** and relay nodes for different system configurations. This is because the MIMO **channel** between **source** and the relay nodes and the multiple-input multiple-output (MISO)**channel** between the **source** and the destination nodes have to be jointly considered and balanced.

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optimization for a single unit data. Furthermore, the authors derived a practical algorithm to address a variety of **transmission** scenarios such as sender driven or receiver driven trans- mission, and streaming **over** the best effort network. This algorithm used a Markov decision process framework, where the cost-error model differentiate different **transmission** scenar- ios from each other. The expected rate-distortion optimization was done by using a general iterative algorithm to form a convex hull that can be solved by Lagrange multiplier opti- mization technique. Furthermore, authors assume a fix short delay (up to several seconds) in the beginning of the data **transmission** that is independent of the length of the presenta- tion. In addition, their rate optimization system is primarily based on the usage of buffer and playback on the receiver side, where incremental redundancy can be more suitable for a more constrained delay and buffer space. By considering the distortion that is suffered by each single data unit independent of each other and developing an error-cost model based on each frame, concluding the optimal rate-distortion will be approximation of the optimal scenario (scenario that studies the affect of error on current frame to the future frame).

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Robust beamformer design with imperfect CSI has received a considerable attention recently. Usually, this problem is tackled by either worst-case optimization [12-27] or stochastic optimization [24,28]. In worst-case optimization (or maximin optimization), the uncertain parameters can take some given set of possible values, but without any known distribution. Then the optimiza- tion variables are designed in such a way that an objective value is maximized while guaranteeing the feasibility of the constraints **over** the given set of possible values of the parameters. This method has been applied to design the robust beamforming vectors for **underlay** CRNs in [20- 26], where the **channel** errors are either norm bounded or bounded by ellipsoids. With the exception of [24] and [26], most of the abovementioned work consider a CRN where a single secondary transmitter (TX) co-exists with a primary network. The problem of maximizing the minimum signal-to-interference-plus-noise ratio (SINR) in an **underlay** CRN, where the transmitter communi- cates with multiple secondary receivers (RXs) is studied in [21]. An iterative solution has been proposed based on semidefinite relaxation [29], and if the solution is not rank-one, rank-one approximations [29] have to be used to achieve the beamforming vectors. For the same problem, a method to achieve a rank-one solution with some tol- erance is presented in [22]. Therefore, none of the above work guarantee the optimal solution of the problem of maximizing the minimum SINR in an **underlay** CRN.

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In this paper, we study the communication **over** a secure multi-input single-output (MISO) **cognitive** **radio** channels, where the multi antenna secondary user transmitter (SU-TX) sends a confidential message to the legitimate secondary user receiver (SU-RX) without affecting the QoS of the PU in the presence of passive eavesdropper, where the CSI of the eavesdropper is not known at the SU-TX. To provide secure communication in CRNs, we propose two beamforming schemes. The first is transmit beamforming (BF) toward the SU-RX, where no AJ is used at the SU-TX. Then, we consider beamforming with jamming (BFJ), where the transmitted power is divided between the information and jamming signals. The comparison between different schemes is investigated. The proposed techniques exploit the randomness of wireless **channel** as a means of ensuring the secrecy of wireless communication. The performance of the proposed system is analyzed in terms of the achievable secrecy rate and the secrecy outage probability.

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This paper investigates the uplink achievable rate of secondary users (SUs) in **underlay** orthogonal frequency division multiplexing based **cognitive** **radio** **networks**, where the SUs randomly access the subcarriers of the primary network. In practice, the primary base stations (PBSs), such as cellular base stations, may not be placed close to each other to mitigate the interferences among them. In this regard, we model the spatial distribution of the PBSs as a β -Ginibre point process which captures the repulsive placement of the PBSs. It is assumed that in order to alleviate the

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300 Kbps, frame rate of 30 f/s, and MB loss rate of 10%. In Figure 3b, the sequence Salesman is encoded in the same way. It can be noted that the HP-based estimation is more accu- rate to estimate the actual distortion at the decoder compared to the IP-based estimation. Figure 4 also shows the average PSNR of the 150 coded frames with respect to MB loss rates from 5% to 20%. When MB loss rate is as small as 5%, the HP-based estimation is almost the same as the actual distor- tion, while the IP-based method has about 3 dB diﬀerence. The results is as expected since there is about 2–4 dB PSNR di ﬀ erence between HP- and IP-based video **coding** e ﬃ ciency given the same bit rate. As the MB loss rate increases as large as 20%, the HP-based estimation is about 1 dB better than the actual distortion, while the IP-based estimation is about 2 dB worse. So the HP-based method is still 1 dB more ac- curate than the IP-based method. The reason is that the er- ror propagation e ﬀ ects play a more significant role when MB loss rate gets larger, so the **coding** gain of the HP-based mo- tion compensation is reduced. Also, the assumption in HP- based method that the **transmission** and propagation errors are not correlated and zero mean may become loose. For practical scenarios, it is demonstrated that the HP-based esti- mation outperforms the original IP-based method by about 1–3 dB.

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In our simulation, 10 different topologies are created using 10 different seeds. The results of our simulation show the average values of the 10 different seeds. The simulation is implemented in the C programming language and run on the Linux operating system. The traffic is assumed to be uniformly distributed across all nodes with various overall loads. The number of new connections per second is given as an arrival rate. The number of terminated connections per second is given as a departure rate. The multiplicative inverse of the departure rate is also the average lifetime of a connection. “PU ON” indicates that a PU is in the active state. “PU OFF” indicates that a PU is in the idle state. Energy detection is easy to implement and commonly used as a spectrum-sensing scheme for SUs to sense the active status of PUs [19]. The PU active/non-active state is randomly determined in our simulation. The SUs is not always-on (i.e., not always transmitting messages), and the number of transmitting SUs is not constant. Therefore, the number of active PUs and SUs is not constant, which makes the token-based MAC protocol proposed in this paper scalable. Because the resources of PUs are assigned to and paid for by particular communities (companies, etc.), control by a PU is not impossible in real applications, with the consequence that **cognitive** **radio** is a key component of good performance by IoT-enabled MANETs. In all **cognitive**-**radio** applications, each node can use two transceivers for transmitting and receiving. In this paper, we need only one transceiver to support both **transmission** and receiving; we do not need two separate channels.

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In the proposed infrastructure based **Cognitive** **radio** **networks**, we assume that there are N PCs (primary channels) that are used by the PUs (Primary Users). In many scenarios, PCs are used by PUs while Nsc( Nsc=N-Npc) PCs Can be used by the SUs. The maximum number of channels the SUs can use is M .M is the number of antennas each UE is equipped. When 0<Nsc<M is satisfied, only Nsc channels can be used. If Nsc=0, then all the antennas should be turned off and the communication of the CCRN will be interrupted. Each **channel** will be divided into T sub channels and T1 of them should be used as signaling such as time synchronization and time slot allocation. The other T2 (T2=T-T1) time slots are only used for data transmissions. If the number of channels the CCRN used is Nw , then Nw antennas are working and ( M-Nw) antennas should be turned off. If there are total D types of multimedia traffic in CCRN, the least number of time slots should be guaranteed for each traffic in order to assure the QoS. For example, some multimedia traffic require at least sub slots for transmissions, then the system should allocate k(k≥cι ) time slots. On the other hand, any type of traffic requires at most cħ sub slots for data transmissions. For each node equipped with multi-antennas, number of k slots should be allocated to the user, and may be larger than ch. However, it must follow ch≤k<(ch+1)*Nw.This is because the working antennas will be in receiving/sending status at a same time due to the limitation of hardware.

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sensing on different channels was assumed to be per- formed by multiple spectrum sensors at each SU. In CRNs with parallel-sensing, there is no need to optimize spec- trum sensing sets for SUs. These works again considered the homogeneous network and each SU simply senses all channels. To the best of our knowledge, existing coopera- tive spectrum sensing schemes rely on a central controller to aggregate sensing results for white space detection (i.e., centralized design). In addition, homogeneous envi- ronments and parallel sensing have been commonly assumed in the literature, which would not be very realistic. In this work, we consider a general semi-distributed cooperative spectrum sensing (SDCSS) and access frame- work under the heterogeneous environment where statis- tics of wireless channels, and spectrum holes can be arbitrary and there is no central controller to collect sensing results and make spectrum status decisions. In addition, we assume that each SU is equipped with only one spectrum sensor so that SUs have to sense chan- nels sequentially. This assumption would be applied to real-world hardware-constrained **cognitive** radios. The considered SDCSS scheme requires SUs to perform sens- ing on their assigned sets of channels and then exchange spectrum sensing results with other SUs, which can be subject to errors. After the sensing and reporting phases, SUs employ the p-persistent CSMA MAC protocol [30] to access one available **channel**. In this MAC protocol, parameter p denotes the access probability to the chosen **channel** if the carrier sensing indicates an available chan- nel (i.e., no other SUs transmit on the chosen **channel**). It is of interest to determine the access parameter p that can mitigate the collisions and hence enhance the system throughput [30]. Also, optimization of the spectrum sens- ing set for each SU (i.e., the set of channels sensed by the SU) is very critical to achieve good system throughput. Moreover, analysis and optimization of the **joint** spectrum sensing and access design become much more challeng- ing in the heterogeneous environment, which, however, can significantly improve the system performance. Our current paper aims to resolve these challenges whose contributions can be summarized as follows:

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To reduce the cost and the node complexity, a self- scheduled **multichannel** **cognitive** **radio** MAC (SMC- MAC) is proposed in [10]. In SMC-MAC, each SU is able to sense only one licensed **channel** at a time, which makes the **radio** inexpensive and easy to implement. To reduce the sensing overhead, each SU in SMC-MAC is allowed to sense limited number of licensed channels, then the sens- ing results are shared with other SUs through the DCCC. Therefore, each SU gets more licensed **channel** informa- tion than that it has sensed. The sensing results are shared on the DCCC during the slotted sensing-sharing (SS) phase. The SS phase is composed of multiple SS slots, and each SS slot is associated with a licensed **channel**. In each MAC cycle, constrained by the limited sensing capability, each SU randomly picks up several SS slots to sense and share. As more than one SU may pick the same SS slot to share the sensing results, a sensing report collision prob- lem [8] may happen. After that, SUs contend to access the sensed idle channels in the fixed contention phase with a slotted ALOHA manner [21]. A contention prob- lem may happen here, as fixed contention phase may be too short for many SUs, consequently leading to a collision problem, or too long for few SUs, thus wasting the oppor- tunities for data **transmission**. To handle the contention problem of the fixed contention phase, along the line with SMC-MAC, [8] adds a backoff algorithm to the MAC pro- tocols. With the backoff algorithm, contention phase will be prolonged by adding a backoff window if any collision is detected during the initial contention window. Hence, it can increase the number of successful users substan- tially and enhance the throughput of DCRNs. However, comparing with the traditional fixed backoff algorithm, using the dynamic backoff algorithm may further improve the network performance [5]. Fu et al., Baher and Doré, and Wu and Xu [22–24] have proposed dynamic back- off algorithms for traditional wireless network to improve network throughput and to decrease access delay. But they are not suitable for DCRNs, as they do not consider the interference between PUs and SUs. Therefore, it is still an open problem on how to design a dynamic backoff algo- rithm and how to set the initial length of the contention phase for DCRNs. Besides, considering more parameters

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To evaluate the performance of the OSA strategy based on RPS in terms of average throughput, given by (2), we first investigate the probability of success for this strategy, i.e., the probability that a given CR finds a **channel** free from the primary user and other CR activity within the time slot. Under this assumption an exact closed-form expression for the probability of success can be derived for any N and 1 ≤ K ≤ N when M = 2. For M > 2, we are able to obtain an exact closed- form expression when K = 1 or K = N. For large M and large N, obtaining an exact closed-form expression for the probability of success is challenging due to the combinatorial explosion in the number of ways that M CRs can find channels free from PUs and other CRs. To simplify the analysis we provide an approximation for any N and 2 ≤ K ≤ N, for 2 < M ≤ K.

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